Ran an analysis for pharma and included all the output.

r-analysis/EffectsOfEnrollmentDelay.qmd

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+---
+title: "The Effects of Recruitment status on completion of clinical trials"
+author: "Will King"
+format: html
+editor: source
+---
+
+
+# Setup
+
+```{r}
+library(bayesplot)
+available_mcmc(pattern = "_nuts_")
+library(ggplot2)
+library(patchwork)
+library(tidyverse)
+library(rstan)
+library(tidyr)
+library(ghibli)
+library(xtable)
+#Resources: https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started
+
+#save unchanged models instead of recompiling
+rstan_options(auto_write = TRUE)
+#allow for multithreaded sampling
+options(mc.cores = parallel::detectCores())
+
+#test installation, shouldn't get any errors
+#example(stan_model, package = "rstan", run.dontrun = TRUE)
+```
+
+```{r}
+################ Pull data from database ######################
+library(RPostgreSQL)
+
+driver <- dbDriver("PostgreSQL")
+
+get_data <- function(driver) {
+
+con <- dbConnect(
+    driver,
+    user='root',
+    password='root',
+    dbname='aact_db',
+    host='will-office'
+    )
+on.exit(dbDisconnect(con))
+
+query <- dbSendQuery(
+    con,
+#    "select * from formatted_data_with_planned_enrollment;"
+"
+select 
+    fdqpe.nct_id
+    --,fdqpe.start_date
+    --,fdqpe.current_enrollment
+    --,fdqpe.enrollment_category
+    ,fdqpe.current_status 
+    ,fdqpe.earliest_date_observed 
+    ,fdqpe.elapsed_duration
+    ,fdqpe.n_brands as identical_brands
+    ,ntbtu.brand_name_count 
+    ,fdqpe.category_id
+    ,fdqpe.final_status
+    ,fdqpe.h_sdi_val
+    --,fdqpe.h_sdi_u95
+    --,fdqpe.h_sdi_l95
+    ,fdqpe.hm_sdi_val
+    --,fdqpe.hm_sdi_u95
+    --,fdqpe.hm_sdi_l95
+    ,fdqpe.m_sdi_val
+    --,fdqpe.m_sdi_u95
+    --,fdqpe.m_sdi_l95
+    ,fdqpe.lm_sdi_val
+    --,fdqpe.lm_sdi_u95
+    --,fdqpe.lm_sdi_l95
+    ,fdqpe.l_sdi_val
+    --,fdqpe.l_sdi_u95
+    --,fdqpe.l_sdi_l95
+from formatted_data_with_planned_enrollment fdqpe
+    join \"Formularies\".nct_to_brands_through_uspdc ntbtu
+        on fdqpe.nct_id = ntbtu.nct_id 
+order by fdqpe.nct_id, fdqpe.earliest_date_observed 
+;
+"
+    )
+df <- fetch(query, n = -1)
+df <- na.omit(df)
+
+query2 <-dbSendQuery(con,"select count(*) from \"DiseaseBurden\".icd10_categories ic where \"level\"=1;")
+n_categories <- fetch(query2, n = -1)
+
+return(list(data=df,ncat=n_categories))
+}
+
+
+get_counterfact_base <- function(driver) {
+
+con <- dbConnect(
+    driver,
+    user='root',
+    password='root',
+    dbname='aact_db',
+    host='will-office'
+    )
+on.exit(dbDisconnect(con))
+
+query <- dbSendQuery(
+    con,
+    "
+    with cte as (
+    --get last recruiting state
+    select fd.nct_id, max(fd.earliest_date_observed),min(fd2.earliest_date_observed) as tmstmp
+    from formatted_data fd 
+    	join formatted_data fd2 
+    	on fd.nct_id=fd2.nct_id and fd.earliest_date_observed < fd2.earliest_date_observed 
+    where fd.current_status = 'Recruiting'
+    	and fd2.current_status = 'Active, not recruiting'
+    group by fd.nct_id 
+    )
+    select 
+        fdqpe.nct_id
+        --,fdqpe.start_date
+        --,fdqpe.current_enrollment
+        --,fdqpe.enrollment_category
+        ,fdqpe.current_status 
+        ,fdqpe.earliest_date_observed 
+        ,fdqpe.elapsed_duration
+        ,fdqpe.n_brands as identical_brands
+        ,ntbtu.brand_name_count 
+        ,fdqpe.category_id
+        ,fdqpe.final_status
+        ,fdqpe.h_sdi_val
+        --,fdqpe.h_sdi_u95
+        --,fdqpe.h_sdi_l95
+        ,fdqpe.hm_sdi_val
+        --,fdqpe.hm_sdi_u95
+        --,fdqpe.hm_sdi_l95
+        ,fdqpe.m_sdi_val
+        --,fdqpe.m_sdi_u95
+        --,fdqpe.m_sdi_l95
+        ,fdqpe.lm_sdi_val
+        --,fdqpe.lm_sdi_u95
+        --,fdqpe.lm_sdi_l95
+        ,fdqpe.l_sdi_val
+        --,fdqpe.l_sdi_u95
+        --,fdqpe.l_sdi_l95
+    from formatted_data_with_planned_enrollment fdqpe
+        join \"Formularies\".nct_to_brands_through_uspdc ntbtu
+            on fdqpe.nct_id = ntbtu.nct_id 
+    	join cte 
+    		on fdqpe.nct_id = cte.nct_id 
+    			and fdqpe.earliest_date_observed = cte.tmstmp
+    order by fdqpe.nct_id, fdqpe.earliest_date_observed 
+    ;
+    "
+    )
+df <- fetch(query, n = -1)
+df <- na.omit(df)
+
+query2 <-dbSendQuery(con,"select count(*) from \"DiseaseBurden\".icd10_categories ic where \"level\"=1;")
+n_categories <- fetch(query2, n = -1)
+
+return(list(data=df,ncat=n_categories))
+}
+
+
+d <- get_data(driver)
+df <- d$data
+n_categories <- d$ncat
+
+cf <- get_counterfact_base(driver)
+df_counterfact_base <- cf$data
+
+
+
+################ Format Data ###########################
+
+data_formatter <- function(df) {
+categories <- df["category_id"]
+
+x <- df["elapsed_duration"]
+x["identical_brands"] <- asinh(df$identical_brands)
+x["brand_name_counts"] <- asinh(df$brand_name_count)
+x["h_sdi_val"] <- asinh(df$h_sdi_val)
+x["hm_sdi_val"] <- asinh(df$hm_sdi_val)
+x["m_sdi_val"] <- asinh(df$m_sdi_val)
+x["lm_sdi_val"] <- asinh(df$lm_sdi_val)
+x["l_sdi_val"] <- asinh(df$l_sdi_val)
+
+
+#Setup fixed effects
+x["status_NYR"] <- ifelse(df["current_status"]=="Not yet recruiting",1,0)
+x["status_EBI"] <- ifelse(df["current_status"]=="Enrolling by invitation",1,0)
+x["status_Rec"] <- ifelse(df["current_status"]=="Recruiting",1,0) 
+x["status_ANR"] <- ifelse(df["current_status"]=="Active, not recruiting",1,0)
+
+
+y <- ifelse(df["final_status"]=="Terminated",1,0)
+
+#get category list
+
+
+return(list(x=x,y=y))
+}
+
+train <- data_formatter(df)
+counterfact_base <- data_formatter(df_counterfact_base)
+
+categories <- df$category_id
+
+x <- train$x
+y <- train$y
+
+x_cf_base <- counterfact_base$x
+y_cf_base <- counterfact_base$y
+cf_categories <- df_counterfact_base$category_id
+```
+
+
+
+# Fit Model
+
+
+
+
+```{r}
+################################# FIT MODEL #########################################
+inherited_cols <- c(
+    "elapsed_duration"
+    #,"identical_brands"
+    #,"brand_name_counts"
+    ,"h_sdi_val"
+    ,"hm_sdi_val"
+    ,"m_sdi_val"
+    ,"lm_sdi_val"
+    ,"l_sdi_val"
+    ,"status_NYR"
+    ,"status_EBI"
+    ,"status_Rec"
+    ,"status_ANR"
+)
+```
+
+
+
+```{r}
+beta_list <- list(
+        groups = c(
+        `1`="Infections & Parasites",
+        `2`="Neoplasms",
+        `3`="Blood & Immune system",
+        `4`="Endocrine, Nutritional, and Metabolic",
+        `5`="Mental & Behavioral",
+        `6`="Nervous System",
+        `7`="Eye and Adnexa",
+        `8`="Ear and Mastoid",
+        `9`="Circulatory",
+        `10`="Respiratory",
+        `11`="Digestive",
+        `12`="Skin & Subcutaneaous tissue",
+        `13`="Musculoskeletal",
+        `14`="Genitourinary",
+        `15`="Pregancy, Childbirth, & Puerperium",
+        `16`="Perinatal Period",
+        `17`="Congential",
+        `18`="Symptoms, Signs etc.",
+        `19`="Injury etc.",
+        `20`="External Causes",
+        `21`="Contact with Healthcare",
+        `22`="Special Purposes"
+    ),
+    parameters = c(
+        `1`="Elapsed Duration",
+        # brands
+        `2`="asinh(Generic Brands)",
+        `3`="asinh(Competitors USPDC)",
+        # population
+        `4`="asinh(High SDI)",
+        `5`="asinh(High-Medium SDI)",
+        `6`="asinh(Medium SDI)",
+        `7`="asinh(Low-Medium SDI)",
+        `8`="asinh(Low SDI)",
+        #Status
+        `9`="status_NYR",
+        `10`="status_EBI",
+        `11`="status_Rec",
+        `12`="status_ANR"
+    )
+)
+
+get_parameters <- function(stem,class_list) {
+    #get categories and lengths
+    named <- names(class_list)
+    lengths <- sapply(named, (function (x) length(class_list[[x]])))
+    
+    #describe the grid needed
+    iter_list <- sapply(named, (function (x) 1:lengths[x]))
+    
+    #generate the list of parameters
+    pardf <- generate_parameter_df(stem, iter_list)
+    
+    #add columns with appropriate human-readable names
+    for (name in named) {
+        pardf[paste(name,"_hr",sep="")] <- as.factor(
+            sapply(pardf[name], (function (i) class_list[[name]][i]))
+        )
+    }
+     
+    return(pardf)   
+}
+
+generate_parameter_df <- function(stem, iter_list) {
+    grid <- expand.grid(iter_list)
+    grid["param_name"] <- grid %>% unite(x,colnames(grid),sep=",")
+    grid["param_name"] <- paste(stem,"[",grid$param_name,"]",sep="")
+    return(grid)
+}
+
+group_mcmc_areas <- function(
+        stem,# = "beta"
+        class_list,# = beta_list
+        stanfit,# = fit
+        group_id,# = 2
+        rename=TRUE,
+        filter=NULL
+        ) {
+    #get all parameter names
+    params <- get_parameters(stem,class_list)
+    
+    #filter down to parameters of interest
+    params <- filter(params,groups == group_id)
+    #Get dataframe with only the rows of interest
+    filtdata <- as.data.frame(stanfit)[params$param_name]
+    #rename columns
+    if (rename) dimnames(filtdata)[[2]] <- params$parameters_hr
+    #get group name for title
+    group_name <- class_list$groups[group_id]
+    #create area plot with appropriate title
+    p <- mcmc_areas(filtdata,prob = 0.8, prob_outer = 0.95) +
+        ggtitle(paste("Parameter distributions for ICD-10 class:",group_name)) +
+        geom_vline(xintercept=0,color="grey",alpha=0.75)
+    
+    d <- pivot_longer(filtdata, everything()) |> 
+        group_by(name) |> 
+        summarize(
+            mean=mean(value)
+            ,q025 = quantile(value,probs = 0.025)
+            ,q975 = quantile(value,probs = 0.975)
+            ,q05 = quantile(value,probs = 0.05)
+            ,q95 = quantile(value,probs = 0.95)
+            )
+    return(list(plot=p,quantiles=d,name=group_name))
+}
+
+parameter_mcmc_areas <- function(
+        stem,# = "beta"
+        class_list,# = beta_list
+        stanfit,# = fit
+        parameter_id,# = 2
+        rename=TRUE
+        ) {
+    #get all parameter names
+    params <- get_parameters(stem,class_list)
+    #filter down to parameters of interest
+    params <- filter(params,parameters == parameter_id)
+    #Get dataframe with only the rows of interest
+    filtdata <- as.data.frame(stanfit)[params$param_name]
+    #rename columns
+    if (rename) dimnames(filtdata)[[2]] <- params$groups_hr
+    #get group name for title
+    parameter_name <- class_list$parameters[parameter_id]
+    #create area plot with appropriate title
+    p <- mcmc_areas(filtdata,prob = 0.8, prob_outer = 0.95) +
+        ggtitle(parameter_name,"Parameter Distribution")
+    
+    d <- pivot_longer(filtdata, everything()) |> 
+        group_by(name) |> 
+        summarize(
+            mean=mean(value)
+            ,q025 = quantile(value,probs = 0.025)
+            ,q975 = quantile(value,probs = 0.975)
+            ,q05 = quantile(value,probs = 0.05)
+            ,q95 = quantile(value,probs = 0.95)
+            )
+    return(list(plot=p,quantiles=d,name=parameter_name))
+}
+
+
+```
+
+Plan: select all snapshots that are the first to have closed enrollment (Rec -> ANR)
+```{r}
+#delay intervention
+intervention_enrollment <- x_cf_base[c(inherited_cols,"brand_name_counts", "identical_brands")]
+intervention_enrollment["status_ANR"] <- 0
+intervention_enrollment["status_Rec"] <- 1
+```
+
+```{r}
+counterfact_delay <- list(
+    D = ncol(x),#
+    N = nrow(x),
+    L = n_categories$count,
+    y = as.vector(y),
+    ll = as.vector(categories),
+    x = as.matrix(x),
+    mu_mean = 0,
+    mu_stdev = 0.05,
+    sigma_shape = 4,
+    sigma_rate = 20,
+    Nx = nrow(x_cf_base),
+    llx = as.vector(cf_categories),
+    counterfact_x_tilde = as.matrix(intervention_enrollment),
+    counterfact_x = as.matrix(x_cf_base)
+)
+```
+
+```{r}
+fit <- stan(
+    file='Hierarchal_Logistic.stan', 
+    data = counterfact_delay,
+    chains = 4,
+    iter = 5000,
+    seed = 11021585
+    )
+```
+
+
+
+
+
+
+
+
+## Explore data
+
+
+```{r}
+#get number of trials and snapshots in each category
+group_trials_by_category <- as.data.frame(aggregate(category_id ~ nct_id, df, max))
+group_trials_by_category <- as.data.frame(group_trials_by_category)
+
+category_count <- group_trials_by_category |> group_by(category_id) |> count()
+
+```
+
+
+
+
+
+## Fit Results
+
+
+```{r}
+################################# ANALYZE #####################################
+print(fit)
+```
+
+
+
+# Counterfactuals
+
+```{r}
+generated_ib <- gqs(
+    fit@stanmodel,
+    data=counterfact_delay,
+    draws=as.matrix(fit),
+    seed=11021585
+    )
+```
+
+```{r}
+df_ib_p <- data.frame(
+    p_prior=as.vector(extract(generated_ib, pars="p_prior")$p_prior)
+    ,p_predicted = as.vector(extract(generated_ib, pars="p_predicted")$p_predicted)
+)
+
+df_ib_prior <- data.frame(
+    mu_prior = as.vector(extract(generated_ib, pars="mu_prior")$mu_prior)
+    ,sigma_prior = as.vector(extract(generated_ib, pars="sigma_prior")$sigma_prior)
+)
+
+#p_prior
+ggplot(df_ib_p, aes(x=p_prior)) +
+    geom_density() + 
+    labs(
+        title="Implied Prior Distribution P"
+        ,subtitle=""
+        ,x="Probability Domain 'p'"
+        ,y="Probability Density"
+    )
+ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/prior_p.png")
+
+#p_posterior
+ggplot(df_ib_p, aes(x=p_predicted)) +
+    geom_density() + 
+    labs(
+        title="Implied Posterior Distribution P"
+        ,subtitle=""
+        ,x="Probability Domain 'p'"
+        ,y="Probability Density"
+    )
+ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/posterior_p.png")
+
+#mu_prior
+ggplot(df_ib_prior) +
+    geom_density(aes(x=mu_prior)) + 
+    labs(
+        title="Prior - Mu"
+        ,subtitle="same prior for all Mu values"
+        ,x="Mu"
+        ,y="Probability"
+    )
+ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/prior_mu.png")
+
+#sigma_posterior
+ggplot(df_ib_prior) +
+    geom_density(aes(x=sigma_prior)) + 
+    labs(
+        title="Prior - Sigma"
+        ,subtitle="same prior for all Sigma values"
+        ,x="Sigma"
+        ,y="Probability"
+    )
+ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/prior_sigma.png")
+```
+
+
+
+```{r}
+check_hmc_diagnostics(fit)
+```
+
+
+
+
+
+### Intervention: Delay close of enrollment
+
+```{r}
+counterfact_predicted_ib <- data.frame(
+    p_predicted_default = as.vector(extract(generated_ib, pars="p_predicted_default")$p_predicted_default)
+    ,p_predicted_intervention = as.vector(extract(generated_ib, pars="p_predicted_intervention")$p_predicted_intervention)
+    ,predicted_difference = as.vector(extract(generated_ib, pars="predicted_difference")$predicted_difference)
+)
+
+
+ggplot(counterfact_predicted_ib, aes(x=p_predicted_default)) +
+    geom_density() + 
+    labs(
+        title="Predicted Distribution of 'p'"
+        ,subtitle="Intervention: None"
+        ,x="Probability Domain 'p'"
+        ,y="Probability Density"
+    )
+ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/default_p_generic_intervention_base.png")
+
+ggplot(counterfact_predicted_ib, aes(x=p_predicted_intervention)) +
+    geom_density() + 
+    labs(
+        title="Predicted Distribution of 'p'"
+        ,subtitle="Intervention: Delay close of enrollment"
+        ,x="Probability Domain 'p'"
+        ,y="Probability Density"
+    )
+ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/default_p_generic_intervention_interv.png")
+
+ggplot(counterfact_predicted_ib, aes(x=predicted_difference)) +
+    geom_density() + 
+    labs(
+        title="Predicted Distribution of differences 'p'"
+        ,subtitle="Intervention: Delay close of enrollment"
+        ,x="Difference in 'p' under treatment"
+        ,y="Probability Density"
+    )
+ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/default_p_generic_intervention_distdiff.png")
+```
+
+
+```{r}
+
+
+pddf_ib <- data.frame(extract(generated_ib, pars="predicted_difference")$predicted_difference) |>
+    pivot_longer(X1:X169)
+
+#TODO: Fix Category names
+pddf_ib["entry_idx"] <- as.numeric(gsub("\\D","",pddf_ib$name))
+pddf_ib["category"] <-  sapply(pddf_ib$entry_idx, function(i) df$category_id[i])
+pddf_ib["category_name"] <- sapply(pddf_ib$category, function(i) beta_list$groups[i])
+
+
+ggplot(pddf_ib, aes(x=value,)) +
+    geom_density(bins=100) +
+    labs(
+        title = "Distribution of predicted differences"
+        ,subtitle = "Intervention: Delay close of enrollment"
+        ,x = "Difference in probability due to intervention"
+        ,y = "Probability Density"
+    ) + 
+    geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") 
+ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/p_generic_intervention_distdiff_styled.png")
+
+ggplot(pddf_ib, aes(x=value,)) +
+    geom_density(bins=100) +
+    facet_wrap(
+        ~factor(
+            category_name, 
+            levels=beta_list$groups
+            )
+        , labeller = label_wrap_gen(multi_line = TRUE)
+        , ncol=4) +
+    labs(
+        title = "Distribution of predicted differences | By Group"
+        ,subtitle = "Intervention: Delay close of enrollment"
+        ,x = "Difference in probability due to intervention"
+        ,y = "Probability Density"
+    ) + 
+    geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") +
+    theme(strip.text.x = element_text(size = 8))
+ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/p_generic_intervention_distdiff_by_group.png")
+
+ggplot(pddf_ib, aes(x=value,)) +
+    geom_histogram(bins=100) +
+    facet_wrap(
+        ~factor(
+            category_name, 
+            levels=beta_list$groups
+            )
+        , labeller = label_wrap_gen(multi_line = TRUE)
+        , ncol=5) +
+    labs(
+        title = "Histogram of predicted differences | By Group"
+        ,subtitle = "Intervention: Delay close of enrollment"
+        ,x = "Difference in probability due to intervention"
+        ,y = "Predicted counts"
+    ) + 
+    #xlim(-0.25,0.1) +
+    geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") +
+    theme(strip.text.x = element_text(size = 8))
+ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/p_generic_intervention_histdiff_by_group.png")
+```
+
+Get the probability of increase over probability of a decrease
+
+```{r}
+mean(counterfact_predicted_ib$predicted_difference)
+```
+Thus adding a Delay close of enrollment increases the probability of termination by 16.72% on average for
+the snapshots investigated.
+
+
+
+```{r}
+n = length(counterfact_predicted_ib$p_predicted_intervention)
+mean(rbinom(n,1,as.vector(counterfact_predicted_ib$p_predicted_intervention)))
+mean(rbinom(n,1,as.vector(counterfact_predicted_ib$p_predicted_default)))
+```
+
+
+
+
+# Diagnostics
+
+```{r}
+#| eval: false
+#trace plots
+plot(fit, pars=c("mu"), plotfun="trace")
+
+
+for (i in 1:4) {
+    print(
+        mcmc_rank_overlay(
+        fit, 
+        pars=c(
+            paste0("mu[",4*i-3,"]"),
+            paste0("mu[",4*i-2,"]"),
+            paste0("mu[",4*i-1,"]"),
+            paste0("mu[",4*i,"]")
+            ), 
+        n_bins=100
+        )+  legend_move("top") +
+             scale_colour_ghibli_d("KikiMedium")
+    )
+}
+```
+
+```{r}
+#| eval: false
+plot(fit, pars=c("sigma"), plotfun="trace")
+
+for (i in 1:4) {
+    print(
+        mcmc_rank_overlay(
+        fit, 
+        pars=c(
+            paste0("sigma[",4*i-3,"]"),
+            paste0("sigma[",4*i-2,"]"),
+            paste0("sigma[",4*i-1,"]"),
+            paste0("sigma[",4*i,"]")
+            ), 
+        n_bins=100
+        )+  legend_move("top") +
+             scale_colour_ghibli_d("KikiMedium")
+    )
+}
+```
+
+```{r}
+#| eval: false
+#other diagnostics
+logpost <- log_posterior(fit)
+nuts_prmts <- nuts_params(fit)
+posterior <- as.array(fit)
+
+```
+
+```{r}
+#| eval: false
+color_scheme_set("darkgray")
+div_style <- parcoord_style_np(div_color = "green", div_size = 0.05, div_alpha = 0.4)
+mcmc_parcoord(posterior, regex_pars = "mu", np=nuts_prmts, np_style = div_style, alpha = 0.05)
+```
+
+```{r}
+#| eval: false
+for (i in 1:4) {
+    mus = sapply(3:0, function(j) paste0("mu[",4*i-j ,"]"))
+    print(
+        mcmc_pairs(
+            posterior,
+            np = nuts_prmts,
+            pars=c(
+                mus,
+                "lp__"
+            ),
+            off_diag_args = list(size = 0.75)
+        )
+    )
+}
+
+
+
+```
+
+```{r}
+#| eval: false
+mcmc_parcoord(posterior,regex_pars = "sigma", np=nuts_prmts, alpha=0.05)
+```
+
+```{r}
+#| eval: false
+
+for (i in 1:4) {
+    params = sapply(3:0, function(j) paste0("sigma[",4*i-j ,"]"))
+    print(
+        mcmc_pairs(
+            posterior,
+            np = nuts_prmts,
+            pars=c(
+                params,
+                "lp__"
+            ),
+            off_diag_args = list(size = 0.75)
+        )
+    )
+}
+```
+
+
+```{r}
+#| eval: false
+for (k in 1:22) {
+for (i in 1:4) {
+    params = sapply(3:0, function(j) paste0("beta[",k,",",4*i-j ,"]"))
+    print(
+        mcmc_pairs(
+            posterior,
+            np = nuts_prmts,
+            pars=c(
+                params,
+                "lp__"
+            ),
+            off_diag_args = list(size = 0.75)
+        )
+    )
+}}
+```
+
+
+
+
+
+
+# TODO
+- [ ] Double check data flow. (Write summary of this in human readable form)
+    - Is it the data we want from the database 
+        - Training
+        - Counterfactual Evaluation
+            - choose a single snapshot per trial.
+    - Is the model in STAN well specified.
+- [ ] work on LOO validation of model

r-analysis/EffectsOfMarketConditions.qmd

@@ -17,6 +17,7 @@ library(tidyverse)
 library(rstan)
 library(tidyr)
 library(ghibli)
+library(xtable)
 #Resources: https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started
 
 #save unchanged models instead of recompiling
@@ -242,10 +243,12 @@ group_mcmc_areas <- function(
         class_list,# = beta_list
         stanfit,# = fit
         group_id,# = 2
-        rename=TRUE
+        rename=TRUE,
+        filter=NULL
         ) {
     #get all parameter names
     params <- get_parameters(stem,class_list)
+    
     #filter down to parameters of interest
     params <- filter(params,groups == group_id)
     #Get dataframe with only the rows of interest
@@ -255,8 +258,20 @@ group_mcmc_areas <- function(
     #get group name for title
     group_name <- class_list$groups[group_id]
     #create area plot with appropriate title
-    mcmc_areas(filtdata,prob = 0.8, prob_outer = 0.95) +
-        ggtitle(paste("Parameter distributions for ICD-10 class:",group_name))
+    p <- mcmc_areas(filtdata,prob = 0.8, prob_outer = 0.95) +
+        ggtitle(paste("Parameter distributions for ICD-10 class:",group_name)) +
+        geom_vline(xintercept=0,color="grey",alpha=0.75)
+    
+    d <- pivot_longer(filtdata, everything()) |> 
+        group_by(name) |> 
+        summarize(
+            mean=mean(value)
+            ,q025 = quantile(value,probs = 0.025)
+            ,q975 = quantile(value,probs = 0.975)
+            ,q05 = quantile(value,probs = 0.05)
+            ,q95 = quantile(value,probs = 0.95)
+            )
+    return(list(plot=p,quantiles=d,name=group_name))
 }
 
 parameter_mcmc_areas <- function(
@@ -277,8 +292,19 @@ parameter_mcmc_areas <- function(
     #get group name for title
     parameter_name <- class_list$parameters[parameter_id]
     #create area plot with appropriate title
-    mcmc_areas(filtdata,prob = 0.8, prob_outer = 0.95) +
+    p <- mcmc_areas(filtdata,prob = 0.8, prob_outer = 0.95) +
         ggtitle(parameter_name,"Parameter Distribution")
+    
+    d <- pivot_longer(filtdata, everything()) |> 
+        group_by(name) |> 
+        summarize(
+            mean=mean(value)
+            ,q025 = quantile(value,probs = 0.025)
+            ,q975 = quantile(value,probs = 0.975)
+            ,q05 = quantile(value,probs = 0.05)
+            ,q95 = quantile(value,probs = 0.95)
+            )
+    return(list(plot=p,quantiles=d,name=parameter_name))
 }
 
 
@@ -320,6 +346,229 @@ fit <- stan(
     )
 ```
 
+
+
+
+
+
+
+
+## Explore data
+
+```{r}
+################################# DATA EXPLORATION ############################
+driver <- dbDriver("PostgreSQL")
+
+con <- dbConnect(
+    driver,
+    user='root',
+    password='root',
+    dbname='aact_db',
+    host='will-office'
+    )
+#Plot histogram of count of snapshots
+df3 <- dbGetQuery(
+    con,
+    "select nct_id,final_status,count(*) from formatted_data_with_planned_enrollment fdwpe 
+    group by nct_id,final_status ;"
+    )
+#df3 <- fetch(query3, n = -1)
+
+ggplot(data=df3, aes(x=count, fill=final_status)) + 
+    geom_histogram(binwidth=1) +
+    ggtitle("Histogram of snapshots per trial (matched trials)") +
+    xlab("Snapshots per trial")
+ggsave("./Images/HistSnapshots.png")
+
+#Plot duration for terminated vs completed
+df4 <- dbGetQuery(
+    con,
+    "
+    select 
+        nct_id, 
+        start_date , 
+        primary_completion_date, 
+        overall_status ,
+        primary_completion_date - start_date as duration
+    from ctgov.studies s 
+    where nct_id in (select distinct nct_id from http.download_status ds)
+    ;"
+    )
+#df4 <- fetch(query4, n = -1)
+
+ggplot(data=df4, aes(x=duration,fill=overall_status)) +
+    geom_histogram()+
+    ggtitle("Histogram of trial durations") +
+    xlab("duration")+
+    facet_wrap(~overall_status)
+ggsave("./Images/HistTrialDurations_Faceted.png")
+
+df5 <- dbGetQuery(
+    con,
+    "
+    with cte1 as (
+    select 
+        nct_id, 
+        start_date , 
+        primary_completion_date, 
+        overall_status ,
+        primary_completion_date - start_date as duration
+    from ctgov.studies s 
+    where nct_id in (select distinct nct_id from http.download_status ds)
+    ), cte2 as (
+    select nct_id,count(*) as snapshot_count from formatted_data_with_planned_enrollment fdwpe
+    group by nct_id
+    )
+    select a.nct_id, a.overall_status, a.duration,b.snapshot_count
+    from cte1 as a
+        join cte2 as b
+            on a.nct_id=b.nct_id
+    ;"
+    )
+df5$overall_status <- as.factor(df5$overall_status)
+
+ggplot(data=df5, aes(x=duration,y=snapshot_count,color=overall_status)) +
+    geom_jitter() +
+    ggtitle("Comparison of duration, status, and snapshot_count") +
+    xlab("duration") +
+    ylab("snapshot count") 
+ggsave("./Images/SnapshotsVsDurationVsTermination.png")
+
+dbDisconnect(con)
+
+#get number of trials and snapshots in each category
+group_trials_by_category <- as.data.frame(aggregate(category_id ~ nct_id, df, max))
+group_trials_by_category <- as.data.frame(group_trials_by_category)
+
+ggplot(data = group_trials_by_category, aes(x=category_id)) +
+    geom_bar(binwidth=1,color="black",fill="seagreen") +
+    scale_x_continuous(breaks=scales::pretty_breaks(n=22)) + 
+    labs(
+        title="bar chart of trial categories"
+        ,x="Category ID"
+        ,y="Count"
+    )
+ggsave("./Images/CategoryCounts.png")
+    
+
+
+summary(df5)
+```
+
+
+
+
+
+
+```{r}
+category_count <- group_trials_by_category |> group_by(category_id) |> count()
+
+```
+
+
+
+
+
+## Fit Results
+
+
+```{r}
+################################# ANALYZE #####################################
+print(fit)
+```
+
+
+
+
+
+
+### Investigating parameter distributions
+
+```{r}
+#g1 <- group_mcmc_areas("beta",beta_list,fit,1)
+
+
+gx <- c()
+
+#grab parameters for every category with more than 8 observations
+for (i in category_count$category_id[category_count$n >= 8]) {
+    print(i)
+    
+    #Print parameter distributions
+    gi <- group_mcmc_areas("beta",beta_list,fit,i) #add way to filter groups
+    ggsave(
+        paste0("./Images/DirectEffects/Parameters/group_",i,"_",gi$name,".png")
+        ,plot=gi$plot
+        )
+    gx <- c(gx,gi)
+
+    #Get Quantiles and means for parameters
+    table <- xtable(gi$quantiles,
+      floating=FALSE
+      ,latex.environments = NULL
+      ,booktabs = TRUE
+      ,zap=getOption("digits")
+      )
+    write_lines(table,paste0("./latex_output/DirectEffects/group_",gi$name,".tex"))
+}
+```
+
+
+
+```{r}
+px <- c()
+
+
+for (i in c(1,2,3,9,10,11,12)) {
+    
+    #Print parameter distributions
+    pi <- parameter_mcmc_areas("beta",beta_list,fit,i) #add way to filter groups
+    ggsave(
+        paste0("./Images/DirectEffects/Parameters/parameters_",i,"_",pi$name,".png")
+        ,plot=pi$plot
+        )
+    px <- c(px,pi)
+
+    #Get Quantiles and means for parameters
+    table <- xtable(pi$quantiles,
+      floating=FALSE
+      ,latex.environments = NULL
+      ,booktabs = TRUE
+      ,zap=getOption("digits")
+      )
+    write_lines(table,paste0("./latex_output/DirectEffects/parameters_",i,"_",pi$name,".tex"))
+    
+}
+```
+
+Note these have 95% outer CI and 80% inner (shaded)
+
+
+    1) "Elapsed Duration",
+    2) "asinh(Generic Brands)",
+    3) "asinh(Competitors USPDC)",
+    4) "asinh(High SDI)",
+    5) "asinh(High-Medium SDI)",
+    6) "asinh(Medium SDI)",
+    7) "asinh(Low-Medium SDI)",
+    8) "asinh(Low SDI)",
+    9) "status_NYR",
+    10) "status_EBI",
+    11) "status_Rec",
+    12) "status_ANR",
+
+
+
+
+```{r}
+print(px[4]$plot + px[7]$plot)
+ggsave("./Images/DirectEffects/Parameters/2+3_generic_and_uspdc.png")
+```
+
+
+
+# Counterfactuals
+
 ```{r}
 generated_ib <- gqs(
     fit@stanmodel,
@@ -394,7 +643,10 @@ check_hmc_diagnostics(fit)
 
 
 
-### Intervention: Adding a single competitor
+
+### Intervention: Alternatives
+
+#### Generic Alternative
 
 ```{r}
 counterfact_predicted_ib <- data.frame(
@@ -499,9 +751,24 @@ ggplot(pddf_ib, aes(x=value,)) +
 ggsave("./Images/DirectEffects/p_generic_intervention_histdiff_by_group.png")
 ```
 
+Get the probability of increase over probability of a decrease
+
+```{r}
+mean(counterfact_predicted_ib$predicted_difference)
+```
+Thus adding a single generic competitor increases the probability of termination by 16.55% on average for
+the snapshots investigated.
 
 
-#### USP DC
+
+```{r}
+n = length(counterfact_predicted_ib$p_predicted_intervention)
+mean(rbinom(n,1,as.vector(counterfact_predicted_ib$p_predicted_intervention)))
+mean(rbinom(n,1,as.vector(counterfact_predicted_ib$p_predicted_default)))
+```
+
+
+#### USP DC Alternative
 
 ```{r}
 #formulary intervention
@@ -645,110 +912,60 @@ ggsave("./Images/DirectEffects/p_uspdc_intervention_histdiff_by_group.png")
 TODO: add density plot of (x,y,z) (date,value,counts)
     - with and without faceting
 
-## Explore data
 
 ```{r}
-################################# DATA EXPLORATION ############################
-driver <- dbDriver("PostgreSQL")
-
-con <- dbConnect(
-    driver,
-    user='root',
-    password='root',
-    dbname='aact_db',
-    host='will-office'
-    )
-#Plot histogram of count of snapshots
-df3 <- dbGetQuery(
-    con,
-    "select nct_id,final_status,count(*) from formatted_data_with_planned_enrollment fdwpe 
-    group by nct_id,final_status ;"
-    )
-#df3 <- fetch(query3, n = -1)
+mean(counterfact_predicted_bnc$predicted_difference)
+```
+Addin a single USP DC competitor increases/reduces the probability of completion by 16.47% on average
+for the snapshots of trials that we have.
 
-ggplot(data=df3, aes(x=count, fill=final_status)) + 
-    geom_histogram(binwidth=1) +
-    ggtitle("Histogram of snapshots per trial (matched trials)") +
-    xlab("Snapshots per trial")
-ggsave("./Images/HistSnapshots.png")
 
-#Plot duration for terminated vs completed
-df4 <- dbGetQuery(
-    con,
-    "
-    select 
-        nct_id, 
-        start_date , 
-        primary_completion_date, 
-        overall_status ,
-        primary_completion_date - start_date as duration
-    from ctgov.studies s 
-    where nct_id in (select distinct nct_id from http.download_status ds)
-    ;"
-    )
-#df4 <- fetch(query4, n = -1)
 
-ggplot(data=df4, aes(x=duration,fill=overall_status)) +
-    geom_histogram()+
-    ggtitle("Histogram of trial durations") +
-    xlab("duration")+
-    facet_wrap(~overall_status)
-ggsave("./Images/HistTrialDurations_Faceted.png")
 
-df5 <- dbGetQuery(
-    con,
-    "
-    with cte1 as (
-    select 
-        nct_id, 
-        start_date , 
-        primary_completion_date, 
-        overall_status ,
-        primary_completion_date - start_date as duration
-    from ctgov.studies s 
-    where nct_id in (select distinct nct_id from http.download_status ds)
-    ), cte2 as (
-    select nct_id,count(*) as snapshot_count from formatted_data_with_planned_enrollment fdwpe
-    group by nct_id
-    )
-    select a.nct_id, a.overall_status, a.duration,b.snapshot_count
-    from cte1 as a
-        join cte2 as b
-            on a.nct_id=b.nct_id
-    ;"
-    )
-df5$overall_status <- as.factor(df5$overall_status)
-
-ggplot(data=df5, aes(x=duration,y=snapshot_count,color=overall_status)) +
-    geom_jitter() +
-    ggtitle("Comparison of duration, status, and snapshot_count") +
-    xlab("duration") +
-    ylab("snapshot count") 
-ggsave("./Images/SnapshotsVsDurationVsTermination.png")
+### Intervention: Marginal increase in time to finish enrollment
 
-dbDisconnect(con)
+```{r}
+#| eval: false
 
-#get number of trials and snapshots in each category
-group_trials_by_category <- as.data.frame(aggregate(category_id ~ nct_id, df, max))
-group_trials_by_category <- as.data.frame(group_trials_by_category)
+pddf <- data.frame(extract(generated, pars="predicted_difference")$predicted_difference)  |> pivot_longer(X1:X189)
+pddf["entry_idx"] <- as.numeric(gsub("\\D","",pddf$name))
 
-ggplot(data = group_trials_by_category, aes(x=category_id)) +
-    geom_bar(binwidth=1,color="black",fill="seagreen") +
-    scale_x_continuous(breaks=scales::pretty_breaks(n=22)) + 
+pddf["category"] <-  sapply(pddf$entry_idx, function(i) counterfact_categories[i])
+pddf["category_name"] <- sapply(pddf$category, function(i) beta_list$groups[i])
+ 
+ggplot(pddf, aes(x=value,)) +
+    geom_histogram(bins=100) +
     labs(
-        title="bar chart of trial categories"
-        ,x="Category ID"
-        ,y="Count"
-    )
-ggsave("./Images/CategoryCounts.png")
-    
+        title = "Distribution of predicted differences"
+        ,x = "Difference in probability due to intervention"
+        ,y = "Predicted counts"
+    ) + 
+    xlim(-0.3,0.1) +
+    geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") 
 
+ggplot(pddf, aes(x=value,)) +
+    geom_histogram(bins=100) +
+    facet_wrap(
+        ~factor(
+            category_name, 
+            levels=beta_list$groups
+            )
+        , labeller = label_wrap_gen(multi_line = TRUE)
+        , ncol=5) +
+    labs(
+        title = "Distribution of predicted differences",
+        subtitle = "By group"
+        ,x = "Difference in probability due to intervention"
+        ,y = "Predicted counts"
+    ) + 
+    xlim(-0.25,0.1) +
+    geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") +
+    theme(strip.text.x = element_text(size = 8))
 
-summary(df5)
 ```
 
 
-
+Recall that we had really tight zero priors.
 
 
 
@@ -880,143 +1097,15 @@ for (i in 1:4) {
 ```
 
 
-# Results
-
-
-```{r}
-################################# ANALYZE #####################################
-print(fit)
-```
-
-## Result Plots
-
-
-Note the regular large difference in variance. 
-I would guess those are the beta[1:22,2] values.
-I wonder if a lot of the variance is due to the 2 values that are sitting out.
-
-
-
-
-### Investigating parameter distributions
-
-```{r}
-#g1 <- group_mcmc_areas("beta",beta_list,fit,2)
-#g2 <- group_mcmc_areas("beta",beta_list,fit,2)
-#g3 <- group_mcmc_areas("beta",beta_list,fit,2)
-#g4 <- group_mcmc_areas("beta",beta_list,fit,2)
-#g5 <- group_mcmc_areas("beta",beta_list,fit,2)
-#g6 <- group_mcmc_areas("beta",beta_list,fit,2)
-#g7 <- group_mcmc_areas("beta",beta_list,fit,2)
-#g8 <- group_mcmc_areas("beta",beta_list,fit,2)
-#g9 <- group_mcmc_areas("beta",beta_list,fit,2)
-#g10 <- group_mcmc_areas("beta",beta_list,fit,2)
-#g11 <- group_mcmc_areas("beta",beta_list,fit,2)
-#g12 <- group_mcmc_areas("beta",beta_list,fit,2)
-#g13 <- group_mcmc_areas("beta",beta_list,fit,2)
-#g14 <- group_mcmc_areas("beta",beta_list,fit,2)
-#g15 <- group_mcmc_areas("beta",beta_list,fit,2)
-#g16 <- group_mcmc_areas("beta",beta_list,fit,2)
-#g17 <- group_mcmc_areas("beta",beta_list,fit,2)
-#g18 <- group_mcmc_areas("beta",beta_list,fit,2)
-#g19 <- group_mcmc_areas("beta",beta_list,fit,2)
-#g20 <- group_mcmc_areas("beta",beta_list,fit,2)
-#g21 <- group_mcmc_areas("beta",beta_list,fit,2)
-#g22 <- group_mcmc_areas("beta",beta_list,fit,2)
-
-
-p1 <- parameter_mcmc_areas("beta",beta_list,fit,1)
-ggsave("./Images/DirectEffects/Parameters/01_elapsed_duration.png")
-p2 <- parameter_mcmc_areas("beta",beta_list,fit,2)
-ggsave("./Images/DirectEffects/Parameters/02_generic.png")
-p3 <- parameter_mcmc_areas("beta",beta_list,fit,3)
-ggsave("./Images/DirectEffects/Parameters/03_uspdc.png")
-#p4 <- parameter_mcmc_areas("beta",beta_list,fit,4)
-#p5 <- parameter_mcmc_areas("beta",beta_list,fit,5)
-#p6 <- parameter_mcmc_areas("beta",beta_list,fit,6)
-#p7 <- parameter_mcmc_areas("beta",beta_list,fit,7)
-#p8 <- parameter_mcmc_areas("beta",beta_list,fit,8)
-p9 <- parameter_mcmc_areas("beta",beta_list,fit,9)
-ggsave("./Images/DirectEffects/Parameters/09_NYR.png")
-p10 <- parameter_mcmc_areas("beta",beta_list,fit,10)
-ggsave("./Images/DirectEffects/Parameters/10_EBI.png")
-p11 <- parameter_mcmc_areas("beta",beta_list,fit,11)
-ggsave("./Images/DirectEffects/Parameters/11_Rec.png")
-p12 <- parameter_mcmc_areas("beta",beta_list,fit,12)
-ggsave("./Images/DirectEffects/Parameters/12_ANR.png")
-```
-
-Note these have 95% outer CI and 80% inner (shaded)
-
-
-    1) "Elapsed Duration",
-    2) "asinh(Generic Brands)",
-    3) "asinh(Competitors USPDC)",
-    4) "asinh(High SDI)",
-    5) "asinh(High-Medium SDI)",
-    6) "asinh(Medium SDI)",
-    7) "asinh(Low-Medium SDI)",
-    8) "asinh(Low SDI)",
-    9) "status_NYR",
-    10) "status_EBI",
-    11) "status_Rec",
-    12) "status_ANR",
-
-
-
-
-```{r}
-p2 + p3
-ggsave("./Images/DirectEffects/Parameters/2+3_generic_and_uspdc.png")
-```
-
-
-
-
-
-### Intervention: Marginal increase in time to finish enrollment
-
-```{r}
-#| eval: false
 
-pddf <- data.frame(extract(generated, pars="predicted_difference")$predicted_difference)  |> pivot_longer(X1:X189)
-pddf["entry_idx"] <- as.numeric(gsub("\\D","",pddf$name))
 
-pddf["category"] <-  sapply(pddf$entry_idx, function(i) counterfact_categories[i])
-pddf["category_name"] <- sapply(pddf$category, function(i) beta_list$groups[i])
- 
-ggplot(pddf, aes(x=value,)) +
-    geom_histogram(bins=100) +
-    labs(
-        title = "Distribution of predicted differences"
-        ,x = "Difference in probability due to intervention"
-        ,y = "Predicted counts"
-    ) + 
-    xlim(-0.3,0.1) +
-    geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") 
-
-ggplot(pddf, aes(x=value,)) +
-    geom_histogram(bins=100) +
-    facet_wrap(
-        ~factor(
-            category_name, 
-            levels=beta_list$groups
-            )
-        , labeller = label_wrap_gen(multi_line = TRUE)
-        , ncol=5) +
-    labs(
-        title = "Distribution of predicted differences",
-        subtitle = "By group"
-        ,x = "Difference in probability due to intervention"
-        ,y = "Predicted counts"
-    ) + 
-    xlim(-0.25,0.1) +
-    geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") +
-    theme(strip.text.x = element_text(size = 8))
-
-```
-
-
-Recall that we had really tight zero priors.
 
 
+# TODO
+- [ ] Double check data flow. (Write summary of this in human readable form)
+    - Is it the data we want from the database 
+        - Training
+        - Counterfactual Evaluation
+            - choose a single snapshot per trial.
+    - Is the model in STAN well specified.
+- [ ] work on LOO validation of model

r-analysis/EffectsOfMarketConditions_no_enrollment_status.qmd

@@ -102,9 +102,8 @@ n_categories <- d$ncat
 ################ Format Data ###########################
 
 data_formatter <- function(df) {
-categories <- df["category_id"]
 
-x <- df["elapsed_duration"]
+x <- df["category_id"]
 x["identical_brands"] <- asinh(df$identical_brands)
 x["brand_name_counts"] <- asinh(df$brand_name_count)
 x["h_sdi_val"] <- asinh(df$h_sdi_val)
@@ -114,9 +113,6 @@ x["lm_sdi_val"] <- asinh(df$lm_sdi_val)
 x["l_sdi_val"] <- asinh(df$l_sdi_val)
 
 
-#interaction terms for competitors
-x["ib*elapsed"] <- x["elapsed_duration"]*x["identical_brands"]
-x["bnc*elapsed"] <- x["elapsed_duration"] * x["brand_name_counts"]
 
 y <- ifelse(df["final_status"]=="Terminated",1,0)
 
@@ -131,6 +127,7 @@ categories <- df$category_id
 
 x <- train$x
 y <- train$y
+x$category_id <- NULL
 ```
 
 
@@ -138,19 +135,13 @@ y <- train$y
 ### Intervention: Adding a single competitor
 ```{r}
 inherited_cols <- c(
-    "elapsed_duration"
-    ,"identical_brands"
+    "identical_brands"
     ,"brand_name_counts"
     ,"h_sdi_val"
     ,"hm_sdi_val"
     ,"m_sdi_val"
     ,"lm_sdi_val"
     ,"l_sdi_val"
-    ,"status_NYR"
-    ,"status_EBI"
-    ,"status_ANR"
-    ,"ib*elapsed"
-    ,"bnc*elapsed"
 )
 ```
 
@@ -162,7 +153,6 @@ inherited_cols <- c(
 #generics intervention
 brand_intervention_ib <- x[inherited_cols]
 brand_intervention_ib["identical_brands"] <- asinh(sinh(x$identical_brands)+1) #add a single generic brand
-brand_intervention_ib["ib*elapsed"] <- brand_intervention_ib$identical_brands * brand_intervention_ib$elapsed_duration
 ```
 
 ```{r}
@@ -184,23 +174,13 @@ counterfact_marketing_ib <- list(
 )
 ```
 
-```{r}
-generated_bi <- gqs(
-    fit@stanmodel,
-    data=counterfact_marketing_ib,
-    draws=as.matrix(fit),
-    seed=11021585
-    )
-```
-
 ### USP DC
 
 
 ```{r}
 #formulary intervention
-brand_intervention_bnc <- x[c(inherited_cols,"identical_brands","ib*elapsed")]
+brand_intervention_bnc <- x[inherited_cols]
 brand_intervention_bnc["brand_name_counts"] <- asinh(sinh(x$brand_name_counts)+1) #add a single formulary competitor brand
-brand_intervention_bnc["bnc*elapsed"] <- brand_intervention_bnc$brand_name_counts * brand_intervention_bnc$elapsed_duration
 ```
 
 ```{r}
@@ -223,16 +203,6 @@ counterfact_marketing_bnc <- list(
 ```
 
 
-```{r}
-generated_bnc <- gqs(
-    fit@stanmodel,
-    data=counterfact_marketing_bnc,
-    draws=as.matrix(fit),
-    seed=11021585
-    )
-```
-
-
 # Fit Model
 
 
@@ -251,18 +221,85 @@ fit <- stan(
 ```
 
 
+```{r}
+generated_bi <- gqs(
+    fit@stanmodel,
+    data=counterfact_marketing_ib,
+    draws=as.matrix(fit),
+    seed=11021585
+    )
+```
+
+## Priors
 
 ```{r}
-hist(as.vector(extract(generated, pars="p_prior")$p_prior)) 
-hist(as.vector(extract(generated, pars="mu_prior")$mu_prior), )
-hist(as.vector(extract(generated, pars="sigma_prior")$sigma_prior))
+#| eval: false
+hist(as.vector(extract(generated_bi, pars="p_prior")$p_prior)) 
+hist(as.vector(extract(generated_bi, pars="mu_prior")$mu_prior), )
+hist(as.vector(extract(generated_bi, pars="sigma_prior")$sigma_prior))
+```
+
+```{r}
+df_ib_p <- data.frame(
+    p_prior=as.vector(extract(generated_bi, pars="p_prior")$p_prior)
+    ,p_predicted = as.vector(extract(generated_bi, pars="p_predicted")$p_predicted)
+)
+
+df_ib_prior <- data.frame(
+    mu_prior = as.vector(extract(generated_bi, pars="mu_prior")$mu_prior)
+    ,sigma_prior = as.vector(extract(generated_bi, pars="sigma_prior")$sigma_prior)
+)
+
+#p_prior
+ggplot(df_ib_p, aes(x=p_prior)) +
+    geom_density() + 
+    labs(
+        title="Implied Prior Distribution P"
+        ,subtitle=""
+        ,x="Probability Domain 'p'"
+        ,y="Probability Density"
+    )
+ggsave("./Images/TotalEffects/prior_p.png")
+
+#p_posterior
+ggplot(df_ib_p, aes(x=p_predicted)) +
+    geom_density() + 
+    labs(
+        title="Implied Posterior Distribution P"
+        ,subtitle=""
+        ,x="Probability Domain 'p'"
+        ,y="Probability Density"
+    )
+ggsave("./Images/TotalEffects/posterior_p.png")
+
+#mu_prior
+ggplot(df_ib_prior) +
+    geom_density(aes(x=mu_prior)) + 
+    labs(
+        title="Prior - Mu"
+        ,subtitle="same prior for all Mu values"
+        ,x="Mu"
+        ,y="Probability"
+    )
+ggsave("./Images/TotalEffects/prior_mu.png")
+
+#sigma_posterior
+ggplot(df_ib_prior) +
+    geom_density(aes(x=sigma_prior)) + 
+    labs(
+        title="Prior - Sigma"
+        ,subtitle="same prior for all Sigma values"
+        ,x="Sigma"
+        ,y="Probability"
+    )
+ggsave("./Images/TotalEffects/prior_sigma.png")
 ```
 
 
 
 ```{r}
 check_hmc_diagnostics(fit)
-hist(as.vector(extract(generated, pars="p_predicted")$p_predicted))
+#hist(as.vector(extract(generated_bi, pars="p_predicted")$p_predicted))
 ```
 
 
@@ -273,48 +310,35 @@ hist(as.vector(extract(generated, pars="p_predicted")$p_predicted))
 # Diagnostics
 
 ```{r}
+#| eval: false
 #trace plots
 plot(fit, pars=c("mu"), plotfun="trace")
 
 
-for (i in 1:4) {
-    print(
-        mcmc_rank_overlay(
-        fit, 
-        pars=c(
-            paste0("mu[",4*i-3,"]"),
-            paste0("mu[",4*i-2,"]"),
-            paste0("mu[",4*i-1,"]"),
-            paste0("mu[",4*i,"]")
-            ), 
-        n_bins=100
-        )+  legend_move("top") +
-             scale_colour_ghibli_d("KikiMedium")
-    )
-}
+mcmc_rank_overlay(
+fit, 
+pars=sapply(1:7, function(i) paste0("mu[",i,"]"))
+,n_bins=100
+)+  legend_move("top") +
+     scale_colour_ghibli_d("KikiMedium")
+
 ```
 
 ```{r}
+#| eval: false
 plot(fit, pars=c("sigma"), plotfun="trace")
 
-for (i in 1:4) {
-    print(
-        mcmc_rank_overlay(
-        fit, 
-        pars=c(
-            paste0("sigma[",4*i-3,"]"),
-            paste0("sigma[",4*i-2,"]"),
-            paste0("sigma[",4*i-1,"]"),
-            paste0("sigma[",4*i,"]")
-            ), 
-        n_bins=100
-        )+  legend_move("top") +
-             scale_colour_ghibli_d("KikiMedium")
-    )
-}
+
+mcmc_rank_overlay(
+fit, 
+pars=sapply(1:7, function(i) paste0("sigma[",i,"]"))
+,n_bins=100
+)+  legend_move("top") +
+     scale_colour_ghibli_d("KikiMedium")
 ```
 
 ```{r}
+#| eval: false
 #other diagnostics
 logpost <- log_posterior(fit)
 nuts_prmts <- nuts_params(fit)
@@ -323,70 +347,65 @@ posterior <- as.array(fit)
 ```
 
 ```{r}
+#| eval: false
 color_scheme_set("darkgray")
 div_style <- parcoord_style_np(div_color = "green", div_size = 0.05, div_alpha = 0.4)
 mcmc_parcoord(posterior, regex_pars = "mu", np=nuts_prmts, np_style = div_style, alpha = 0.05)
 ```
 
 ```{r}
-for (i in 1:4) {
-    mus = sapply(3:0, function(j) paste0("mu[",4*i-j ,"]"))
-    print(
-        mcmc_pairs(
-            posterior,
-            np = nuts_prmts,
-            pars=c(
-                mus,
-                "lp__"
-            ),
-            off_diag_args = list(size = 0.75)
-        )
-    )
-}
+#| eval: false
+mus = sapply(1:7, function(j) paste0("mu[",j ,"]"))
+mcmc_pairs(
+    posterior,
+    np = nuts_prmts,
+    pars=c(
+        mus,
+        "lp__"
+    ),
+    off_diag_args = list(size = 0.75)
+)
 
 
 
 ```
 
 ```{r}
+#| eval: false
 mcmc_parcoord(posterior,regex_pars = "sigma", np=nuts_prmts, alpha=0.05)
 ```
 
 ```{r}
-
-for (i in 1:4) {
-    params = sapply(3:0, function(j) paste0("sigma[",4*i-j ,"]"))
-    print(
-        mcmc_pairs(
-            posterior,
-            np = nuts_prmts,
-            pars=c(
-                params,
-                "lp__"
-            ),
-            off_diag_args = list(size = 0.75)
-        )
-    )
-}
+#| eval: false
+sigmas = sapply(1:7, function(j) paste0("sigma[",j ,"]"))
+mcmc_pairs(
+    posterior,
+    np = nuts_prmts,
+    pars=c(
+        sigmas,
+        "lp__"
+    ),
+    off_diag_args = list(size = 0.75)
+)
 ```
 
 
 ```{r}
-for (k in 1:22) {
-for (i in 1:4) {
-    params = sapply(3:0, function(j) paste0("beta[",k,",",4*i-j ,"]"))
-    print(
-        mcmc_pairs(
-            posterior,
-            np = nuts_prmts,
-            pars=c(
-                params,
-                "lp__"
-            ),
-            off_diag_args = list(size = 0.75)
-        )
-    )
-}}
+#| eval: false
+#for (k in 1:22) {
+#    params = sapply(1:7, function(j) paste0("beta[",k,",",j ,"]"))
+#    print(
+#        mcmc_pairs(
+#            posterior,
+#            np = nuts_prmts,
+#            pars=c(
+#                params,
+#                "lp__"
+#            ),
+#            off_diag_args = list(size = 0.75)
+#        )
+#    )
+#}
 ```
 
 
@@ -434,28 +453,15 @@ beta_list <- list(
         `22`="Special Purposes"
     ),
     parameters = c(
-        `1`="Elapsed Duration",
         # brands
-        `2`="asinh(Generic Brands)",
-        `3`="asinh(Competitors USPDC)",
+        `1`="asinh(Generic Brands)",
+        `2`="asinh(Competitors USPDC)",
         # population
-        `4`="asinh(High SDI)",
-        `5`="asinh(High-Medium SDI)",
-        `6`="asinh(Medium SDI)",
-        `7`="asinh(Low-Medium SDI)",
-        `8`="asinh(Low SDI)",
-        #Status
-        `9`="status_NYR",
-        `10`="status_EBI",
-        `11`="status_ANR",
-        #interactions for brands
-        `12`="ib*elapsed",
-        `13`="bnc*elapsed",
-        # interactions for status
-        `14`="sNYR*elapsed",
-        `15`="sEBI*elapsed",
-        `16`="sANR*elapsed"
-        
+        `3`="asinh(High SDI)",
+        `4`="asinh(High-Medium SDI)",
+        `5`="asinh(Medium SDI)",
+        `6`="asinh(Low-Medium SDI)",
+        `7`="asinh(Low SDI)"
     )
 )
 
@@ -566,96 +572,42 @@ parameter_mcmc_areas <- function(
 
 
 p1 <- parameter_mcmc_areas("beta",beta_list,fit,1)
+ggsave("./Images/TotalEffects/Parameters/01_generics.png")
 p2 <- parameter_mcmc_areas("beta",beta_list,fit,2)
-p3 <- parameter_mcmc_areas("beta",beta_list,fit,3)
+ggsave("./Images/TotalEffects/Parameters/02_uspdc.png")
+#p3 <- parameter_mcmc_areas("beta",beta_list,fit,3)
 #p4 <- parameter_mcmc_areas("beta",beta_list,fit,4)
 #p5 <- parameter_mcmc_areas("beta",beta_list,fit,5)
 #p6 <- parameter_mcmc_areas("beta",beta_list,fit,6)
 #p7 <- parameter_mcmc_areas("beta",beta_list,fit,7)
-#p8 <- parameter_mcmc_areas("beta",beta_list,fit,8)
-p9 <- parameter_mcmc_areas("beta",beta_list,fit,9)
-p10 <- parameter_mcmc_areas("beta",beta_list,fit,10)
-p11 <- parameter_mcmc_areas("beta",beta_list,fit,11)
-p12 <- parameter_mcmc_areas("beta",beta_list,fit,12)
-p13 <- parameter_mcmc_areas("beta",beta_list,fit,13)
-p14 <- parameter_mcmc_areas("beta",beta_list,fit,14)
-p15 <- parameter_mcmc_areas("beta",beta_list,fit,15)
-p16 <- parameter_mcmc_areas("beta",beta_list,fit,16)
+
 ```
 
 Note these have 95% outer CI and 80% inner (shaded)
 
 
-    1) "Elapsed Duration",
-    2) "asinh(Generic Brands)",
-    3) "asinh(Competitors USPDC)",
-    4) "asinh(High SDI)",
-    5) "asinh(High-Medium SDI)",
-    6) "asinh(Medium SDI)",
-    7) "asinh(Low-Medium SDI)",
-    8) "asinh(Low SDI)",
-    9) "status_NYR",
-    10) "status_EBI",
-    11) "status_ANR",
-    12) "ib*elapsed",
-    13) "bnc*elapsed",
-    14) "sNYR*elapsed",
-    15) "sEBI*elapsed",
-    16) "sANR*elapsed"
+    1) "asinh(Generic Brands)",
+    2) "asinh(Competitors USPDC)",
+    3) "asinh(High SDI)",
+    4) "asinh(High-Medium SDI)",
+    5) "asinh(Medium SDI)",
+    6) "asinh(Low-Medium SDI)",
+    7) "asinh(Low SDI)",
 
 of interest 
 - p1 + p2
-- p3 + p2
-- p2 + p12
-- p3 + p13
-- p9 + p14
-- p10 + p15
-- p11 + p16
-
-```{r}
-p1 + p2
-```
 
 
 ```{r}
-p2 + p3
-```
-
-```{r}
-p2 + p12
-```
-
-```{r}
-p3 + p13
-```
-
-```{r}
-p9 + p14
-```
-
-
-
-```{r}
-p10 + p15
+p1 + p2
+ggsave("./Images/TotalEffects/Parameters/1&2_generics_and_uspdc.png")
 ```
 
 
-```{r}
-p11 + p16
-```
-
 
 # Posterior Prediction
 
 
-```{r}
-#TODO: Convert to ggplot, stabilize y axis
-hist(as.vector(extract(generated, pars="p_predicted_default")$p_predicted_default))
-hist(as.vector(extract(generated, pars="p_predicted_intervention")$p_predicted_intervention))
-```
-
-
-
 
 
 ## Distribution of Predicted Differences 
@@ -668,19 +620,62 @@ hist(as.vector(extract(generated, pars="p_predicted_intervention")$p_predicted_i
 #### Generics
 
 
+
+
+
 ```{r}
+#| eval: false
 #TODO: Convert to ggplot, stabilize y axis
-hist(as.vector(extract(generated_ib, pars="p_predicted_default")$p_predicted_default), bins=100)
-hist(as.vector(extract(generated_ib, pars="p_predicted_intervention")$p_predicted_intervention), bins=100)
-hist(as.vector(extract(generated_ib, pars="predicted_difference")$predicted_difference), bins=100)
+hist(as.vector(extract(generated_bi, pars="p_predicted_default")$p_predicted_default))
+hist(as.vector(extract(generated_bi, pars="p_predicted_intervention")$p_predicted_intervention))
+hist(as.vector(extract(generated_bi, pars="predicted_difference")$predicted_difference))
 ```
 
 
+```{r}
+counterfact_predicted_ib <- data.frame(
+    p_predicted_default = as.vector(extract(generated_bi, pars="p_predicted_default")$p_predicted_default)
+    ,p_predicted_intervention = as.vector(extract(generated_bi, pars="p_predicted_intervention")$p_predicted_intervention)
+    ,predicted_difference = as.vector(extract(generated_bi, pars="predicted_difference")$predicted_difference)
+)
+```
+
 
 ```{r}
+ggplot(counterfact_predicted_ib, aes(x=p_predicted_default)) +
+    geom_density() + 
+    labs(
+        title="Predicted Distribution of 'p'"
+        ,subtitle="Intervention: None"
+        ,x="Probability Domain 'p'"
+        ,y="Probability Density"
+    )
+ggsave("./Images/TotalEffects/default_p_generic_intervention_base.png")
+
+ggplot(counterfact_predicted_ib, aes(x=p_predicted_intervention)) +
+    geom_density() + 
+    labs(
+        title="Predicted Distribution of 'p'"
+        ,subtitle="Intervention: Add a single generic competitor"
+        ,x="Probability Domain 'p'"
+        ,y="Probability Density"
+    )
+ggsave("./Images/TotalEffects/default_p_generic_intervention_interv.png")
+
+ggplot(counterfact_predicted_ib, aes(x=predicted_difference)) +
+    geom_density() + 
+    labs(
+        title="Predicted Distribution of differences 'p'"
+        ,subtitle="Intervention: Add a single generic competitor"
+        ,x="Difference in 'p' under treatment"
+        ,y="Probability Density"
+    )
+ggsave("./Images/TotalEffects/default_p_generic_intervention_distdiff.png")
+```
 
 
-pddf_ib <- data.frame(extract(generated_ib, pars="predicted_difference")$predicted_difference) |>
+```{r}
+pddf_ib <- data.frame(extract(generated_bi, pars="predicted_difference")$predicted_difference) |>
     pivot_longer(X1:X1343)
 
 #TODO: Fix Category names
@@ -691,16 +686,38 @@ pddf_ib["category_name"] <- sapply(pddf_ib$category, function(i) beta_list$group
 
 
 ```{r}
-
 ggplot(pddf_ib, aes(x=value,)) +
-    geom_histogram(bins=100) +
+    geom_density() +
     labs(
         title = "Distribution of predicted differences"
+        ,subtitle = "Intervention: add a single generic competitor"
         ,x = "Difference in probability due to intervention"
-        ,y = "Predicted counts"
+        ,y = "Probability Density"
     ) + 
-    #xlim(-0.3,0.1) +
     geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") 
+ggsave("./Images/TotalEffects/p_generic_intervention_distdiff_styled.png")
+
+ggplot(pddf_ib, aes(x=value,)) +
+    geom_density() +
+    facet_wrap(
+        ~factor(
+            category_name, 
+            levels=beta_list$groups
+            )
+        , labeller = label_wrap_gen(multi_line = TRUE)
+        , ncol=5
+        ,scales="free"
+        ) +
+    xlim(-1,1)+
+    labs(
+        title = "Distribution of predicted differences | By Group"
+        ,subtitle = "Intervention: add a single generic competitor"
+        ,x = "Difference in probability due to intervention"
+        ,y = "Probability Density"
+    ) + 
+    geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") +
+    theme(strip.text.x = element_text(size = 8))
+ggsave("./Images/TotalEffects/p_generic_intervention_distdiff_by_group.png")
 
 ggplot(pddf_ib, aes(x=value,)) +
     geom_histogram(bins=100) +
@@ -712,53 +729,100 @@ ggplot(pddf_ib, aes(x=value,)) +
         , labeller = label_wrap_gen(multi_line = TRUE)
         , ncol=5) +
     labs(
-        title = "Distribution of predicted differences",
-        subtitle = "By group"
+        title = "Histogram of predicted differences | By Group"
+        ,subtitle = "Intervention: add a single generic competitor"
         ,x = "Difference in probability due to intervention"
         ,y = "Predicted counts"
     ) + 
     #xlim(-0.25,0.1) +
     geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") +
     theme(strip.text.x = element_text(size = 8))
-
+ggsave("./Images/TotalEffects/p_generic_intervention_histdiff_by_group.png")
 ```
 
 
 
+
+
 #### USP DC
+
+
 ```{r}
+generated_bnc <- gqs(
+    fit@stanmodel,
+    data=counterfact_marketing_bnc,
+    draws=as.matrix(fit),
+    seed=11021585
+    )
+```
+
+```{r}
+#| eval: false
 #TODO: Convert to ggplot, stabilize y axis
 hist(as.vector(extract(generated_bnc, pars="p_predicted_default")$p_predicted_default), bins=100)
 hist(as.vector(extract(generated_bnc, pars="p_predicted_intervention")$p_predicted_intervention), bins=100)
 hist(as.vector(extract(generated_bnc, pars="predicted_difference")$predicted_difference), bins=100)
 ```
 
+```{r}
+counterfact_predicted_bnc <- data.frame(
+    p_predicted_default = as.vector(extract(generated_bnc, pars="p_predicted_default")$p_predicted_default)
+    ,p_predicted_intervention = as.vector(extract(generated_bnc, pars="p_predicted_intervention")$p_predicted_intervention)
+    ,predicted_difference = as.vector(extract(generated_bnc, pars="predicted_difference")$predicted_difference)
+)
+```
+
+
+
+
+
+
+
+
 
 ```{r}
 pddf_bnc <- data.frame(extract(generated_bnc, pars="predicted_difference")$predicted_difference) |>
     pivot_longer(X1:X1343)
 
-#Add Category names
+#TODO: Fix Category names
 pddf_bnc["entry_idx"] <- as.numeric(gsub("\\D","",pddf_bnc$name))
 pddf_bnc["category"] <-  sapply(pddf_bnc$entry_idx, function(i) df$category_id[i])
 pddf_bnc["category_name"] <- sapply(pddf_bnc$category, function(i) beta_list$groups[i])
-
-#add snapshot date
-pddf_bnc["snapshot_date"] <- sapply(pddf_bnc$entry_idx, function(i) as_date(df$earliest_date_observed[i]))
 ```
 
 
 ```{r}
-
 ggplot(pddf_bnc, aes(x=value,)) +
-    geom_histogram(bins=100) +
+    geom_density() +
     labs(
         title = "Distribution of predicted differences"
+        ,subtitle = "Intervention: add a single USP DC competitor"
         ,x = "Difference in probability due to intervention"
-        ,y = "Predicted counts"
+        ,y = "Probability Density"
     ) + 
-    #xlim(-0.3,0.1) +
     geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") 
+ggsave("./Images/TotalEffects/p_uspdc_intervention_distdiff_styled.png")
+
+ggplot(pddf_bnc, aes(x=value,)) +
+    geom_density() +
+    facet_wrap(
+        ~factor(
+            category_name, 
+            levels=beta_list$groups
+            )
+        , labeller = label_wrap_gen(multi_line = TRUE)
+        , ncol=5
+        ,scales="free"
+        ) +
+    labs(
+        title = "Distribution of predicted differences | By Group"
+        ,subtitle = "Intervention: add a single USP DC competitor"
+        ,x = "Difference in probability due to intervention"
+        ,y = "Probability Density"
+    ) + 
+    geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") +
+    theme(strip.text.x = element_text(size = 8))
+ggsave("./Images/TotalEffects/p_uspdc_intervention_distdiff_by_group.png")
 
 ggplot(pddf_bnc, aes(x=value,)) +
     geom_histogram(bins=100) +
@@ -770,14 +834,17 @@ ggplot(pddf_bnc, aes(x=value,)) +
         , labeller = label_wrap_gen(multi_line = TRUE)
         , ncol=5) +
     labs(
-        title = "Distribution of predicted differences",
-        subtitle = "By group"
+        title = "Histogram of predicted differences | By Group"
+        ,subtitle = "Intervention: add a single USP DC competitor"
         ,x = "Difference in probability due to intervention"
         ,y = "Predicted counts"
     ) + 
     #xlim(-0.25,0.1) +
     geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") +
     theme(strip.text.x = element_text(size = 8))
-
+ggsave("./Images/TotalEffects/p_uspdc_intervention_histdiff_by_group.png")
 ```
 
+
+
+

r-analysis/Hierarchal_Logistic.stan

@@ -23,7 +23,7 @@ data {
   real sigma_rate; //hyperprior
   //counterfactuals
   int<lower=0> Nx;
-  array[Nx] int<lower=1, upper=L> llx;
+  array[Nx] int<lower=1, upper=L> llx;//vec of categories
   array[Nx] row_vector[D] counterfact_x_tilde; // Posterior Prediction intervention
   array[Nx] row_vector[D] counterfact_x; // Posterior Prediction intervention
 }
@@ -87,6 +87,7 @@ generated quantities {
         p_predicted_default[n] = inv_logit(  counterfact_x[n] * beta[llx[n]] );
         p_predicted_intervention[n] = inv_logit(  counterfact_x_tilde[n] * beta[llx[n]] ); //intervention
 
-        predicted_difference[n] = p_predicted_default[n] - p_predicted_intervention[n];
+        //intervention - base case
+        predicted_difference[n] = p_predicted_intervention[n] - p_predicted_default[n];
     }
 }

r-analysis/Hierarchal_Logistic_prior.stan

@@ -0,0 +1,47 @@
+//
+// This Stan program defines a simple model, with a
+// vector of values 'y' modeled as normally distributed
+// with mean 'mu' and standard deviation 'sigma'.
+//
+// Learn more about model development with Stan at:
+//
+//    http://mc-stan.org/users/interfaces/rstan.html
+//    https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started
+//
+// The input data is a vector 'y' of length 'N'.
+data {
+  int<lower=1> D; //Number of parameters
+  int<lower=1> N; // Number of observations
+  int<lower=1> L; //Number of categories
+  int<lower=1, upper=L> ll[N];
+  row_vector[D] x[N];
+  real mu_m;
+  real mu_sd;
+  real sigma_shape;
+  real sigma_rate;
+}
+generated quantities {
+    //preallocate
+    real mu_prior[D];
+    real sigma_prior[D];
+    vector[D] beta_prior[L];
+    real p_prior[N]; // what I have priors about
+    //sample parameters
+    for (d in 1:D) {
+        mu_prior[d] = normal_rng(0,1);
+        sigma_prior[d] = gamma_rng(2,1);
+    }
+    for (l in 1:L) {
+        for (d in 1:D) {
+            beta_prior[l,d] = normal_rng(mu_prior[d],sigma_prior[d]);
+        }
+    }
+    //generate probabilities
+    {
+        vector[D] b_prior[N];//local var
+        for (n in 1:N){
+            b_prior[n] = beta_prior[ll[n]];
+            p_prior[n] = inv_logit(  x[n] * b_prior[n] );
+        }
+    }
+}

r-analysis/latex_output/DirectEffects/group_Digestive.tex

@@ -0,0 +1,6 @@
+c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec")
+c(-0.0333554671965411, -0.0816048428798888, -0.14822283281126, -0.266661171653673, -0.122811504866503, -0.0730682464154404, -0.0948854818270773, -0.121457456523795, -0.0405372878690479, -0.00533663159113219, -0.000585600111380386, -0.00557138538681874)
+c(`2.5%` = -0.650918866487675, `2.5%` = -1.46567361056997, `2.5%` = -2.00373524711419, `2.5%` = -0.884014357912585, `2.5%` = -0.589078622487128, `2.5%` = -0.515799157139537, `2.5%` = -0.575525591134666, `2.5%` = -0.619592982366338, `2.5%` = -0.700807445098139, `2.5%` = -0.478359030066064, `2.5%` = -0.747738822953945, `2.5%` = -0.547236884821695)
+c(`97.5%` = 0.586408105812293, `97.5%` = 1.25686631205631, `97.5%` = 1.63450278223622, `97.5%` = 0.239311394065014, `97.5%` = 0.226457215642141, `97.5%` = 0.2713042034849, `97.5%` = 0.267732959365188, `97.5%` = 0.239658080544198, `97.5%` = 0.626632265835835, `97.5%` = 0.452833279685459, `97.5%` = 0.722592110325922, `97.5%` = 0.521616619266678)
+c(`5%` = -0.521557208692412, `5%` = -1.20092272626125, `5%` = -1.62666028114303, `5%` = -0.750874732929316, `5%` = -0.479625923706422, `5%` = -0.418969780537693, `5%` = -0.465685567964911, `5%` = -0.507989133896294, `5%` = -0.561400134198236, `5%` = -0.354956928534609, `5%` = -0.571443956067767, `5%` = -0.425566130863293)
+c(`95%` = 0.458661660656351, `95%` = 1.0103835099505, `95%` = 1.31428822112951, `95%` = 0.163309336653743, `95%` = 0.160084526533093, `95%` = 0.208252318625455, `95%` = 0.196998436891251, `95%` = 0.176224129361429, `95%` = 0.475022207136582, `95%` = 0.34504055978718, `95%` = 0.568906077580551, `95%` = 0.402307136842525)

r-analysis/latex_output/DirectEffects/group_Endocrine, Nutritional, and Metabolic.tex

@@ -0,0 +1,6 @@
+c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec")
+c(-0.0353319092798262, -0.667659588573525, -0.426662961246809, 0.0627606452845482, -0.032122876366859, 0.0692372274974577, 0.00333854196734669, -0.0796819135752826, -0.20912536018799, -0.0110760315296536, -0.13143407853664, 0.208644014756123)
+c(`2.5%` = -0.617973586189941, `2.5%` = -1.84852850564476, `2.5%` = -1.58611083836008, `2.5%` = -0.417049157935813, `2.5%` = -0.387151005859201, `2.5%` = -0.255831609193942, `2.5%` = -0.371336426680338, `2.5%` = -0.492590738435841, `2.5%` = -0.945439609291323, `2.5%` = -0.485658627931781, `2.5%` = -0.907802053311157, `2.5%` = -0.233024875361211)
+c(`97.5%` = 0.557141349785682, `97.5%` = 0.333296222901717, `97.5%` = 0.676762640081955, `97.5%` = 0.580578562892596, `97.5%` = 0.330507422600221, `97.5%` = 0.472199940957423, `97.5%` = 0.38886451402152, `97.5%` = 0.275313479272503, `97.5%` = 0.315669435320966, `97.5%` = 0.447738518135558, `97.5%` = 0.469638387064318, `97.5%` = 0.947611094270466)
+c(`5%` = -0.508832891153791, `5%` = -1.61987740240911, `5%` = -1.374497411387, `5%` = -0.335726433843714, `5%` = -0.320537590648167, `5%` = -0.196582716266943, `5%` = -0.291245691126286, `5%` = -0.405842777306539, `5%` = -0.770582950456033, `5%` = -0.37653437100647, `5%` = -0.733995288939712, `5%` = -0.159905839522464)
+c(`95%` = 0.445024272718964, `95%` = 0.192677426246452, `95%` = 0.491906288928273, `95%` = 0.483104060696174, `95%` = 0.256114038078104, `95%` = 0.388274645012249, `95%` = 0.30403376761348, `95%` = 0.203557356084469, `95%` = 0.224931630208054, `95%` = 0.328780688143198, `95%` = 0.361698139473533, `95%` = 0.766303524326403)

r-analysis/latex_output/DirectEffects/group_Eye and Adnexa.tex

@@ -0,0 +1,6 @@
+c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec")
+c(-0.1350166586327, 1.42004536167462, -0.180236471981648, -0.0834031170778047, -0.114425712850253, -0.0624505479275495, -0.0770062362507839, -0.12325792019416, -0.062701663439268, -0.00370684105270185, 0.051697644596875, -0.0297390835449691)
+c(`2.5%` = -0.841164238146927, `2.5%` = 0.166228321710892, `2.5%` = -1.24185484456687, `2.5%` = -0.575720317140294, `2.5%` = -0.544731233105528, `2.5%` = -0.498602083969468, `2.5%` = -0.4980741080226, `2.5%` = -0.568847042903758, `2.5%` = -0.680839523831996, `2.5%` = -0.462623364969651, `2.5%` = -0.622045682166618, `2.5%` = -0.577014239447581)
+c(`97.5%` = 0.422370584061102, `97.5%` = 3.03047315901632, `97.5%` = 0.850790497217344, `97.5%` = 0.417839736473721, `97.5%` = 0.224427289664008, `97.5%` = 0.271253324519114, `97.5%` = 0.263581453569408, `97.5%` = 0.219054459221291, `97.5%` = 0.522372341682048, `97.5%` = 0.450642071977278, `97.5%` = 0.802350604222116, `97.5%` = 0.439020973201874)
+c(`5%` = -0.674921145513311, `5%` = 0.325020098610294, `5%` = -1.07057709615347, `5%` = -0.490154573212924, `5%` = -0.455866770748054, `5%` = -0.400298559553039, `5%` = -0.40994240294468, `5%` = -0.482628439569884, `5%` = -0.557990156682396, `5%` = -0.364772370803702, `5%` = -0.481699496200734, `5%` = -0.449023424748452)
+c(`95%` = 0.315064862057188, `95%` = 2.71868187996996, `95%` = 0.6825129130599, `95%` = 0.322993762960079, `95%` = 0.158724278745239, `95%` = 0.210081698558979, `95%` = 0.204675097314049, `95%` = 0.160771578813489, `95%` = 0.412334948233884, `95%` = 0.35762381742188, `95%` = 0.636739588170121, `95%` = 0.341991702173203)

r-analysis/latex_output/DirectEffects/group_Infections & Parasites.tex

@@ -0,0 +1,6 @@
+c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec")
+c(-0.0993758150556964, 0.683653688372939, -0.420548263203837, -0.462678784625764, 0.00186329456065648, 0.0597795199040114, 0.0681148600699909, 0.0461761490923264, -0.219385658942507, -0.026301838156295, 0.32478426535094, 0.020489615259928)
+c(`2.5%` = -0.649453534111085, `2.5%` = -0.0715480521081878, `2.5%` = -1.22916631970868, `2.5%` = -0.709034383494051, `2.5%` = -0.348811194923399, `2.5%` = -0.23351441505003, `2.5%` = -0.241101891952905, `2.5%` = -0.292204493477622, `2.5%` = -0.81900840565343, `2.5%` = -0.52584405348817, `2.5%` = -0.243402705068146, `2.5%` = -0.429251090289663)
+c(`97.5%` = 0.368644030362086, `97.5%` = 1.47768415582758, `97.5%` = 0.39318191253476, `97.5%` = -0.23735311291066, `97.5%` = 0.391318541907379, `97.5%` = 0.385985888234964, `97.5%` = 0.428550766860501, `97.5%` = 0.438579304744015, `97.5%` = 0.239595636184448, `97.5%` = 0.404201638523238, `97.5%` = 1.23628887068288, `97.5%` = 0.487394572761673)
+c(`5%` = -0.52192353020523, `5%` = 0.0437719361245331, `5%` = -1.10318164735297, `5%` = -0.662637668672948, `5%` = -0.280412083599109, `5%` = -0.17959411845604, `5%` = -0.181850570524538, `5%` = -0.231355058698291, `5%` = -0.702502858669705, `5%` = -0.40133526307109, `5%` = -0.164285973529124, `5%` = -0.340507185356601)
+c(`95%` = 0.282609130587132, `95%` = 1.35108734959306, `95%` = 0.262600483963485, `95%` = -0.27266375346642, `95%` = 0.307352752974449, `95%` = 0.318231328297662, `95%` = 0.362736140529989, `95%` = 0.358037869811668, `95%` = 0.169351071503387, `95%` = 0.316946023033097, `95%` = 1.05804976340078, `95%` = 0.386482609592056)

r-analysis/latex_output/DirectEffects/group_Mental & Behavioral.tex

@@ -0,0 +1,6 @@
+c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec")
+c(-0.101687967791986, 0.260625956246919, -1.41311346802269, 0.0234773335237628, -0.0167675265218763, 0.0856177665463779, 0.0541663300254734, -0.0489442451897826, -0.175613009906426, -0.0129978772390341, 0.0136500560555857, 0.0828587018493729)
+c(`2.5%` = -0.75353018780598, `2.5%` = -1.00766752361942, `2.5%` = -3.401492690184, `2.5%` = -0.449334233697236, `2.5%` = -0.3813420073426, `2.5%` = -0.239325630096086, `2.5%` = -0.294701408616, `2.5%` = -0.44544207912836, `2.5%` = -0.892050792681191, `2.5%` = -0.494462890468011, `2.5%` = -0.666840078342412, `2.5%` = -0.376439176914794)
+c(`97.5%` = 0.456963518658231, `97.5%` = 1.69446395582928, `97.5%` = 0.199302958070319, `97.5%` = 0.507180220455301, `97.5%` = 0.368701157216287, `97.5%` = 0.517238144007455, `97.5%` = 0.484238515353489, `97.5%` = 0.312151961605374, `97.5%` = 0.370881206753106, `97.5%` = 0.435596577978869, `97.5%` = 0.721254548490607, `97.5%` = 0.696764948267981)
+c(`5%` = -0.598975479040644, `5%` = -0.774388773799267, `5%` = -3.03473408528801, `5%` = -0.369579631037732, `5%` = -0.302781726817594, `5%` = -0.180751834428207, `5%` = -0.227459855411846, `5%` = -0.359959289739994, `5%` = -0.726956896133474, `5%` = -0.37607446715096, `5%` = -0.511640963000957, `5%` = -0.283655680952849)
+c(`95%` = 0.334682947415185, `95%` = 1.39113469911214, `95%` = -0.0370570732959479, `95%` = 0.424927943281204, `95%` = 0.290563883495334, `95%` = 0.420212397505738, `95%` = 0.392316891142529, `95%` = 0.240788422786828, `95%` = 0.268210851959822, `95%` = 0.333041306872968, `95%` = 0.570750639989039, `95%` = 0.544517445336076)

r-analysis/latex_output/DirectEffects/group_Musculoskeletal.tex

@@ -0,0 +1,6 @@
+c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec")
+c(0.100124327907652, -1.41625310169202, 1.52446064588643, -0.0873137500894338, -0.0677464856346281, 0.0108650892063307, 0.00612669940351482, -0.0314187576539557, -0.211716341147135, -0.00183935600163672, -0.0738493576307788, 0.115533405660669)
+c(`2.5%` = -0.406446255373983, `2.5%` = -2.60148475745005, `2.5%` = 0.527275445018903, `2.5%` = -0.584022199941087, `2.5%` = -0.463154561951676, `2.5%` = -0.346315618199531, `2.5%` = -0.362381508509159, `2.5%` = -0.409819426261724, `2.5%` = -0.936591248518692, `2.5%` = -0.457818242374006, `2.5%` = -0.798071774552942, `2.5%` = -0.329263294357893)
+c(`97.5%` = 0.797719164264997, `97.5%` = -0.457781574665431, `97.5%` = 2.69049448523487, `97.5%` = 0.374989682835585, `97.5%` = 0.295740868485436, `97.5%` = 0.377261709291995, `97.5%` = 0.395888264864696, `97.5%` = 0.341241054883828, `97.5%` = 0.314398740452913, `97.5%` = 0.452102307502102, `97.5%` = 0.547821159342026, `97.5%` = 0.740236931779179)
+c(`5%` = -0.316960032380135, `5%` = -2.39673285825223, `5%` = 0.677198726234082, `5%` = -0.48370456159241, `5%` = -0.380333066540771, `5%` = -0.271668438834234, `5%` = -0.28575489564353, `5%` = -0.331549343941397, `5%` = -0.776805336768321, `5%` = -0.356984822002284, `5%` = -0.649098498864267, `5%` = -0.244951545599123)
+c(`95%` = 0.636944255371381, `95%` = -0.579922080756115, `95%` = 2.46984046267875, `95%` = 0.301174279247803, `95%` = 0.219720584710747, `95%` = 0.305577531163344, `95%` = 0.314025671253092, `95%` = 0.263968986148209, `95%` = 0.22352188185812, `95%` = 0.343376182190238, `95%` = 0.424983622057134, `95%` = 0.586186029847553)

r-analysis/latex_output/DirectEffects/group_Neoplasms.tex

@@ -0,0 +1,6 @@
+c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec")
+c(-0.412146324077768, 0.470951452129627, -1.2444367751168, 0.245268419893298, -0.0946502363520193, 0.0500740339796919, -0.0704987792914352, -0.124433125807786, -0.358974070196284, -0.00267878134821512, -0.476611882301335, -0.140862225354917)
+c(`2.5%` = -0.990046725218716, `2.5%` = 0.0825563723532098, `2.5%` = -1.78375367793506, `2.5%` = -0.144576058853997, `2.5%` = -0.509816834681553, `2.5%` = -0.248086617381755, `2.5%` = -0.463337090435845, `2.5%` = -0.545151665330706, `2.5%` = -0.959715545078129, `2.5%` = -0.477380824369578, `2.5%` = -1.42904260420137, `2.5%` = -0.614365567191018)
+c(`97.5%` = 0.0176791224477116, `97.5%` = 0.869227382956849, `97.5%` = -0.71625139500421, `97.5%` = 0.711161632402175, `97.5%` = 0.235929001218496, `97.5%` = 0.403217442159893, `97.5%` = 0.250453043701491, `97.5%` = 0.208799589086939, `97.5%` = 0.0761919118896935, `97.5%` = 0.485090234449767, `97.5%` = 0.0901610144027345, `97.5%` = 0.205381626274404)
+c(`5%` = -0.880856751562785, `5%` = 0.140776593909285, `5%` = -1.69975002339859, `5%` = -0.0851775550449366, `5%` = -0.413144404534561, `5%` = -0.190555995277503, `5%` = -0.374730282423007, `5%` = -0.457195436456302, `5%` = -0.860866809127253, `5%` = -0.371058543649785, `5%` = -1.24192236504004, `5%` = -0.521580687834855)
+c(`95%` = -0.0294487230376872, `95%` = 0.805026483916245, `95%` = -0.79680171298815, `95%` = 0.622920880640266, `95%` = 0.180557342384185, `95%` = 0.328458422894137, `95%` = 0.191388773507259, `95%` = 0.144775027690501, `95%` = 0.0202098111117333, `95%` = 0.368527660640565, `95%` = 0.0339253948301201, `95%` = 0.150938308973965)

r-analysis/latex_output/DirectEffects/group_Nervous System.tex

@@ -0,0 +1,6 @@
+c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec")
+c(-0.0605258874521727, 0.208110428258286, 2.932306880193, -0.476036244602958, -0.138197079911931, 0.0472640116153194, -0.0108024185768078, -0.0673889444925787, 0.0439801430116134, -0.00220242035797092, 0.0222560987441026, -0.0954924005742835)
+c(`2.5%` = -0.668423764362022, `2.5%` = -0.483919695463572, `2.5%` = 1.32770940293854, `2.5%` = -1.08972255835219, `2.5%` = -0.588355736454172, `2.5%` = -0.288061459094765, `2.5%` = -0.390261122641681, `2.5%` = -0.480037769561072, `2.5%` = -0.52071567059202, `2.5%` = -0.467981470659442, `2.5%` = -0.64696023851359, `2.5%` = -0.702986558104766)
+c(`97.5%` = 0.524706569957862, `97.5%` = 0.860804399758541, `97.5%` = 4.8161142366925, `97.5%` = 0.00951208071252858, `97.5%` = 0.205030274179544, `97.5%` = 0.447777447414387, `97.5%` = 0.375894039808643, `97.5%` = 0.309831367516505, `97.5%` = 0.714311594742576, `97.5%` = 0.46197268399125, `97.5%` = 0.724174566356542, `97.5%` = 0.352194753343582)
+c(`5%` = -0.540693659717227, `5%` = -0.358913993804725, `5%` = 1.56455100307583, `5%` = -0.970987430695906, `5%` = -0.492652682388389, `5%` = -0.226103001674273, `5%` = -0.310685728948158, `5%` = -0.401058959418401, `5%` = -0.416255094218329, `5%` = -0.352812984615348, `5%` = -0.514000899450722, `5%` = -0.55490968224003)
+c(`95%` = 0.404566643306311, `95%` = 0.757665883111782, `95%` = 4.4435402069247, `95%` = -0.0535686895330286, `95%` = 0.144052434219696, `95%` = 0.356322084893283, `95%` = 0.295168504418982, `95%` = 0.235723096029231, `95%` = 0.569378098960284, `95%` = 0.353283663339729, `95%` = 0.576987914910334, `95%` = 0.268009451996814)

r-analysis/latex_output/DirectEffects/group_Skin & Subcutaneaous tissue.tex

@@ -0,0 +1,6 @@
+c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec")
+c(-0.174849840107956, 0.344654754591335, -0.71289290741607, -0.185341893373765, -0.0612704269574426, 0.0475554801866666, 0.0209325549818404, 0.00823090691189444, -0.140469476402341, -0.000991729207921393, 0.0426395752164606, 0.0531332616653892)
+c(`2.5%` = -0.853225149285778, `2.5%` = -0.81212639023518, `2.5%` = -2.47632399275718, `2.5%` = -0.700340720981917, `2.5%` = -0.459624700583444, `2.5%` = -0.299576900991634, `2.5%` = -0.329465385054038, `2.5%` = -0.35530723101666, `2.5%` = -0.818237407714228, `2.5%` = -0.457007790658148, `2.5%` = -0.626446320243322, `2.5%` = -0.42852558763013)
+c(`97.5%` = 0.313015825195173, `97.5%` = 1.64628843970952, `97.5%` = 0.803673159441113, `97.5%` = 0.264097087549475, `97.5%` = 0.301714093464364, `97.5%` = 0.457951093623432, `97.5%` = 0.41718558607108, `97.5%` = 0.42368030495614, `97.5%` = 0.415680090003177, `97.5%` = 0.473772689053127, `97.5%` = 0.774791684477986, `97.5%` = 0.622393397809352)
+c(`5%` = -0.69853877504723, `5%` = -0.619677989159712, `5%` = -2.16966249843568, `5%` = -0.602161468147168, `5%` = -0.370652637547503, `5%` = -0.233289235150446, `5%` = -0.267307968491817, `5%` = -0.289828310680018, `5%` = -0.66997803833358, `5%` = -0.350248519811252, `5%` = -0.483515522092064, `5%` = -0.330171694279594)
+c(`95%` = 0.228050943041459, `95%` = 1.41171899585957, `95%` = 0.585314360782336, `95%` = 0.187637916801343, `95%` = 0.231239446941901, `95%` = 0.360332344911533, `95%` = 0.329385758977443, `95%` = 0.33895575065099, `95%` = 0.315101103321463, `95%` = 0.355492422245274, `95%` = 0.614652923649533, `95%` = 0.486396526448576)

r-analysis/latex_output/DirectEffects/group_status_ANR.tex

@@ -0,0 +1,10 @@
+c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.")
+c(-0.0379222039996637, -0.00951521433050643, -0.0357669209565548, -0.0289858802339238, -0.0384045782213899, -0.0354852935888951, -0.195806801668196, -0.0319728051993748, -0.064502474892061, -0.0359000714018003, -0.209385718567549, -0.0293405266823335, -0.166256613851307, -0.204154479367649, -0.343021435135372, 0.0429675640807533, -0.031540048708067, -0.0346985641986963, -0.0303375281259717, -0.13391213882268, -0.0315874427691754, -0.0292508480128129)
+c(`2.5%` = -0.670177498740691, `2.5%` = -0.606574716528012, `2.5%` = -0.660124076867341, `2.5%` = -0.658500223161749, `2.5%` = -0.673266724039317, `2.5%` = -0.693646849412836, `2.5%` = -0.917059388708302, `2.5%` = -0.662394912651839, `2.5%` = -0.67991481986874, `2.5%` = -0.670316698080103, `2.5%` = -0.83183723815622, `2.5%` = -0.663476379211969, `2.5%` = -0.854735257874132, `2.5%` = -0.918576287776926, `2.5%` = -0.963532341357661, `2.5%` = -0.495917243186253, `2.5%` = -0.64751983879636, `2.5%` = -0.673951139603439, 
+`2.5%` = -0.665804518054749, `2.5%` = -0.791664538636436, `2.5%` = -0.643761100670645, `2.5%` = -0.680737220370752)
+c(`97.5%` = 0.579428469021833, `97.5%` = 0.640138775494956, `97.5%` = 0.596180548733038, `97.5%` = 0.619030332938483, `97.5%` = 0.597678927212641, `97.5%` = 0.6121441149317, `97.5%` = 0.323295322658126, `97.5%` = 0.590730699845894, `97.5%` = 0.503098249043251, `97.5%` = 0.607349406190736, `97.5%` = 0.228895375416542, `97.5%` = 0.624129832779025, `97.5%` = 0.342536899260382, `97.5%` = 0.307896298234294, `97.5%` = 0.0903633166835597, `97.5%` = 0.686527591660775, `97.5%` = 0.616181981700487, `97.5%` = 0.599647736473311, 
+`97.5%` = 0.609123918623858, `97.5%` = 0.415337198000103, `97.5%` = 0.597111489383847, `97.5%` = 0.632428409170173)
+c(`5%` = -0.530855657109975, `5%` = -0.487829039222839, `5%` = -0.530023192811465, `5%` = -0.516610981668542, `5%` = -0.524022832574353, `5%` = -0.537218989651104, `5%` = -0.75728605285178, `5%` = -0.517067859106373, `5%` = -0.547235882003818, `5%` = -0.536626649173697, `5%` = -0.691946442016388, `5%` = -0.52266339334223, `5%` = -0.70021531860572, `5%` = -0.769560225018392, `5%` = -0.844315621869912, `5%` = -0.393051567766974, `5%` = -0.519010088416499, `5%` = -0.529941857869653, `5%` = -0.52218968602785, 
+`5%` = -0.648665425674694, `5%` = -0.520022794035758, `5%` = -0.521893002755623)
+c(`95%` = 0.449851048402911, `95%` = 0.496036928523184, `95%` = 0.452249441742121, `95%` = 0.47353271148754, `95%` = 0.454890426424996, `95%` = 0.474673790264394, `95%` = 0.22238953047732, `95%` = 0.44333608430914, `95%` = 0.387054472484903, `95%` = 0.475167927437824, `95%` = 0.156987753663085, `95%` = 0.473536190521047, `95%` = 0.254381363435638, `95%` = 0.217911529700368, `95%` = 0.0348859178585696, `95%` = 0.552104217589191, `95%` = 0.462090261872998, `95%` = 0.456310261272674, `95%` = 0.474214855342067, 
+`95%` = 0.307884811241422, `95%` = 0.465192052817642, `95%` = 0.472198045319103)

r-analysis/latex_output/DirectEffects/parameters_10_status_EBI.tex

@@ -0,0 +1,10 @@
+c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.")
+c(-0.00232413253272223, -0.00152293417694882, -0.00353563813667985, -0.00310660613711883, -0.00533663159113219, -0.00305599644590309, -0.0110760315296536, -0.00588960985826809, -0.00370684105270185, -0.00470552774062355, -0.026301838156295, -0.00292165177766262, -0.0129978772390341, -0.00183935600163672, -0.00267878134821512, -0.00220242035797092, -0.000914878424206371, -0.00258882299014672, 0.000504336438586614, -0.000991729207921393, -0.000800806458059062, -0.00386686775250893)
+c(`2.5%` = -0.479474973075092, `2.5%` = -0.472669740624549, `2.5%` = -0.464039194626794, `2.5%` = -0.473392603483428, `2.5%` = -0.478359030066064, `2.5%` = -0.455950775505214, `2.5%` = -0.485658627931781, `2.5%` = -0.485882001168744, `2.5%` = -0.462623364969651, `2.5%` = -0.464633556660333, `2.5%` = -0.52584405348817, `2.5%` = -0.46165946058897, `2.5%` = -0.494462890468011, `2.5%` = -0.457818242374006, `2.5%` = -0.477380824369578, `2.5%` = -0.467981470659442, `2.5%` = -0.462006993832474, `2.5%` = -0.475538447435748, 
+`2.5%` = -0.459119942403029, `2.5%` = -0.457007790658148, `2.5%` = -0.457457906185736, `2.5%` = -0.463105985034812)
+c(`97.5%` = 0.472157783917002, `97.5%` = 0.465334754332652, `97.5%` = 0.444423983236463, `97.5%` = 0.450919265047758, `97.5%` = 0.452833279685459, `97.5%` = 0.451291140159942, `97.5%` = 0.447738518135558, `97.5%` = 0.47182876476933, `97.5%` = 0.450642071977278, `97.5%` = 0.460733730501955, `97.5%` = 0.404201638523238, `97.5%` = 0.463075602426935, `97.5%` = 0.435596577978869, `97.5%` = 0.452102307502102, `97.5%` = 0.485090234449767, `97.5%` = 0.46197268399125, `97.5%` = 0.471768785447442, `97.5%` = 0.469214434976901, 
+`97.5%` = 0.458559371773605, `97.5%` = 0.473772689053127, `97.5%` = 0.468011769036541, `97.5%` = 0.460260492516532)
+c(`5%` = -0.362566524131353, `5%` = -0.365391855607623, `5%` = -0.356688829996404, `5%` = -0.360094627174451, `5%` = -0.354956928534609, `5%` = -0.35377118365327, `5%` = -0.37653437100647, `5%` = -0.370648036848808, `5%` = -0.364772370803702, `5%` = -0.363091670495567, `5%` = -0.40133526307109, `5%` = -0.359038021511569, `5%` = -0.37607446715096, `5%` = -0.356984822002284, `5%` = -0.371058543649785, `5%` = -0.352812984615348, `5%` = -0.357581652703343, `5%` = -0.357584782489892, `5%` = -0.358080523145794, 
+`5%` = -0.350248519811252, `5%` = -0.359275569693152, `5%` = -0.355239427293753)
+c(`95%` = 0.349080687253274, `95%` = 0.362582528037606, `95%` = 0.344628062144889, `95%` = 0.343429488771613, `95%` = 0.34504055978718, `95%` = 0.347420792879581, `95%` = 0.328780688143198, `95%` = 0.352000827804009, `95%` = 0.35762381742188, `95%` = 0.353870410467441, `95%` = 0.316946023033097, `95%` = 0.352512087776552, `95%` = 0.333041306872968, `95%` = 0.343376182190238, `95%` = 0.368527660640565, `95%` = 0.353283663339729, `95%` = 0.354267255995946, `95%` = 0.347884235465927, `95%` = 0.352698507186818, 
+`95%` = 0.355492422245274, `95%` = 0.354508762801702, `95%` = 0.347938178694997)

r-analysis/latex_output/DirectEffects/parameters_11_status_Rec.tex

@@ -0,0 +1,10 @@
+c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.")
+c(-0.00224061119544955, -0.0498803962359136, 0.0029866500758097, 0.0092020834745846, -0.00557138538681874, 0.00139701841247001, 0.208644014756123, 0.00486415637216138, -0.0297390835449691, 0.00664076692247885, 0.020489615259928, 0.00270098452440913, 0.0828587018493729, 0.115533405660669, -0.140862225354917, -0.0954924005742835, 0.00218728416741836, 0.00490518108019053, -0.000597898274967985, 0.0531332616653892, 0.00140665840745891, 0.00267108484868849)
+c(`2.5%` = -0.573130925585206, `2.5%` = -0.631300654586586, `2.5%` = -0.549248794254278, `2.5%` = -0.526524124449695, `2.5%` = -0.547236884821695, `2.5%` = -0.525794262806427, `2.5%` = -0.233024875361211, `2.5%` = -0.531351924132048, `2.5%` = -0.577014239447581, `2.5%` = -0.526231597802405, `2.5%` = -0.429251090289663, `2.5%` = -0.539844609215633, `2.5%` = -0.376439176914794, `2.5%` = -0.329263294357893, `2.5%` = -0.614365567191018, `2.5%` = -0.702986558104766, `2.5%` = -0.548813703529005, `2.5%` = -0.549035834183446, 
+`2.5%` = -0.55240500338214, `2.5%` = -0.42852558763013, `2.5%` = -0.532991842266845, `2.5%` = -0.561981734264935)
+c(`97.5%` = 0.535870607314021, `97.5%` = 0.439183977577692, `97.5%` = 0.531820860628844, `97.5%` = 0.557228636508852, `97.5%` = 0.521616619266678, `97.5%` = 0.521530978763819, `97.5%` = 0.947611094270466, `97.5%` = 0.551470726780118, `97.5%` = 0.439020973201874, `97.5%` = 0.543800507198643, `97.5%` = 0.487394572761673, `97.5%` = 0.537619293317655, `97.5%` = 0.696764948267981, `97.5%` = 0.740236931779179, `97.5%` = 0.205381626274404, `97.5%` = 0.352194753343582, `97.5%` = 0.553383244915363, `97.5%` = 0.559402688668633, 
+`97.5%` = 0.550312950172821, `97.5%` = 0.622393397809352, `97.5%` = 0.536869755062453, `97.5%` = 0.557804559325436)
+c(`5%` = -0.428266097170789, `5%` = -0.491562554012717, `5%` = -0.411292594357374, `5%` = -0.403177595011685, `5%` = -0.425566130863293, `5%` = -0.404058425652893, `5%` = -0.159905839522464, `5%` = -0.416161496643271, `5%` = -0.449023424748452, `5%` = -0.399764467982059, `5%` = -0.340507185356601, `5%` = -0.420233862640142, `5%` = -0.283655680952849, `5%` = -0.244951545599123, `5%` = -0.521580687834855, `5%` = -0.55490968224003, `5%` = -0.422336361660143, `5%` = -0.422015963935169, `5%` = -0.420900175948769, 
+`5%` = -0.330171694279594, `5%` = -0.41323722677094, `5%` = -0.42744212405137)
+c(`95%` = 0.404392809444781, `95%` = 0.333871293266063, `95%` = 0.417529613693018, `95%` = 0.430463617558602, `95%` = 0.402307136842525, `95%` = 0.410955507727826, `95%` = 0.766303524326403, `95%` = 0.430330128267061, `95%` = 0.341991702173203, `95%` = 0.419913772461363, `95%` = 0.386482609592056, `95%` = 0.421100502019285, `95%` = 0.544517445336076, `95%` = 0.586186029847553, `95%` = 0.150938308973965, `95%` = 0.268009451996814, `95%` = 0.425628186353177, `95%` = 0.433826523724275, `95%` = 0.417184121248096, 
+`95%` = 0.486396526448576, `95%` = 0.410430471444198, `95%` = 0.43788787573263)

r-analysis/latex_output/DirectEffects/parameters_12_status_ANR.tex

@@ -0,0 +1,10 @@
+c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.")
+c(-0.033444959026225, -0.00793522059592051, -0.0336009736151623, -0.0316339144412533, -0.0405372878690479, -0.0414663539452286, -0.20912536018799, -0.0266756188322575, -0.062701663439268, -0.0331874740057613, -0.219385658942507, -0.0325137781481118, -0.175613009906426, -0.211716341147135, -0.358974070196284, 0.0439801430116134, -0.0306047651482687, -0.032498476375438, -0.0371883825844407, -0.140469476402341, -0.0333940650001783, -0.0354885056744574)
+c(`2.5%` = -0.666923716865872, `2.5%` = -0.64102067023692, `2.5%` = -0.677795179892062, `2.5%` = -0.670757215379559, `2.5%` = -0.700807445098139, `2.5%` = -0.693814265711575, `2.5%` = -0.945439609291323, `2.5%` = -0.649489899228185, `2.5%` = -0.680839523831996, `2.5%` = -0.69007268009126, `2.5%` = -0.81900840565343, `2.5%` = -0.679848629284621, `2.5%` = -0.892050792681191, `2.5%` = -0.936591248518692, `2.5%` = -0.959715545078129, `2.5%` = -0.52071567059202, `2.5%` = -0.684856132016299, `2.5%` = -0.682381096206198, 
+`2.5%` = -0.682121426892847, `2.5%` = -0.818237407714228, `2.5%` = -0.644091572578507, `2.5%` = -0.695950746544654)
+c(`97.5%` = 0.62590334980134, `97.5%` = 0.653919501490395, `97.5%` = 0.641015835184623, `97.5%` = 0.629010798078368, `97.5%` = 0.626632265835835, `97.5%` = 0.605986429897587, `97.5%` = 0.315669435320966, `97.5%` = 0.629421610550102, `97.5%` = 0.522372341682048, `97.5%` = 0.611617883076929, `97.5%` = 0.239595636184448, `97.5%` = 0.614136402646953, `97.5%` = 0.370881206753106, `97.5%` = 0.314398740452913, `97.5%` = 0.0761919118896935, `97.5%` = 0.714311594742576, `97.5%` = 0.62707456073199, `97.5%` = 0.660256568215515, 
+`97.5%` = 0.614029672287225, `97.5%` = 0.415680090003177, `97.5%` = 0.590770974069619, `97.5%` = 0.638335616472465)
+c(`5%` = -0.534614253816882, `5%` = -0.492652438031023, `5%` = -0.534510689945915, `5%` = -0.537204763734244, `5%` = -0.561400134198236, `5%` = -0.554901107934352, `5%` = -0.770582950456033, `5%` = -0.526625109409884, `5%` = -0.557990156682396, `5%` = -0.545095270800367, `5%` = -0.702502858669705, `5%` = -0.532227460109175, `5%` = -0.726956896133474, `5%` = -0.776805336768321, `5%` = -0.860866809127253, `5%` = -0.416255094218329, `5%` = -0.540848631429852, `5%` = -0.545406019188074, `5%` = -0.556323223126082, 
+`5%` = -0.66997803833358, `5%` = -0.528524246946612, `5%` = -0.557925242336448)
+c(`95%` = 0.477502832552862, `95%` = 0.513718992636229, `95%` = 0.489098182794968, `95%` = 0.480711435248938, `95%` = 0.475022207136582, `95%` = 0.474127651026557, `95%` = 0.224931630208054, `95%` = 0.491419189883086, `95%` = 0.412334948233884, `95%` = 0.482084498841415, `95%` = 0.169351071503387, `95%` = 0.478552110978265, `95%` = 0.268210851959822, `95%` = 0.22352188185812, `95%` = 0.0202098111117333, `95%` = 0.569378098960284, `95%` = 0.485945600247666, `95%` = 0.497377449520452, `95%` = 0.472090491001111, 
+`95%` = 0.315101103321463, `95%` = 0.458319237867322, `95%` = 0.490829889891898)

r-analysis/latex_output/DirectEffects/parameters_1_Elapsed Duration.tex

@@ -0,0 +1,10 @@
+c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.")
+c(-0.0295164133163417, -0.0430143836673774, -0.0339902806330816, -0.0260159056108803, -0.0333554671965411, -0.028359266287689, -0.0353319092798262, -0.0187357101597514, -0.1350166586327, -0.0260977976963767, -0.0993758150556964, -0.0236033005860729, -0.101687967791986, 0.100124327907652, -0.412146324077768, -0.0605258874521727, -0.024078230002875, -0.0269598652137639, -0.0274504939892296, -0.174849840107956, -0.0222497943593906, -0.0274902413057739)
+c(`2.5%` = -0.646070710340414, `2.5%` = -0.683290321643176, `2.5%` = -0.675081729811003, `2.5%` = -0.648421169748859, `2.5%` = -0.650918866487675, `2.5%` = -0.668055634029354, `2.5%` = -0.617973586189941, `2.5%` = -0.640444657164909, `2.5%` = -0.841164238146927, `2.5%` = -0.670733739121956, `2.5%` = -0.649453534111085, `2.5%` = -0.635643676510193, `2.5%` = -0.75353018780598, `2.5%` = -0.406446255373983, `2.5%` = -0.990046725218716, `2.5%` = -0.668423764362022, `2.5%` = -0.65459523058199, `2.5%` = -0.623518595242253, 
+`2.5%` = -0.639580335809163, `2.5%` = -0.853225149285778, `2.5%` = -0.651372688023807, `2.5%` = -0.665950740608084)
+c(`97.5%` = 0.622118536783365, `97.5%` = 0.567754232733557, `97.5%` = 0.594665290830079, `97.5%` = 0.595712650833978, `97.5%` = 0.586408105812293, `97.5%` = 0.61702506930373, `97.5%` = 0.557141349785682, `97.5%` = 0.64037824695388, `97.5%` = 0.422370584061102, `97.5%` = 0.619806919714738, `97.5%` = 0.368644030362086, `97.5%` = 0.596438871658726, `97.5%` = 0.456963518658231, `97.5%` = 0.797719164264997, `97.5%` = 0.0176791224477116, `97.5%` = 0.524706569957862, `97.5%` = 0.622703525731803, `97.5%` = 0.570789155361791, 
+`97.5%` = 0.59747717452121, `97.5%` = 0.313015825195173, `97.5%` = 0.627269012162122, `97.5%` = 0.615198351898292)
+c(`5%` = -0.510251564339674, `5%` = -0.52441458740886, `5%` = -0.532441957391025, `5%` = -0.518410421043954, `5%` = -0.521557208692412, `5%` = -0.521871400612245, `5%` = -0.508832891153791, `5%` = -0.518203720240489, `5%` = -0.674921145513311, `5%` = -0.531996138266231, `5%` = -0.52192353020523, `5%` = -0.500496765978635, `5%` = -0.598975479040644, `5%` = -0.316960032380135, `5%` = -0.880856751562785, `5%` = -0.540693659717227, `5%` = -0.519375237016899, `5%` = -0.509904935942235, `5%` = -0.513849598148613, 
+`5%` = -0.69853877504723, `5%` = -0.511181165016275, `5%` = -0.520466766457042)
+c(`95%` = 0.471281041217163, `95%` = 0.423703175708398, `95%` = 0.466378617261805, `95%` = 0.454221478113165, `95%` = 0.458661660656351, `95%` = 0.460036483429395, `95%` = 0.445024272718964, `95%` = 0.492401099446282, `95%` = 0.315064862057188, `95%` = 0.475963967540287, `95%` = 0.282609130587132, `95%` = 0.45986621483045, `95%` = 0.334682947415185, `95%` = 0.636944255371381, `95%` = -0.0294487230376872, `95%` = 0.404566643306311, `95%` = 0.478649635433352, `95%` = 0.441853123918437, `95%` = 0.463352362829126, 
+`95%` = 0.228050943041459, `95%` = 0.482992956431603, `95%` = 0.466730867021221)

r-analysis/latex_output/DirectEffects/parameters_2_asinh(Generic Brands).tex

@@ -0,0 +1,10 @@
+c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.")
+c(-0.12331418463673, -0.643524712834449, -0.155293145618607, -0.00107765414463224, -0.14822283281126, 0.00881755884088804, -0.426662961246809, -0.0178268855590179, -0.180236471981648, -0.290840496837507, -0.420548263203837, -0.00380033220537924, -1.41311346802269, 1.52446064588643, -1.2444367751168, 2.932306880193, 0.00169846910876418, -0.00893420267820256, -0.235385994622511, -0.71289290741607, -0.00820736853904142, -0.12592765159372)
+c(`2.5%` = -2.02564979096055, `2.5%` = -2.39439013494348, `2.5%` = -1.99208722130077, `2.5%` = -1.83809201587386, `2.5%` = -2.00373524711419, `2.5%` = -1.87235054261414, `2.5%` = -1.58611083836008, `2.5%` = -1.81806137603714, `2.5%` = -1.24185484456687, `2.5%` = -2.12331233705335, `2.5%` = -1.22916631970868, `2.5%` = -1.87838853425636, `2.5%` = -3.401492690184, `2.5%` = 0.527275445018903, `2.5%` = -1.78375367793506, `2.5%` = 1.32770940293854, `2.5%` = -1.89529361773991, `2.5%` = -1.9106750132439, 
+`2.5%` = -2.02803126368439, `2.5%` = -2.47632399275718, `2.5%` = -1.86122689687105, `2.5%` = -2.00542308533653)
+c(`97.5%` = 1.67969744861974, `97.5%` = 0.935428242477581, `97.5%` = 1.6348058321464, `97.5%` = 1.81710270358056, `97.5%` = 1.63450278223622, `97.5%` = 1.88045146010562, `97.5%` = 0.676762640081955, `97.5%` = 1.78906314682073, `97.5%` = 0.850790497217344, `97.5%` = 1.47057766493624, `97.5%` = 0.39318191253476, `97.5%` = 1.88452418451776, `97.5%` = 0.199302958070319, `97.5%` = 2.69049448523487, `97.5%` = -0.71625139500421, `97.5%` = 4.8161142366925, `97.5%` = 1.90068098208742, `97.5%` = 1.88773849773307, 
+`97.5%` = 1.47639661532028, `97.5%` = 0.803673159441113, `97.5%` = 1.86049650391514, `97.5%` = 1.71917016589104)
+c(`5%` = -1.66549179341294, `5%` = -2.05805403972291, `5%` = -1.66084826903427, `5%` = -1.50609037865979, `5%` = -1.62666028114303, `5%` = -1.53803827380169, `5%` = -1.374497411387, `5%` = -1.50464058833537, `5%` = -1.07057709615347, `5%` = -1.79986867754153, `5%` = -1.10318164735297, `5%` = -1.56027911708145, `5%` = -3.03473408528801, `5%` = 0.677198726234082, `5%` = -1.69975002339859, `5%` = 1.56455100307583, `5%` = -1.54500435851042, `5%` = -1.54650472228178, `5%` = -1.71550508080056, `5%` = -2.16966249843568, 
+`5%` = -1.51253234614188, `5%` = -1.68286480200484)
+c(`95%` = 1.38012867025627, `95%` = 0.661680376811838, `95%` = 1.31572988103649, `95%` = 1.49439855925108, `95%` = 1.31428822112951, `95%` = 1.54140325707139, `95%` = 0.491906288928273, `95%` = 1.47557433066007, `95%` = 0.6825129130599, `95%` = 1.15276028747074, `95%` = 0.262600483963485, `95%` = 1.54587386125945, `95%` = -0.0370570732959479, `95%` = 2.46984046267875, `95%` = -0.79680171298815, `95%` = 4.4435402069247, `95%` = 1.53642174870284, `95%` = 1.54093350572683, `95%` = 1.19508577316775, 
+`95%` = 0.585314360782336, `95%` = 1.5156111408874, `95%` = 1.37175745000343)

r-analysis/latex_output/DirectEffects/parameters_3_asinh(Competitors USPDC).tex

@@ -0,0 +1,10 @@
+c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.")
+c(-0.101703799949395, -0.564637906246216, -0.0956910914293675, -0.00337692444405644, -0.0816048428798888, -6.35740684684093e-05, -0.667659588573525, -0.00405812351552042, 1.42004536167462, -0.171009890912524, 0.683653688372939, 0.00344017290368172, 0.260625956246919, -1.41625310169202, 0.470951452129627, 0.208110428258286, 0.00250209506601534, -0.00562874748733102, -0.105857797157013, 0.344654754591335, 0.0027807037509892, -0.0552183959035103)
+c(`2.5%` = -1.51590135737519, `2.5%` = -1.842080521195, `2.5%` = -1.52784116663569, `2.5%` = -1.39142200782843, `2.5%` = -1.46567361056997, `2.5%` = -1.42253864934177, `2.5%` = -1.84852850564476, `2.5%` = -1.35828695365276, `2.5%` = 0.166228321710892, `2.5%` = -1.56024033377577, `2.5%` = -0.0715480521081878, `2.5%` = -1.39463661876756, `2.5%` = -1.00766752361942, `2.5%` = -2.60148475745005, `2.5%` = 0.0825563723532098, `2.5%` = -0.483919695463572, `2.5%` = -1.40349956302864, `2.5%` = -1.38403675822353, 
+`2.5%` = -1.53199462919355, `2.5%` = -0.81212639023518, `2.5%` = -1.34556223161126, `2.5%` = -1.46894825626173)
+c(`97.5%` = 1.23043134810689, `97.5%` = 0.516158819481392, `97.5%` = 1.27757713500863, `97.5%` = 1.34564557876177, `97.5%` = 1.25686631205631, `97.5%` = 1.4133810381728, `97.5%` = 0.333296222901717, `97.5%` = 1.3394057445905, `97.5%` = 3.03047315901632, `97.5%` = 1.09705630948954, `97.5%` = 1.47768415582758, `97.5%` = 1.42305354019587, `97.5%` = 1.69446395582928, `97.5%` = -0.457781574665431, `97.5%` = 0.869227382956849, `97.5%` = 0.860804399758541, `97.5%` = 1.39777584880611, `97.5%` = 1.32536002712745, 
+`97.5%` = 1.22054546483929, `97.5%` = 1.64628843970952, `97.5%` = 1.40043610000601, `97.5%` = 1.32705054827579)
+c(`5%` = -1.23554421440395, `5%` = -1.60865719854905, `5%` = -1.26990351126472, `5%` = -1.10623630944065, `5%` = -1.20092272626125, `5%` = -1.14277805533658, `5%` = -1.61987740240911, `5%` = -1.10114220997093, `5%` = 0.325020098610294, `5%` = -1.28688609021933, `5%` = 0.0437719361245331, `5%` = -1.13051681084733, `5%` = -0.774388773799267, `5%` = -2.39673285825223, `5%` = 0.140776593909285, `5%` = -0.358913993804725, `5%` = -1.12240343354847, `5%` = -1.13264764154031, `5%` = -1.25936194261107, `5%` = -0.619677989159712, 
+`5%` = -1.12306907889816, `5%` = -1.19574516476068)
+c(`95%` = 0.9962993915319, `95%` = 0.344271026201751, `95%` = 1.02585852524287, `95%` = 1.11395208896461, `95%` = 1.0103835099505, `95%` = 1.11961350598891, `95%` = 0.192677426246452, `95%` = 1.10063510284539, `95%` = 2.71868187996996, `95%` = 0.899896640181501, `95%` = 1.35108734959306, `95%` = 1.14893128770879, `95%` = 1.39113469911214, `95%` = -0.579922080756115, `95%` = 0.805026483916245, `95%` = 0.757665883111782, `95%` = 1.12853607517645, `95%` = 1.09611509546288, `95%` = 0.961250543645123, 
+`95%` = 1.41171899585957, `95%` = 1.10399402315835, `95%` = 1.04723154370974)

r-analysis/latex_output/DirectEffects/parameters_9_status_NYR.tex

@@ -0,0 +1,10 @@
+c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.")
+c(-0.000505821125346685, 0.045620786014229, -0.00525743090751, -0.00499600218282098, -0.000585600111380386, -0.00276881425069573, -0.13143407853664, -0.0036185203171912, 0.051697644596875, -0.0107891870271464, 0.32478426535094, -0.00145937361475847, 0.0136500560555857, -0.0738493576307788, -0.476611882301335, 0.0222560987441026, -0.00414548910371886, -0.0104742177218119, -0.00809846939246779, 0.0426395752164606, -0.0115019802718463, -0.00996274304864821)
+c(`2.5%` = -0.701450360691855, `2.5%` = -0.63003373124748, `2.5%` = -0.715622145882045, `2.5%` = -0.727044467579978, `2.5%` = -0.747738822953945, `2.5%` = -0.735270274057596, `2.5%` = -0.907802053311157, `2.5%` = -0.733860049506474, `2.5%` = -0.622045682166618, `2.5%` = -0.742120219363362, `2.5%` = -0.243402705068146, `2.5%` = -0.735852738037434, `2.5%` = -0.666840078342412, `2.5%` = -0.798071774552942, `2.5%` = -1.42904260420137, `2.5%` = -0.64696023851359, `2.5%` = -0.746859618917552, `2.5%` = -0.739101938071361, 
+`2.5%` = -0.730727303837492, `2.5%` = -0.626446320243322, `2.5%` = -0.750902816931559, `2.5%` = -0.743675008324717)
+c(`97.5%` = 0.732116835481222, `97.5%` = 0.789641535896855, `97.5%` = 0.73726328358774, `97.5%` = 0.702602048400459, `97.5%` = 0.722592110325922, `97.5%` = 0.745772208274965, `97.5%` = 0.469638387064318, `97.5%` = 0.740059158395697, `97.5%` = 0.802350604222116, `97.5%` = 0.729903013043148, `97.5%` = 1.23628887068288, `97.5%` = 0.735322757301528, `97.5%` = 0.721254548490607, `97.5%` = 0.547821159342026, `97.5%` = 0.0901610144027345, `97.5%` = 0.724174566356542, `97.5%` = 0.75034324181327, `97.5%` = 0.709982748482133, 
+`97.5%` = 0.719605886783329, `97.5%` = 0.774791684477986, `97.5%` = 0.735072184820506, `97.5%` = 0.723815080592094)
+c(`5%` = -0.558975272641589, `5%` = -0.481208746850505, `5%` = -0.558122801520996, `5%` = -0.566607488065583, `5%` = -0.571443956067767, `5%` = -0.560863708568709, `5%` = -0.733995288939712, `5%` = -0.573157666695002, `5%` = -0.481699496200734, `5%` = -0.584184368190383, `5%` = -0.164285973529124, `5%` = -0.561811722478321, `5%` = -0.511640963000957, `5%` = -0.649098498864267, `5%` = -1.24192236504004, `5%` = -0.514000899450722, `5%` = -0.573055024907531, `5%` = -0.589897724557502, `5%` = -0.576009342907356, 
+`5%` = -0.483515522092064, `5%` = -0.59377439249432, `5%` = -0.589781985563434)
+c(`95%` = 0.558241074532826, `95%` = 0.6343107609265, `95%` = 0.564064518165769, `95%` = 0.554270165096566, `95%` = 0.568906077580551, `95%` = 0.564421378888805, `95%` = 0.361698139473533, `95%` = 0.556521770858677, `95%` = 0.636739588170121, `95%` = 0.554481744418514, `95%` = 1.05804976340078, `95%` = 0.564219400738208, `95%` = 0.570750639989039, `95%` = 0.424983622057134, `95%` = 0.0339253948301201, `95%` = 0.576987914910334, `95%` = 0.588745135323456, `95%` = 0.565535664200477, `95%` = 0.572292114386171, 
+`95%` = 0.614652923649533, `95%` = 0.564155780832837, `95%` = 0.568721991063375)

r-analysis/latex_output/DirectEffects/parameters_Elapsed Duration.tex

@@ -0,0 +1,10 @@
+c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.")
+c(-0.0338934915059191, -0.0424617202550975, -0.0299850538975256, -0.0228882519097089, -0.0337281404548882, -0.0258485686453499, -0.0402405594871021, -0.0253886763112486, -0.136200903561077, -0.0212623461765324, -0.102097548934776, -0.0229996389940294, -0.106049590275806, 0.103092606051222, -0.431577879697863, -0.0589099972525265, -0.0270810088184279, -0.0260112735850109, -0.0325654806803472, -0.182061588936881, -0.0261135283573324, -0.0227456753432774)
+c(`2.5%` = -0.690100665844187, `2.5%` = -0.671202369164881, `2.5%` = -0.666088312766176, `2.5%` = -0.666578142141343, `2.5%` = -0.678544709698103, `2.5%` = -0.669196847024182, `2.5%` = -0.655923003454131, `2.5%` = -0.655413313800275, `2.5%` = -0.845732099866594, `2.5%` = -0.653192374517221, `2.5%` = -0.64122257390992, `2.5%` = -0.659159394086157, `2.5%` = -0.787764109870441, `2.5%` = -0.425148647750428, `2.5%` = -1.00072959307916, `2.5%` = -0.69270592355035, `2.5%` = -0.685883418294076, `2.5%` = -0.679342853679393, 
+`2.5%` = -0.681272654592773, `2.5%` = -0.837786199816108, `2.5%` = -0.665616824031369, `2.5%` = -0.6589068176532)
+c(`97.5%` = 0.607853806397433, `97.5%` = 0.575243002929989, `97.5%` = 0.623466621187216, `97.5%` = 0.641896362473495, `97.5%` = 0.611672327879679, `97.5%` = 0.621633709372348, `97.5%` = 0.556891230159617, `97.5%` = 0.626653736033388, `97.5%` = 0.426452209102124, `97.5%` = 0.604814400002199, `97.5%` = 0.370375731808055, `97.5%` = 0.656507843091209, `97.5%` = 0.463728458888126, `97.5%` = 0.803945161990884, `97.5%` = 0.00182348352747238, `97.5%` = 0.534212402242803, `97.5%` = 0.609945240239135, `97.5%` = 0.637145158270836, 
+`97.5%` = 0.595960590167125, `97.5%` = 0.325103240011011, `97.5%` = 0.639977001719915, `97.5%` = 0.645740444211412)
+c(`5%` = -0.543756319725243, `5%` = -0.53599965672411, `5%` = -0.53861642350383, `5%` = -0.536297908680718, `5%` = -0.535626704345617, `5%` = -0.534014891414532, `5%` = -0.515645442393892, `5%` = -0.521060513099755, `5%` = -0.698732119314075, `5%` = -0.513828973026094, `5%` = -0.536163536290694, `5%` = -0.520608887905471, `5%` = -0.637108697299896, `5%` = -0.333645087620771, `5%` = -0.901047800865227, `5%` = -0.558977042638121, `5%` = -0.528351288300235, `5%` = -0.531154576765547, `5%` = -0.535401625627599, 
+`5%` = -0.704974120731683, `5%` = -0.530102478693174, `5%` = -0.519892009691237)
+c(`95%` = 0.476219882900217, `95%` = 0.43869836398252, `95%` = 0.487869109380049, `95%` = 0.495857205487324, `95%` = 0.477771545962831, `95%` = 0.479824548055006, `95%` = 0.431579111164995, `95%` = 0.489104423628694, `95%` = 0.318624951651975, `95%` = 0.475052338519103, `95%` = 0.285016338923033, `95%` = 0.498321179113614, `95%` = 0.356019293245049, `95%` = 0.645249978382246, `95%` = -0.0484915873069474, `95%` = 0.422234919504667, `95%` = 0.474969816313099, `95%` = 0.495469548426488, `95%` = 0.459482254828791, 
+`95%` = 0.237533499815169, `95%` = 0.491700443982918, `95%` = 0.504132013362862)

r-analysis/latex_output/DirectEffects/parameters_asinh(Competitors USPDC).tex

@@ -0,0 +1,10 @@
+c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.")
+c(-0.101589582550983, -0.55509837146517, -0.0876297898120394, -0.00424167706294526, -0.0856815547958816, -0.000891733412756743, -0.656064432872784, -0.00724344466650777, 1.39799100967958, -0.162272605765493, 0.674878170950668, 0.00679859198995776, 0.256393632879033, -1.39692218698997, 0.469486930453339, 0.201756832022176, -0.000743046330130356, -0.00239703676867723, -0.101293376110023, 0.334667664170298, 0.0036268316699096, -0.0553940979488865)
+c(`2.5%` = -1.50442203323384, `2.5%` = -1.84877532048521, `2.5%` = -1.4700191365573, `2.5%` = -1.37500277258178, `2.5%` = -1.4848837602478, `2.5%` = -1.32756197431566, `2.5%` = -1.85894539233901, `2.5%` = -1.33085969325607, `2.5%` = 0.16999404397094, `2.5%` = -1.53594319207073, `2.5%` = -0.0683721763680483, `2.5%` = -1.33523195430546, `2.5%` = -0.987723094632562, `2.5%` = -2.58976230693659, `2.5%` = 0.0834766521350615, `2.5%` = -0.475822349223494, `2.5%` = -1.33731236431054, `2.5%` = -1.35448275771273, 
+`2.5%` = -1.42832958185923, `2.5%` = -0.797988116140275, `2.5%` = -1.3168218907587, `2.5%` = -1.41268885278703)
+c(`97.5%` = 1.22021244556502, `97.5%` = 0.475219087766019, `97.5%` = 1.2205452457074, `97.5%` = 1.38725107457264, `97.5%` = 1.24763911953619, `97.5%` = 1.36828112117342, `97.5%` = 0.304797046306457, `97.5%` = 1.2809727123191, `97.5%` = 2.97850864225959, `97.5%` = 1.1200916681713, `97.5%` = 1.48172994699915, `97.5%` = 1.35752400157911, `97.5%` = 1.59974497777273, `97.5%` = -0.420429798817082, `97.5%` = 0.874242905367939, `97.5%` = 0.869912231337424, `97.5%` = 1.32550284173821, `97.5%` = 1.35888949832736, 
+`97.5%` = 1.1936539738932, `97.5%` = 1.609867105785, `97.5%` = 1.34949094435626, `97.5%` = 1.22944948085911)
+c(`5%` = -1.22562889252442, `5%` = -1.59673376165319, `5%` = -1.21885811476843, `5%` = -1.11813404961243, `5%` = -1.20918844446486, `5%` = -1.07055731145333, `5%` = -1.6236745264581, `5%` = -1.07506861425794, `5%` = 0.318380069304849, `5%` = -1.29675430841515, `5%` = 0.041344205634408, `5%` = -1.10591252208745, `5%` = -0.740508923444193, `5%` = -2.36760582705321, `5%` = 0.147263440981433, `5%` = -0.359590775683247, `5%` = -1.09935756841891, `5%` = -1.10179682888056, `5%` = -1.19332341912876, `5%` = -0.607636638304879, 
+`5%` = -1.08485266813522, `5%` = -1.13445756592737)
+c(`95%` = 0.972806121648346, `95%` = 0.338841318556515, `95%` = 0.988158619006595, `95%` = 1.09780676333781, `95%` = 1.00794578824477, `95%` = 1.1020724405486, `95%` = 0.184033329014398, `95%` = 1.04560505760623, `95%` = 2.69222620971344, `95%` = 0.884777865331596, `95%` = 1.35166837167041, `95%` = 1.11527199108615, `95%` = 1.36231797243719, `95%` = -0.54782380504844, `95%` = 0.80963853468214, `95%` = 0.767827618485196, `95%` = 1.08512970421361, `95%` = 1.08918975608724, `95%` = 0.953418962916104, 
+`95%` = 1.37967304828955, `95%` = 1.09273387336662, `95%` = 1.00375347161432)

r-analysis/latex_output/DirectEffects/parameters_asinh(Generic Brands).tex

@@ -0,0 +1,10 @@
+c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.")
+c(-0.107172841753111, -0.652217546171737, -0.158153468999919, -0.00872754000664511, -0.152469836074415, -0.00511048990810721, -0.432756950030333, -0.0176343397968463, -0.174941414466581, -0.312199818256478, -0.421462561969054, -0.00543187548165017, -1.41347612539297, 1.52465722253264, -1.24155955461637, 2.9333026125354, -0.0260562265370799, -0.00708575237043839, -0.236014521608801, -0.708583530845461, -0.00537911254785814, -0.115835951645073)
+c(`2.5%` = -1.98999201263705, `2.5%` = -2.39214090710107, `2.5%` = -2.01846766579584, `2.5%` = -1.8757515797304, `2.5%` = -2.0334114088637, `2.5%` = -1.85212654123702, `2.5%` = -1.60516533489668, `2.5%` = -1.81993228600129, `2.5%` = -1.25821009021598, `2.5%` = -2.16646127648176, `2.5%` = -1.25120863703686, `2.5%` = -1.88684117832516, `2.5%` = -3.34866909602091, `2.5%` = 0.47643422496699, `2.5%` = -1.7911797031523, `2.5%` = 1.31489061844565, `2.5%` = -1.85712798081873, `2.5%` = -1.91898409412556, 
+`2.5%` = -2.07179758391882, `2.5%` = -2.45975286581684, `2.5%` = -1.8878079254812, `2.5%` = -2.01296251650662)
+c(`97.5%` = 1.7025058387162, `97.5%` = 0.881408586766731, `97.5%` = 1.65331806761982, `97.5%` = 1.81774079148411, `97.5%` = 1.66674910435877, `97.5%` = 1.86647987416102, `97.5%` = 0.645259664512326, `97.5%` = 1.80599008456882, `97.5%` = 0.856678839564855, `97.5%` = 1.41839221738041, `97.5%` = 0.360026899921426, `97.5%` = 1.89267030844417, `97.5%` = 0.201003318675158, `97.5%` = 2.69056887105094, `97.5%` = -0.715192981105914, `97.5%` = 4.78775173338893, `97.5%` = 1.83490876984593, `97.5%` = 1.88931228937232, 
+`97.5%` = 1.5091000822795, `97.5%` = 0.802989438020258, `97.5%` = 1.86093048637463, `97.5%` = 1.70786218772968)
+c(`5%` = -1.62973704571072, `5%` = -2.07294410260449, `5%` = -1.69801214255266, `5%` = -1.53184108821626, `5%` = -1.68584731697327, `5%` = -1.54481245603677, `5%` = -1.39759449940044, `5%` = -1.49900355048193, `5%` = -1.06880137117197, `5%` = -1.83763159160532, `5%` = -1.11103379908195, `5%` = -1.53234039460221, `5%` = -3.00363339456024, `5%` = 0.638951221037764, `5%` = -1.70700005124097, `5%` = 1.54171189173018, `5%` = -1.54285125721537, `5%` = -1.58329512208634, `5%` = -1.76844032673346, `5%` = -2.13894081007506, 
+`5%` = -1.54263378199832, `5%` = -1.67735076532346)
+c(`95%` = 1.41879257978458, `95%` = 0.648571235197482, `95%` = 1.33700306317695, `95%` = 1.49389338436903, `95%` = 1.3461526982071, `95%` = 1.50194445717269, `95%` = 0.481912048276356, `95%` = 1.48993841444006, `95%` = 0.70167186942968, `95%` = 1.12285471327414, `95%` = 0.252246900593004, `95%` = 1.55595453136487, `95%` = -0.0371324515642565, `95%` = 2.48090816148459, `95%` = -0.797626914298678, `95%` = 4.47581369319019, `95%` = 1.48349835052459, `95%` = 1.55512904554564, `95%` = 1.23036073907808, 
+`95%` = 0.570893677970625, `95%` = 1.51891783905047, `95%` = 1.38398983193578)

r-analysis/latex_output/DirectEffects/parameters_status_ANR.tex

@@ -0,0 +1,10 @@
+c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.")
+c(-0.0379222039996637, -0.00951521433050643, -0.0357669209565548, -0.0289858802339238, -0.0384045782213899, -0.0354852935888951, -0.195806801668196, -0.0319728051993748, -0.064502474892061, -0.0359000714018003, -0.209385718567549, -0.0293405266823335, -0.166256613851307, -0.204154479367649, -0.343021435135372, 0.0429675640807533, -0.031540048708067, -0.0346985641986963, -0.0303375281259717, -0.13391213882268, -0.0315874427691754, -0.0292508480128129)
+c(`2.5%` = -0.670177498740691, `2.5%` = -0.606574716528012, `2.5%` = -0.660124076867341, `2.5%` = -0.658500223161749, `2.5%` = -0.673266724039317, `2.5%` = -0.693646849412836, `2.5%` = -0.917059388708302, `2.5%` = -0.662394912651839, `2.5%` = -0.67991481986874, `2.5%` = -0.670316698080103, `2.5%` = -0.83183723815622, `2.5%` = -0.663476379211969, `2.5%` = -0.854735257874132, `2.5%` = -0.918576287776926, `2.5%` = -0.963532341357661, `2.5%` = -0.495917243186253, `2.5%` = -0.64751983879636, `2.5%` = -0.673951139603439, 
+`2.5%` = -0.665804518054749, `2.5%` = -0.791664538636436, `2.5%` = -0.643761100670645, `2.5%` = -0.680737220370752)
+c(`97.5%` = 0.579428469021833, `97.5%` = 0.640138775494956, `97.5%` = 0.596180548733038, `97.5%` = 0.619030332938483, `97.5%` = 0.597678927212641, `97.5%` = 0.6121441149317, `97.5%` = 0.323295322658126, `97.5%` = 0.590730699845894, `97.5%` = 0.503098249043251, `97.5%` = 0.607349406190736, `97.5%` = 0.228895375416542, `97.5%` = 0.624129832779025, `97.5%` = 0.342536899260382, `97.5%` = 0.307896298234294, `97.5%` = 0.0903633166835597, `97.5%` = 0.686527591660775, `97.5%` = 0.616181981700487, `97.5%` = 0.599647736473311, 
+`97.5%` = 0.609123918623858, `97.5%` = 0.415337198000103, `97.5%` = 0.597111489383847, `97.5%` = 0.632428409170173)
+c(`5%` = -0.530855657109975, `5%` = -0.487829039222839, `5%` = -0.530023192811465, `5%` = -0.516610981668542, `5%` = -0.524022832574353, `5%` = -0.537218989651104, `5%` = -0.75728605285178, `5%` = -0.517067859106373, `5%` = -0.547235882003818, `5%` = -0.536626649173697, `5%` = -0.691946442016388, `5%` = -0.52266339334223, `5%` = -0.70021531860572, `5%` = -0.769560225018392, `5%` = -0.844315621869912, `5%` = -0.393051567766974, `5%` = -0.519010088416499, `5%` = -0.529941857869653, `5%` = -0.52218968602785, 
+`5%` = -0.648665425674694, `5%` = -0.520022794035758, `5%` = -0.521893002755623)
+c(`95%` = 0.449851048402911, `95%` = 0.496036928523184, `95%` = 0.452249441742121, `95%` = 0.47353271148754, `95%` = 0.454890426424996, `95%` = 0.474673790264394, `95%` = 0.22238953047732, `95%` = 0.44333608430914, `95%` = 0.387054472484903, `95%` = 0.475167927437824, `95%` = 0.156987753663085, `95%` = 0.473536190521047, `95%` = 0.254381363435638, `95%` = 0.217911529700368, `95%` = 0.0348859178585696, `95%` = 0.552104217589191, `95%` = 0.462090261872998, `95%` = 0.456310261272674, `95%` = 0.474214855342067, 
+`95%` = 0.307884811241422, `95%` = 0.465192052817642, `95%` = 0.472198045319103)

r-analysis/latex_output/DirectEffects/parameters_status_EBI.tex

@@ -0,0 +1,10 @@
+c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.")
+c(-0.00486381271611535, -0.00234177649674774, -0.00515183815786496, -0.00421305648644563, -0.00389136209748, -0.00610673537139904, -0.0126249322314229, -0.00563946285209699, -0.00713553939645088, -0.00556109229654605, -0.0296681294218427, -0.00302065971753859, -0.0160603020475268, -0.0033204482574487, -0.00366894940571103, -0.0033782308159864, -0.00147263938060779, -0.00281902338049175, -0.00516465945474794, 0.00117504910800777, -0.00536819164938224, -0.00341714311862774)
+c(`2.5%` = -0.4928902240148, `2.5%` = -0.471597568252128, `2.5%` = -0.507060592786285, `2.5%` = -0.496187329730551, `2.5%` = -0.493846364631587, `2.5%` = -0.512035468722232, `2.5%` = -0.503922991423901, `2.5%` = -0.499136678960227, `2.5%` = -0.498255016423811, `2.5%` = -0.479550877030934, `2.5%` = -0.527710057117981, `2.5%` = -0.47738578213782, `2.5%` = -0.504170004794057, `2.5%` = -0.488027036715767, `2.5%` = -0.508311086739842, `2.5%` = -0.488325893335097, `2.5%` = -0.500350365353871, `2.5%` = -0.489797593321065, 
+`2.5%` = -0.493614948527939, `2.5%` = -0.478099940085293, `2.5%` = -0.480388926446991, `2.5%` = -0.480339298649014)
+c(`97.5%` = 0.477181388655603, `97.5%` = 0.478253799598711, `97.5%` = 0.483897884400919, `97.5%` = 0.477733634248101, `97.5%` = 0.480671053527334, `97.5%` = 0.483026873590512, `97.5%` = 0.455687812451121, `97.5%` = 0.488335048561587, `97.5%` = 0.476215783846475, `97.5%` = 0.466429687492193, `97.5%` = 0.423734438029339, `97.5%` = 0.474465088221571, `97.5%` = 0.458565492328125, `97.5%` = 0.493372345702204, `97.5%` = 0.494169616877378, `97.5%` = 0.483648553653476, `97.5%` = 0.505031717406501, `97.5%` = 0.46569700208092, 
+`97.5%` = 0.471415999038097, `97.5%` = 0.49621435196537, `97.5%` = 0.476743752937605, `97.5%` = 0.467403287438688)
+c(`5%` = -0.384839256524113, `5%` = -0.370890653819468, `5%` = -0.389717242753722, `5%` = -0.382138541945515, `5%` = -0.371384438942403, `5%` = -0.390763667720738, `5%` = -0.387699039445137, `5%` = -0.394477291159571, `5%` = -0.39220012720177, `5%` = -0.380637632210929, `5%` = -0.411827047834269, `5%` = -0.374609901237044, `5%` = -0.39083922063065, `5%` = -0.387483535903527, `5%` = -0.390162763770864, `5%` = -0.37365571697494, `5%` = -0.381743220627302, `5%` = -0.369783694172149, `5%` = -0.385379188161294, 
+`5%` = -0.373496126525493, `5%` = -0.377291769622258, `5%` = -0.368630128560942)
+c(`95%` = 0.363815583651208, `95%` = 0.370227548106358, `95%` = 0.373524054930431, `95%` = 0.369698098361814, `95%` = 0.369300634866769, `95%` = 0.36794145288606, `95%` = 0.358137811206345, `95%` = 0.368909612747601, `95%` = 0.364170778673988, `95%` = 0.365633719092603, `95%` = 0.33048200014893, `95%` = 0.363053774138134, `95%` = 0.347229606528999, `95%` = 0.382089305066404, `95%` = 0.387514695717931, `95%` = 0.375728674172833, `95%` = 0.385485941672613, `95%` = 0.361659448576342, `95%` = 0.368353921113658, 
+`95%` = 0.388511185198981, `95%` = 0.369886822399706, `95%` = 0.365593471311159)

r-analysis/latex_output/DirectEffects/parameters_status_NYR.tex

@@ -0,0 +1,10 @@
+c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.")
+c(-0.00832576787627327, 0.0502181175509121, -0.00254797568859221, 0.00037391254389325, -0.00114134588173891, -0.00451497853572214, -0.142153374440972, -0.00195287948710614, 0.0654719447924103, -0.00677887728466109, 0.345662047693858, -0.00157110819962499, 0.0149688400470326, -0.0782677724551585, -0.49791727768667, 0.0367700783674713, -0.00716063945233656, -0.00176465495021884, -0.00435528924328807, 0.043863405759711, -0.00548445340847891, -0.0100421324092617)
+c(`2.5%` = -0.798176858708199, `2.5%` = -0.649287090985632, `2.5%` = -0.752180190601171, `2.5%` = -0.765720288201341, `2.5%` = -0.761310269145469, `2.5%` = -0.769404872006371, `2.5%` = -0.962752437012212, `2.5%` = -0.768468256092748, `2.5%` = -0.613672917773224, `2.5%` = -0.741396156335141, `2.5%` = -0.240225670397011, `2.5%` = -0.778550541201842, `2.5%` = -0.671882552677685, `2.5%` = -0.829702544391712, `2.5%` = -1.47884841599921, `2.5%` = -0.653709569493313, `2.5%` = -0.752391272912992, `2.5%` = -0.725938326026952, 
+`2.5%` = -0.785753969844037, `2.5%` = -0.654434721761801, `2.5%` = -0.772385114493806, `2.5%` = -0.781547218625338)
+c(`97.5%` = 0.76034469875563, `97.5%` = 0.845633947832787, `97.5%` = 0.770407348684183, `97.5%` = 0.814474757996063, `97.5%` = 0.781292873345278, `97.5%` = 0.779826237909694, `97.5%` = 0.494695385934057, `97.5%` = 0.766089116079086, `97.5%` = 0.858482468878284, `97.5%` = 0.747665756417337, `97.5%` = 1.31119870717426, `97.5%` = 0.791026840863526, `97.5%` = 0.72899311565112, `97.5%` = 0.558765218546179, `97.5%` = 0.0976524892433262, `97.5%` = 0.781833224643643, `97.5%` = 0.741470213490854, `97.5%` = 0.780312796382334, 
+`97.5%` = 0.776445672109217, `97.5%` = 0.835951227522058, `97.5%` = 0.75578833553135, `97.5%` = 0.750101827954997)
+c(`5%` = -0.616116918100389, `5%` = -0.512051758484746, `5%` = -0.589817478032612, `5%` = -0.60546319491298, `5%` = -0.583839532437199, `5%` = -0.599673916128779, `5%` = -0.771236336079563, `5%` = -0.595759768355798, `5%` = -0.47175814689012, `5%` = -0.585717774179175, `5%` = -0.156763845257426, `5%` = -0.590593736012543, `5%` = -0.529938547912805, `5%` = -0.669829197453793, `5%` = -1.26963007145184, `5%` = -0.508859660216102, `5%` = -0.602929326426733, `5%` = -0.573320601674407, `5%` = -0.611359700135997, 
+`5%` = -0.497215940632518, `5%` = -0.609562003721472, `5%` = -0.603191340531708)
+c(`95%` = 0.597088301775022, `95%` = 0.672111603595297, `95%` = 0.597359235347557, `95%` = 0.61918916388514, `95%` = 0.595716780090434, `95%` = 0.597426850327021, `95%` = 0.368655084080813, `95%` = 0.592458408903632, `95%` = 0.689843781266554, `95%` = 0.577350192007013, `95%` = 1.10666629752813, `95%` = 0.608146006908894, `95%` = 0.567469287801582, `95%` = 0.431936020918889, `95%` = 0.0306194413604995, `95%` = 0.619341630421946, `95%` = 0.574033327341637, `95%` = 0.591738000435572, `95%` = 0.598312121323889, 
+`95%` = 0.645311247919002, `95%` = 0.587439430679619, `95%` = 0.583648949072352)

r-analysis/latex_output/DirectEffects/parameters_status_Rec.tex

@@ -0,0 +1,10 @@
+c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.")
+c(0.00150784514582254, -0.0497907368054486, 0.00434380644445338, 0.00218718976679278, -0.00434291403887011, 0.00302001293551759, 0.20336581169622, 0.00343296396767198, -0.0385920705103708, -0.00141334075132648, 0.0207373662562078, 0.0015000850140683, 0.0801600329536096, 0.113121841705728, -0.136274450655838, -0.0938571304025214, 0.00282123851288503, -0.00146833112673362, -0.00082584116000937, 0.0483281653119099, 0.00659062980994563, -0.00055535206472268)
+c(`2.5%` = -0.550531966199477, `2.5%` = -0.629394891363134, `2.5%` = -0.527672919300448, `2.5%` = -0.509227038923741, `2.5%` = -0.550868934343651, `2.5%` = -0.542855202488396, `2.5%` = -0.225399121304215, `2.5%` = -0.534213624588921, `2.5%` = -0.57231251252735, `2.5%` = -0.552100213849103, `2.5%` = -0.419418356555291, `2.5%` = -0.567178469443596, `2.5%` = -0.370748780309252, `2.5%` = -0.318655190350014, `2.5%` = -0.614817809004343, `2.5%` = -0.703951782706409, `2.5%` = -0.524221538466178, `2.5%` = -0.541042128309509, 
+`2.5%` = -0.553979099390109, `2.5%` = -0.429137673995, `2.5%` = -0.534659084619921, `2.5%` = -0.53134970116874)
+c(`97.5%` = 0.566388836665101, `97.5%` = 0.445760030666272, `97.5%` = 0.550712495508801, `97.5%` = 0.510548679450175, `97.5%` = 0.543799039346651, `97.5%` = 0.546171328528686, `97.5%` = 0.932547573554047, `97.5%` = 0.562478415721886, `97.5%` = 0.429713264446548, `97.5%` = 0.509501648868077, `97.5%` = 0.482434252392398, `97.5%` = 0.552388649630916, `97.5%` = 0.655194288743027, `97.5%` = 0.700748091326741, `97.5%` = 0.205549966757929, `97.5%` = 0.348679606266848, `97.5%` = 0.523973004291571, `97.5%` = 0.52237026080622, 
+`97.5%` = 0.551330298928471, `97.5%` = 0.607445504451599, `97.5%` = 0.557102597425807, `97.5%` = 0.509379389425598)
+c(`5%` = -0.429366635609993, `5%` = -0.496218717595005, `5%` = -0.411147660889218, `5%` = -0.398570977236643, `5%` = -0.422007458084124, `5%` = -0.412200344995762, `5%` = -0.160763338289191, `5%` = -0.423552637832629, `5%` = -0.458333301038196, `5%` = -0.418097069598191, `5%` = -0.324235933898656, `5%` = -0.417261044253087, `5%` = -0.281885257093864, `5%` = -0.234540125743699, `5%` = -0.508687221040413, `5%` = -0.56179500309088, `5%` = -0.411556667615538, `5%` = -0.418938395029271, `5%` = -0.433526826335861, 
+`5%` = -0.33703866498282, `5%` = -0.415046863367997, `5%` = -0.417110901717416)
+c(`95%` = 0.433864705383823, `95%` = 0.34493920495178, `95%` = 0.421270429380312, `95%` = 0.402340857877479, `95%` = 0.396223107432888, `95%` = 0.417603923761695, `95%` = 0.749712378119009, `95%` = 0.423198808398514, `95%` = 0.339524038845502, `95%` = 0.410767571164308, `95%` = 0.384778572752778, `95%` = 0.427718825229051, `95%` = 0.522116792789822, `95%` = 0.572014784561829, `95%` = 0.15192983211524, `95%` = 0.262927135744447, `95%` = 0.408590937183379, `95%` = 0.414061379519885, `95%` = 0.424759632515887, 
+`95%` = 0.478613866408021, `95%` = 0.43229047773505, `95%` = 0.400833024334571)