Merge branch 'lognorm_sigma_prior'

Merge branch 'exploring_fixed_effects'
reran analysis to get up to date stuff
88 changed files with 11878 additions and 7475 deletions
--- a/ClinicalTrialsEstimation.Rproj
+++ b/ClinicalTrialsEstimation.Rproj
@ -0,0 +1,13 @@
 Version: 1.0
 RestoreWorkspace: Default
 SaveWorkspace: Default
 AlwaysSaveHistory: Default
 EnableCodeIndexing: Yes
 UseSpacesForTab: Yes
 NumSpacesForTab: 2
 Encoding: UTF-8
 RnwWeave: Sweave
 LaTeX: pdfLaTeX
--- a/models/Logistic_WorkingParamRecovery.jl
+++ b/models/Logistic_WorkingParamRecovery.jl
--- a/models/Model.jl
+++ b/models/Model.jl
@ -1,62 +0,0 @@
 using Turing, StatsFuns
 using Distributions, StatsPlots
 using FillArrays
 begin #creating synthetic data
 	βs = [1 2 3]'
 	x = Matrix([1:10 1:2:20 1:3:30])
 	X = (x .- mean(x,dims=1)) ./ std(x,dims=1)
 	t = [x for x=1:0.5:5.5]
 	s = rand([1,2],10)
 	σ= [2 3]
 	rand_draw1 = randn(10)
 	y = x*βs .+ σ[s]
 end
@model function JointDurationStateModel(
 	DeviationFromExpectedDuration,
 	ConclusionStatus,
 	SnapshotState,
 	CurrentDuration,
 )
 	# get dimensions
 	n,k = size(SnapshotState)
 	#hyperpriors priors
 	#Heirarchal parameters
 	#β ~ MvNormal(Fill(0,k),2)
 	η ~ MvNormal(Fill(0,k),2)
 	#Direct Priors
 	#σ_DFED ~ filldist(Exponential(1),2) #TODO: check implication of this form
 	#model
 	#μ = SnapshotState * β
 	p = StatsFuns.logistic.(SnapshotState * η)
 	#estimate ConclusionStatus model
 	ConclusionStatus .~ Bernoulli(p)
 	#Estimate DFED model
 	#=
 	for i in eachindex(ConclusionStatus)
 		DeviationFromExpectedDuration ~ Normal(
 			μ[ConclusionStatus[i]],
 			σ_DFED[ConclusionStatus[i]]
 		)
 	end
 	=#
 end
 model = JointDurationStateModel(y,s,X,t)
 prior = JointDurationStateModel(fill(missing,size(y)),fill(missing,size(s)),X,t)
 chain = sample(model,NUTS(0.85),2000)
 plot(chain)
--- a/models/MultivariateExample.jl
+++ b/models/MultivariateExample.jl
--- a/models/PlutoModel.jl
+++ b/models/PlutoModel.jl
--- a/r-analysis/EffectsOfEnrollmentDelay.qmd
+++ b/r-analysis/EffectsOfEnrollmentDelay.qmd
--- a/r-analysis/EffectsOfEnrollmentDelay.rmarkdown
+++ b/r-analysis/EffectsOfEnrollmentDelay.rmarkdown
--- a/r-analysis/EffectsOfMarketConditions.qmd
+++ b/r-analysis/EffectsOfMarketConditions.qmd
--- a/r-analysis/EffectsOfMarketConditions_no_enrollment_status.qmd
+++ b/r-analysis/EffectsOfMarketConditions_no_enrollment_status.qmd
@ -1,783 +0,0 @@
 ---
 title: "The Effects of market conditions on recruitment and 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)
 #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))
 }
 d <- get_data(driver)
 df <- d$data
 n_categories <- d$ncat
 ################ 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$l_sdi_val)
 #interaction terms for competitors
 x["ib*elapsed"] <- x["elapsed_duration"]*x["identical_brands"]
 x["bnc*elapsed"] <- x["elapsed_duration"] * x["brand_name_counts"]
 y <- ifelse(df["final_status"]=="Terminated",1,0)
 return(list(x=x,y=y))
 }
 train <- data_formatter(df)
 categories <- df$category_id
 x <- train$x
 y <- train$y
 ```
 ### Intervention: Adding a single competitor
 ```{r}
 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_ANR"
    ,"ib*elapsed"
    ,"bnc*elapsed"
 )
 ```
 #### Generics
 ```{r}
 #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$elapsed_duration
 ```
 ```{r}
 counterfact_marketing_ib <- list(
    D = ncol(x),#
    N = nrow(x),
    L = n_categories$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),
    llx = as.vector(categories),
    counterfact_x_tilde = as.matrix(brand_intervention_ib),
    counterfact_x = as.matrix(x)
 )
 ```
 ```{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["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$elapsed_duration
 ```
 ```{r}
 counterfact_marketing_bnc <- list(
    D = ncol(x),#
    N = nrow(x),
    L = n_categories$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),
    llx = as.vector(categories),
    counterfact_x_tilde = as.matrix(brand_intervention_bnc),
    counterfact_x = as.matrix(x)
 )
 ```
 ```{r}
 generated_bnc <- gqs(
    fit@stanmodel,
    data=counterfact_marketing_bnc,
    draws=as.matrix(fit),
    seed=11021585
    )
 ```
 # Fit Model
 ```{r}
 ################################# FIT MODEL #########################################
 fit <- stan(
    file='Hierarchal_Logistic.stan', 
    data = counterfact_marketing_ib,
    chains = 4,
    iter = 5000,
    seed = 11021585
    )
 ```
 ```{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")$sigma_prior))
 ```
 ```{r}
 check_hmc_diagnostics(fit)
 hist(as.vector(extract(generated, pars="p_predicted")$p_predicted))
 ```
 # Diagnostics
 ```{r}
 #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}
 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}
 #other diagnostics
 logpost <- log_posterior(fit)
 nuts_prmts <- nuts_params(fit)
 posterior <- as.array(fit)
 ```
 ```{r}
 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)
        )
    )
 }
 ```
 ```{r}
 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)
        )
    )
 }
 ```
 ```{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)
        )
    )
 }}
 ```
 # 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.
 ```{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_ANR",
        #interactions for brands
        `12`="ib*elapsed",
        `13`="bnc*elapsed",
        # interactions for status
        `14`="sNYR*elapsed",
        `15`="sEBI*elapsed",
        `16`="sANR*elapsed"
    )
 )
 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$param_name,"]",sep="")
    return(grid)
 }
 group_mcmc_areas <- function(
        stem,# = "beta"
        class_list,# = beta_list
        stanfit,# = fit
        group_id,# = 2
        rename=TRUE
        ) {
    #get all parameter names
    params <- get_parameters(stem,class_list)
    #filter down to parameters of interest
    params <- filter(params,groups == group_id)
    #Get dataframe with only the rows of interest
    filtdata <- as.data.frame(stanfit)[params$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
    mcmc_areas(filtdata,prob = 0.8, prob_outer = 0.95) +
        ggtitle(paste("Parameter distributions for ICD-10 class:",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$parameters[parameter_id]
    #create area plot with appropriate title
    mcmc_areas(filtdata,prob = 0.8, prob_outer = 0.95) +
        ggtitle(parameter_name,"Parameter Distribution")
 }
 ```
 ```{r}
 #mcmc_intervals(fit, pars=get_parameters("beta",beta_list)$param_name)
 ```
 ### 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)
 p2 <- parameter_mcmc_areas("beta",beta_list,fit,2)
 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"
 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
 ```
 ```{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 
 ### Intervention: Adding a single competitor
 #### Generics
 ```{r}
 #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")$predicted_difference), bins=100)
 ```
 ```{r}
 pddf_ib <- data.frame(extract(generated_ib, pars="predicted_difference")$predicted_difference) |>
    pivot_longer(X1:X1343)
 #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$groups[i])
 ```
 ```{r}
 ggplot(pddf_ib, aes(x=value,)) +
    geom_histogram(bins=100) +
    labs(
        title = "Distribution of predicted differences"
        ,x = "Difference in probability due to intervention"
        ,y = "Predicted counts"
    ) + 
    #xlim(-0.3,0.1) +
    geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") 
 ggplot(pddf_ib, aes(x=value,)) +
    geom_histogram(bins=100) +
    facet_wrap(
        ~factor(
            category_name, 
            levels=beta_list$groups
            )
        , labeller = label_wrap_gen(multi_line = TRUE)
        , ncol=5) +
    labs(
        title = "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))
 ```
 #### USP DC
 ```{r}
 #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")$predicted_difference), bins=100)
 ```
 ```{r}
 pddf_bnc <- data.frame(extract(generated_bnc, pars="predicted_difference")$predicted_difference) |>
    pivot_longer(X1:X1343)
 #Add 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$earliest_date_observed[i]))
 ```
 ```{r}
 ggplot(pddf_bnc, aes(x=value,)) +
    geom_histogram(bins=100) +
    labs(
        title = "Distribution of predicted differences"
        ,x = "Difference in probability due to intervention"
        ,y = "Predicted counts"
    ) + 
    #xlim(-0.3,0.1) +
    geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") 
 ggplot(pddf_bnc, aes(x=value,)) +
    geom_histogram(bins=100) +
    facet_wrap(
        ~factor(
            category_name, 
            levels=beta_list$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))
 ```
--- a/r-analysis/FullAnalysis.R
+++ b/r-analysis/FullAnalysis.R
@ -1,239 +0,0 @@
 library(bayesplot)
 available_mcmc(pattern = "_nuts_")
 library(ggplot2)
 library(posterior)
 library(cmdstanr)
 library(rstan) # for stanfit
 #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)
 ################ Pull data from database ######################
 library(RPostgreSQL)
 driver <- dbDriver("PostgreSQL")
 con <- dbConnect(
    driver,
    user='root',
    password='root',
    dbname='aact_db',
    host='will-office'
    )
 query <- dbSendQuery(
    con,
    "select * from formatted_data_with_planned_enrollment;"
    )
 df <- fetch(query,Inf)
 df <- na.omit(df)
 n_categories <- 22 #number of categories in ICD-10
 ################ Format Data ###########################
 categories <- df["category_id"]
 #Convert log(x+1) to arcsinh
 x <- df["elapsed_duration"]
 x["log_n_brands"] <- log(df$n_brands + 1)
 x["h_sdi_val"] <- log(df$h_sdi_val + 1)
 x["hm_sdi_val"] <- log(df$hm_sdi_val + 1)
 x["m_sdi_val"] <- log(df$m_sdi_val + 1)
 x["lm_sdi_val"] <- log(df$lm_sdi_val + 1)
 x["l_sdi_val"] <- log(df$l_sdi_val + 1)
 #x["enrollment"] <- df["current_enrollment"] / df["planned_enrollment"]
 #square terms
 #x["enrollment^2"] <- x["enrollment"]^2
 #x["elapsed_duration^2"] <- x["elapsed_duration"]^2
 #x["n_brands^2"] <- x["n_brands"]^2
 #break out
 x["status_NYR"] <- ifelse(df["current_status"]=="Not yet recruiting",1,0)
 x["status_Rec"] <- ifelse(df["current_status"]=="Recruiting",1,0)
 x["status_ANR"] <- ifelse(df["current_status"]=="Active, not recruiting",1,0)
 x["status_EBI"] <- ifelse(df["current_status"]=="Enrolling by invitation",1,0)
 y <- ifelse(df["final_status"]=="Terminated",1,0)
 ################################# DATA EXPLORATION ############################
 #Plot terminated vs completed
 #Plot duration for terminated vs completed
 #Plot different times of
 ################################# FIT #########################################
 #setup data (named list)
 trials_data <- list(
    D = ncol(x),#
    N = nrow(x),
    L = n_categories,
    y = as.vector(y),
    ll = categories$category_id,
    x = as.matrix(x),
    mu_mean = 0,
    mu_stdev = 0.5,
    sigma_shape = 6,
    sigma_rate = 12
 )
 model <- cmdstan_model(file.path("Hierarchal_Logistic.stan"))
 fit <- model$sample(
    data = trials_data,
    seed = 11021585,
    chains = 4,
    parallel_chains = 4,
    refresh = 500
 )
 ################################# ANALYZE #####################################
 color_scheme_set("darkgray")
 div_style <- parcoord_style_np(div_color = "green", div_size = 0.05, div_alpha = 0.4)
 print(fit$summary(),n=265)
 stanfitted <- rstan::read_stan_csv(fit$output_files())
 #analyze mu values
 draw_mu <- fit$draws("mu")
 mcmc_hist(draw_mu)
 mcmc_trace(draw_mu)
 mcmc_pairs(draw_mu)
 mcmc_parcoord(posterior,pars=c(
    "mu[1]",
    "mu[2]",
    "mu[3]",
    "mu[4]",
    "mu[5]",
    "mu[6]",
    "mu[7]",
    "mu[8]",
    "mu[9]",
    "mu[10]",
    "mu[11]"
 ),
 np=nuts_prmts,
 np_style = div_style
 )
 #check sigma
 draw_sigma <- fit$draws("sigma")
 mcmc_hist(draw_sigma)
 mcmc_trace(draw_sigma)
 mcmc_parcoord(posterior,pars=c(
    "sigma[1]",
    "sigma[2]",
    "sigma[3]",
    "sigma[4]",
    "sigma[5]",
    "sigma[6]",
    "sigma[7]",
    "sigma[8]",
    "sigma[9]",
    "sigma[10]",
    "sigma[11]"
 ),
 np=nuts_prmts,
 np_style = div_style
 )
 #other diagnostics
 logpost <- log_posterior(fit)
 nuts_prmts <- nuts_params(fit)
 posterior <- fit$draws()
 mcmc_pairs(
    posterior,
    np = nuts_prmts,
    pars=c(
        "mu[1]",
        "mu[2]",
        "mu[3]",
        "mu[4]",
        "mu[5]",
        "mu[6]",
        "mu[7]",
        "mu[8]",
        "mu[9]",
        "mu[10]",
        "mu[11]"
    ),
    off_diag_args = list(size = 0.75)
 )
 #Interpretation
 mcmc_areas(posterior,
    pars=c(
        "mu[1]",
        "mu[2]",
        "mu[3]",
        "mu[4]",
        "mu[5]",
        "mu[6]",
        "mu[7]",
        "mu[8]",
        "mu[9]",
        "mu[10]",
        "mu[11]"
    ),
    prob = 0.95
 )
 #Interpretation
 mcmc_areas(posterior,
           pars=c(
               "sigma[1]",
               "sigma[2]",
               "sigma[3]",
               "sigma[4]",
               "sigma[5]",
               "sigma[6]",
               "sigma[7]",
               "sigma[8]",
               "sigma[9]",
               "sigma[10]",
               "sigma[11]"
           ),
           prob = 0.95
 )
 #iterate through betas
 mcmc_areas(posterior,
           pars=c(
               "beta[1,*]"
           ),
           prob = 0.95
 )
 #generate array of betas
 betas_array <- sapply(1:11, function(param) sapply(1:22, function(group) paste0("beta[",group,",",param,"]")))
 for (group in 1:22) {
    print(
        mcmc_areas(posterior,
               pars=betas_array[group,],
               prob = 0.95
        )
    )
 }
 for (param in 1:11) {
    print(
        mcmc_areas(posterior,
                   pars=betas_array[,param],
                   prob = 0.95
        )
    )
 }
--- a/r-analysis/Hierarchal_Logistic.rds
+++ b/r-analysis/Hierarchal_Logistic.rds
--- a/r-analysis/Hierarchal_Logistic.stan
+++ b/r-analysis/Hierarchal_Logistic.stan
@ -19,13 +19,15 @@ data {
  array[N] row_vector[D] x; // independent variables
  real mu_mean; //hyperprior
  real<lower=0> mu_stdev; //hyperprior
-  real sigma_shape; //hyperprior
+  real sigma_location; //hyperprior
-  real sigma_rate; //hyperprior
+  real<lower=0> sigma_scale; //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
  //the two statuses to catch relative difference
  array[2] int status_indexes; //the two status indexes to compare (order is x[1] - x[2])
 }
 parameters {
  array[D] real mu;
@ -33,7 +35,7 @@ parameters {
  array[L] vector[D] beta;
 }
 model {
-  sigma ~ gamma(sigma_shape,sigma_rate); //hyperprior for stdev: shape and inverse scale
+  sigma ~ lognormal(sigma_location,sigma_scale); //hyperprior for stdev: shape and inverse scale
  mu ~ normal(mu_mean, mu_stdev); //hyperprior for mean //TODO: convert to mvnormal
  for (l in 1:L) {
    beta[l] ~ normal(mu, sigma);
@ -58,13 +60,20 @@ generated quantities {
    real p_predicted_intervention[Nx]; //predicted p_values
    real predicted_difference[Nx]; //difference in predicted values
    //collect array of relative status differences between
    array[L] real status_diff;
    //GENERATE RELATIVE DIFFERENCES BETWEEN STATUSES
    for (l in 1:L) {
      status_diff[l] = beta[l,status_indexes[1]] - beta[l,status_indexes[2]];
    }
    //GENERATE PRIOR PREDICTIONS
    {
        vector[D] beta_prior[L];//local var
        //sample parameters
        for (d in 1:D) {
            mu_prior[d] = normal_rng(mu_mean,mu_stdev);
-            sigma_prior[d] = gamma_rng(sigma_shape,sigma_rate);
+            sigma_prior[d] = lognormal_rng(sigma_location,sigma_scale);
        }
        for (l in 1:L) {
            for (d in 1:D) {
@ -87,6 +96,7 @@ 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];
    }
 }
--- a/r-analysis/Logistic.stan
+++ b/r-analysis/Logistic.stan
@ -1,30 +0,0 @@
 //
 // 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;
  int<lower=1> N;
  int<lower=0, upper=1> y[N];
  row_vector[D] x[N];
 }
 parameters {
  real mu[D];
  real<lower=0> sigma[D];
 }
 model {
  sigma ~ gamma(2,0.1);
  mu ~ normal(0, sigma); //convert to mvnormal
  for (n in 1:N) {
    y[n] ~ bernoulli_logit(x[n] * mu);
  }
 }
--- a/r-analysis/The
+++ b/r-analysis/The
--- a/r-analysis/basic_logit.stan
+++ b/r-analysis/basic_logit.stan
@ -1,32 +0,0 @@
 //
 // 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=0> N;
  int<lower=0> k;
  matrix[N,k] X;
  vector[N] int<lower=0, upper=1> y;
 }
 // The parameters accepted by the model. Our model
 // accepts two parameters 'mu' and 'sigma'.
 parameters {
  vector[k] beta;
 }
 // The model to be estimated. We model the output
 // 'y' to be normally distributed with mean 'mu'
 // and standard deviation 'sigma'.
 model {
  y ~ bernoulli_logit( X * beta);
 }
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/fixed_effects_anr_vs_rec_by_group.png
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/fixed_effects_anr_vs_rec_by_group.png
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/fixed_effects_anr_vs_rec_by_group.tex
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/fixed_effects_anr_vs_rec_by_group.tex
@ -0,0 +1,34 @@
 % latex table generated in R 4.4.2 by xtable 1.8-4 package
 % Sun Feb  2 01:37:36 2025
 \begin{table}[ht]
 \centering
 \begin{tabular}{lrrrrrrrrr}
  \hline
 ICD-10 Category & mean & P(≥0) & 2.5\% & 5\% & 25\% & 50\% median & 75\% & 95\% & 97.5\% \\ 
  \hline
 Blood \& Immune system & 0.061 & 0.624 & -0.333 & -0.267 & -0.071 & 0.061 & 0.194 & 0.388 & 0.454 \\ 
  Circulatory & 0.037 & 0.577 & -0.362 & -0.293 & -0.094 & 0.038 & 0.170 & 0.363 & 0.426 \\ 
  Congential & 0.063 & 0.628 & -0.332 & -0.264 & -0.068 & 0.063 & 0.195 & 0.391 & 0.456 \\ 
  Contact with Healthcare & 0.063 & 0.627 & -0.335 & -0.267 & -0.070 & 0.064 & 0.194 & 0.390 & 0.454 \\ 
  Digestive & 0.060 & 0.621 & -0.339 & -0.270 & -0.071 & 0.059 & 0.192 & 0.388 & 0.454 \\ 
  Ear and Mastoid & 0.063 & 0.625 & -0.332 & -0.263 & -0.069 & 0.064 & 0.196 & 0.393 & 0.457 \\ 
  Endocrine, Nutritional, and Metabolic & 0.177 & 0.820 & -0.201 & -0.137 & 0.045 & 0.173 & 0.304 & 0.505 & 0.573 \\ 
  External Causes & 0.063 & 0.625 & -0.330 & -0.266 & -0.070 & 0.064 & 0.194 & 0.392 & 0.460 \\ 
  Eye and Adnexa & 0.061 & 0.624 & -0.333 & -0.265 & -0.069 & 0.062 & 0.192 & 0.386 & 0.452 \\ 
  Genitourinary & 0.063 & 0.627 & -0.337 & -0.270 & -0.070 & 0.065 & 0.195 & 0.389 & 0.456 \\ 
  Infections \& Parasites & 0.135 & 0.772 & -0.223 & -0.163 & 0.013 & 0.134 & 0.257 & 0.438 & 0.498 \\ 
  Injury etc. & 0.063 & 0.625 & -0.337 & -0.268 & -0.069 & 0.062 & 0.196 & 0.392 & 0.459 \\ 
  Mental \& Behavioral & 0.142 & 0.767 & -0.239 & -0.175 & 0.010 & 0.140 & 0.270 & 0.469 & 0.537 \\ 
  Musculoskeletal & 0.159 & 0.794 & -0.218 & -0.156 & 0.027 & 0.156 & 0.286 & 0.482 & 0.550 \\ 
  Neoplasms & 0.211 & 0.902 & -0.107 & -0.056 & 0.099 & 0.208 & 0.320 & 0.489 & 0.546 \\ 
  Nervous System & 0.009 & 0.524 & -0.383 & -0.315 & -0.120 & 0.011 & 0.140 & 0.327 & 0.389 \\ 
  Perinatal Period & 0.062 & 0.626 & -0.338 & -0.270 & -0.068 & 0.062 & 0.194 & 0.390 & 0.457 \\ 
  Pregancy, Childbirth, \& Puerperium & 0.061 & 0.622 & -0.336 & -0.270 & -0.071 & 0.061 & 0.194 & 0.392 & 0.458 \\ 
  Respiratory & 0.061 & 0.622 & -0.335 & -0.269 & -0.072 & 0.061 & 0.195 & 0.391 & 0.458 \\ 
  Skin \& Subcutaneaous tissue & 0.104 & 0.704 & -0.283 & -0.220 & -0.027 & 0.103 & 0.234 & 0.429 & 0.494 \\ 
  Special Purposes & 0.062 & 0.625 & -0.335 & -0.268 & -0.070 & 0.062 & 0.194 & 0.391 & 0.456 \\ 
  Symptoms, Signs etc. & 0.062 & 0.625 & -0.332 & -0.265 & -0.069 & 0.062 & 0.194 & 0.390 & 0.456 \\ 
   \hline
 \end{tabular}
 \end{table}
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/parameters_10_status_EBI.png
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/parameters_10_status_EBI.png
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/parameters_11_status_Rec.png
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/parameters_11_status_Rec.png
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/parameters_12_status_ANR.png
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/parameters_12_status_ANR.png
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/parameters_1_Elapsed
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/parameters_1_Elapsed
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/parameters_2_asinh(Generic
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/parameters_2_asinh(Generic
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/parameters_3_asinh(Competitors
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/parameters_3_asinh(Competitors
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/parameters_9_status_NYR.png
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/parameters_9_status_NYR.png
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/parameters_tables_10_status_EBI.tex
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/parameters_tables_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(-0.00287140656905416, -0.0020229135961933, -0.00252250078632693, -0.00209236160817254, -0.00233302952373521, -0.00216616648896081, -0.00681543732790809, -0.00273617241938979, -0.00219896995482389, -0.00310971192809426, -0.00900529540694934, -0.00252589644802287, -0.00444905498743218, -0.00275503221432378, -0.00295173909211749, -0.00517241124667845, -0.00201260276746087, -0.00257497154836192, -0.00242272398017638, -0.00267620974013439, -0.00248385412497201, -0.00206395314166231)
 c(`2.5%` = -0.275944463803495, `2.5%` = -0.272921161056328, `2.5%` = -0.272430725186905, `2.5%` = -0.272911375314851, `2.5%` = -0.27286212768348, `2.5%` = -0.276406977245084, `2.5%` = -0.277987677303119, `2.5%` = -0.270429258845101, `2.5%` = -0.272173859797933, `2.5%` = -0.270957011640022, `2.5%` = -0.281051472827658, `2.5%` = -0.274123164780341, `2.5%` = -0.273927630545993, `2.5%` = -0.272259113214428, `2.5%` = -0.271867873121147, `2.5%` = -0.275526164264352, `2.5%` = -0.275736741086972, `2.5%` = -0.274025059638234, 
 `2.5%` = -0.271895651649722, `2.5%` = -0.274439135854432, `2.5%` = -0.272777026543343, `2.5%` = -0.274505781494588)
 c(`97.5%` = 0.268525663885088, `97.5%` = 0.269687555821123, `97.5%` = 0.263988376114375, `97.5%` = 0.268376674723849, `97.5%` = 0.271595831106391, `97.5%` = 0.27398054865504, `97.5%` = 0.26497995981819, `97.5%` = 0.264914777867797, `97.5%` = 0.268729163024419, `97.5%` = 0.266687930004995, `97.5%` = 0.260632526818398, `97.5%` = 0.270701536658997, `97.5%` = 0.264287404395679, `97.5%` = 0.266631371530776, `97.5%` = 0.265171319133342, `97.5%` = 0.267696135908434, `97.5%` = 0.269792405742312, `97.5%` = 0.267738792080357, 
 `97.5%` = 0.270026733830649, `97.5%` = 0.267892275489918, `97.5%` = 0.266305005411752, `97.5%` = 0.271501986023693)
 c(`5%` = -0.228422342352796, `5%` = -0.225192615705392, `5%` = -0.225155935562034, `5%` = -0.227577347541465, `5%` = -0.226336678780408, `5%` = -0.227963356496732, `5%` = -0.230488468962024, `5%` = -0.226216509362732, `5%` = -0.224813386072165, `5%` = -0.224824888562106, `5%` = -0.232874112114568, `5%` = -0.227479714082238, `5%` = -0.227858560023144, `5%` = -0.22530608055078, `5%` = -0.226179910621409, `5%` = -0.228284369418676, `5%` = -0.228444224989461, `5%` = -0.226622261032726, `5%` = -0.224288571128251, 
 `5%` = -0.227590395464131, `5%` = -0.225531022618535, `5%` = -0.227689137616937)
 c(`95%` = 0.2234974899362, `95%` = 0.222074828728455, `95%` = 0.218357594565282, `95%` = 0.22222430007764, `95%` = 0.222289991457749, `95%` = 0.223327488400761, `95%` = 0.216270931014924, `95%` = 0.220095617146775, `95%` = 0.221009192368411, `95%` = 0.218989193656767, `95%` = 0.213804656879867, `95%` = 0.222889299900387, `95%` = 0.220133939553423, `95%` = 0.21899037055656, `95%` = 0.218796743068272, `95%` = 0.220217569663176, `95%` = 0.224044557893232, `95%` = 0.220936167501407, `95%` = 0.222159099161132, 
 `95%` = 0.220911069519647, `95%` = 0.220781640962889, `95%` = 0.223599356920508)
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/parameters_tables_11_status_Rec.tex
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/parameters_tables_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(0.00652882287222823, -0.0121276026193075, 0.00806097077518296, 0.00921072076176892, 0.00511136013511625, 0.00923452323640744, 0.0756452851959799, 0.00901967004583639, 0.00596509106734287, 0.00869308015726206, 0.0234704825434495, 0.00891347968211811, 0.0410008650026277, 0.0493113026890666, -0.0318651891745962, -0.0230137010884716, 0.00907668265519354, 0.0082773304353959, 0.00672386515176627, 0.0229027326955782, 0.00903950397151059, 0.00840626979762671)
 c(`2.5%` = -0.26412401679988, `2.5%` = -0.284982548735974, `2.5%` = -0.262583809560454, `2.5%` = -0.259623047912961, `2.5%` = -0.267223017905891, `2.5%` = -0.262824783073146, `2.5%` = -0.182738362310984, `2.5%` = -0.261194421023313, `2.5%` = -0.264838971088612, `2.5%` = -0.263984097470059, `2.5%` = -0.233328888684629, `2.5%` = -0.260381735988535, `2.5%` = -0.219982381931615, `2.5%` = -0.209178899422793, `2.5%` = -0.272496159194591, `2.5%` = -0.293189864865698, `2.5%` = -0.259442015177563, `2.5%` = -0.265643699376373, 
 `2.5%` = -0.264848607612733, `2.5%` = -0.243262927814473, `2.5%` = -0.263583321279326, `2.5%` = -0.260710033426863)
 c(`97.5%` = 0.279506924359323, `97.5%` = 0.25166727687992, `97.5%` = 0.279367210952163, `97.5%` = 0.278659096264822, `97.5%` = 0.274044451638933, `97.5%` = 0.280562384576216, `97.5%` = 0.359198759844572, `97.5%` = 0.282464614440408, `97.5%` = 0.274146522142649, `97.5%` = 0.279840596472466, `97.5%` = 0.282219317765516, `97.5%` = 0.279650795194328, `97.5%` = 0.3146382824935, `97.5%` = 0.324504387608479, `97.5%` = 0.198395249398707, `97.5%` = 0.238015436387141, `97.5%` = 0.279664049019525, `97.5%` = 0.282472259620116, 
 `97.5%` = 0.280170359985892, `97.5%` = 0.296090615801029, `97.5%` = 0.278827396276502, `97.5%` = 0.276519814652463)
 c(`5%` = -0.217405377189081, `5%` = -0.235700053008877, `5%` = -0.215580888141684, `5%` = -0.212836780879927, `5%` = -0.220927862811969, `5%` = -0.215245283188846, `5%` = -0.139947007794807, `5%` = -0.214372400370517, `5%` = -0.216692707544591, `5%` = -0.215840640492215, `5%` = -0.190195428873606, `5%` = -0.214974248730335, `5%` = -0.175242262948575, `5%` = -0.166101667860902, `5%` = -0.228981038446028, `5%` = -0.246615923137647, `5%` = -0.214004181031889, `5%` = -0.218493362752846, `5%` = -0.21829426001738, 
 `5%` = -0.197184265526973, `5%` = -0.216831943794109, `5%` = -0.21336683632312)
 c(`95%` = 0.231060746721114, `95%` = 0.206639994505824, `95%` = 0.232261579219567, `95%` = 0.231058781781102, `95%` = 0.228526975656424, `95%` = 0.233653720301541, `95%` = 0.306122759153545, `95%` = 0.234530990151044, `95%` = 0.226800701259279, `95%` = 0.230966741806046, `95%` = 0.237452575327, `95%` = 0.23322865073513, `95%` = 0.264306220644107, `95%` = 0.274559112084226, `95%` = 0.161143364484194, `95%` = 0.195044859439803, `95%` = 0.232513302970324, `95%` = 0.233960896032084, `95%` = 0.232756864140852, 
 `95%` = 0.246925692056239, `95%` = 0.231685551132494, `95%` = 0.230370019187908)
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/parameters_tables_12_status_ANR.tex
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/parameters_tables_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(-0.0543082831893662, -0.0491179148659016, -0.0550797452225853, -0.0532946953959887, -0.0546326964547788, -0.054263670489829, -0.101183732558573, -0.0537419015558274, -0.055296341966815, -0.0541521176630153, -0.111933746601916, -0.0536195743237358, -0.101358093383466, -0.109295568325447, -0.242704066590673, -0.0324576251191776, -0.0530684599999257, -0.0529707690770896, -0.0545919114607711, -0.0809466615050335, -0.0530673747164349, -0.0537090394695628)
 c(`2.5%` = -0.3460926221197, `2.5%` = -0.340513734259116, `2.5%` = -0.345063660691308, `2.5%` = -0.343936968361375, `2.5%` = -0.347086498849369, `2.5%` = -0.346730664863283, `2.5%` = -0.402573667581036, `2.5%` = -0.344748248866169, `2.5%` = -0.348019495991554, `2.5%` = -0.343259904607912, `2.5%` = -0.397011060181461, `2.5%` = -0.347299904964005, `2.5%` = -0.398857333530158, `2.5%` = -0.407252271853641, `2.5%` = -0.543673509898535, `2.5%` = -0.315411836398564, `2.5%` = -0.344614905885746, `2.5%` = -0.345987208689716, 
 `2.5%` = -0.34467924126698, `2.5%` = -0.37464941870207, `2.5%` = -0.346982193867717, `2.5%` = -0.34255968704254)
 c(`97.5%` = 0.23859370882391, `97.5%` = 0.247596369898235, `97.5%` = 0.236924146027621, `97.5%` = 0.239608837398885, `97.5%` = 0.23894631939739, `97.5%` = 0.238196795290067, `97.5%` = 0.180340815442861, `97.5%` = 0.240218974320717, `97.5%` = 0.235175513197134, `97.5%` = 0.237777766149638, `97.5%` = 0.156402075952097, `97.5%` = 0.242638519639189, `97.5%` = 0.182559370226462, `97.5%` = 0.170699887327904, `97.5%` = 0.0114150292406248, `97.5%` = 0.259944925352525, `97.5%` = 0.242795694071721, `97.5%` = 0.242602907693202, 
 `97.5%` = 0.239492781782234, `97.5%` = 0.204585540113804, `97.5%` = 0.238328250302865, `97.5%` = 0.240534823211121)
 c(`5%` = -0.295316756003101, `5%` = -0.29096401042673, `5%` = -0.294243091294835, `5%` = -0.293355784827148, `5%` = -0.296193852536529, `5%` = -0.294348519148639, `5%` = -0.347024621453625, `5%` = -0.293279771169653, `5%` = -0.297136938616546, `5%` = -0.2933097076324, `5%` = -0.347474604177594, `5%` = -0.294318945943837, `5%` = -0.344628024841975, `5%` = -0.352532667961251, `5%` = -0.488379411138948, `5%` = -0.266550224886603, `5%` = -0.293396519481707, `5%` = -0.295913858425759, `5%` = -0.294475437187543, 
 `5%` = -0.322719512364534, `5%` = -0.294234511411049, `5%` = -0.291661527567577)
 c(`95%` = 0.186170625673217, `95%` = 0.194446427538144, `95%` = 0.18316007227917, `95%` = 0.185398130082336, `95%` = 0.18656668417393, `95%` = 0.186918825584728, `95%` = 0.133329444707804, `95%` = 0.188140180351712, `95%` = 0.186846293810823, `95%` = 0.188406047073467, `95%` = 0.112535221760718, `95%` = 0.191176019972506, `95%` = 0.132004530075709, `95%` = 0.124477125901656, `95%` = -0.0267022930175043, `95%` = 0.205343484029368, `95%` = 0.187850282416095, `95%` = 0.190035921589226, `95%` = 0.188021330440522, 
 `95%` = 0.155090212003947, `95%` = 0.188333556494078, `95%` = 0.187492448246279)
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/parameters_tables_1_Elapsed
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/parameters_tables_1_Elapsed
@ -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(-0.0505321895701756, -0.0561236542390722, -0.0499660268658585, -0.0481574325504504, -0.0505967062178304, -0.0480668691756736, -0.0585983827703775, -0.047133883963608, -0.0507912436647474, -0.0478593490739956, -0.0816317285457012, -0.0482822682398582, -0.0774004055701482, -0.016726156695298, -0.315526037006588, -0.0578472700681722, -0.0475268941302727, -0.0480850373469818, -0.0494676291479126, -0.0931186973375372, -0.0479483938019099, -0.0486654863146441)
 c(`2.5%` = -0.351333711658477, `2.5%` = -0.358192918691032, `2.5%` = -0.350165746673952, `2.5%` = -0.348260904879301, `2.5%` = -0.355213104294602, `2.5%` = -0.349537890844496, `2.5%` = -0.356636178298712, `2.5%` = -0.348967122811696, `2.5%` = -0.356770047469594, `2.5%` = -0.348523219782961, `2.5%` = -0.369067201876791, `2.5%` = -0.352751358464831, `2.5%` = -0.384222770661754, `2.5%` = -0.310459552529128, `2.5%` = -0.642054194627079, `2.5%` = -0.359167198818045, `2.5%` = -0.351037786426624, `2.5%` = -0.35686171619278, 
 `2.5%` = -0.353960823288628, `2.5%` = -0.398212841275992, `2.5%` = -0.351411960176182, `2.5%` = -0.352062830672535)
 c(`97.5%` = 0.252996678658013, `97.5%` = 0.245199739323497, `97.5%` = 0.252568610015387, `97.5%` = 0.256300675128962, `97.5%` = 0.256220116158008, `97.5%` = 0.256880194696558, `97.5%` = 0.237496943950342, `97.5%` = 0.260731873495491, `97.5%` = 0.255694095814266, `97.5%` = 0.258199105421347, `97.5%` = 0.196240544658593, `97.5%` = 0.256780445660338, `97.5%` = 0.218570279154564, `97.5%` = 0.295751559042532, `97.5%` = -0.0498087194116947, `97.5%` = 0.238629751570793, `97.5%` = 0.258078396538427, `97.5%` = 0.259647869046876, 
 `97.5%` = 0.256663885447249, `97.5%` = 0.195728293212208, `97.5%` = 0.259664751280795, `97.5%` = 0.257403541589351)
 c(`5%` = -0.29803422118825, `5%` = -0.304624731177692, `5%` = -0.298500654906565, `5%` = -0.295625102451718, `5%` = -0.300950367218894, `5%` = -0.296909500653078, `5%` = -0.304028644400323, `5%` = -0.295898014297575, `5%` = -0.301053900765771, `5%` = -0.297395770121559, `5%` = -0.318536205133514, `5%` = -0.298729047781782, `5%` = -0.328832973524529, `5%` = -0.260408972551825, `5%` = -0.580798660746861, `5%` = -0.303098555574414, `5%` = -0.297724180258084, `5%` = -0.300789071762739, `5%` = -0.29885389568916, 
 `5%` = -0.341186799958823, `5%` = -0.297555789883245, `5%` = -0.297336353504652)
 c(`95%` = 0.199805705380915, `95%` = 0.193775727317953, `95%` = 0.20049984396355, `95%` = 0.203214802673669, `95%` = 0.202603902019158, `95%` = 0.203401989386427, `95%` = 0.187816936293634, `95%` = 0.205427481892033, `95%` = 0.200369696005793, `95%` = 0.203709821019864, `95%` = 0.149606773044668, `95%` = 0.201078718458991, `95%` = 0.168803960348435, `95%` = 0.23786658788661, `95%` = -0.0876108144400803, `95%` = 0.186931912303368, `95%` = 0.204472031901114, `95%` = 0.205112704275416, `95%` = 0.201106127451977, 
 `95%` = 0.145913702571692, `95%` = 0.204425554740135, `95%` = 0.202755710634796)
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/parameters_tables_2_asinh(Generic
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/parameters_tables_2_asinh(Generic
@ -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(-0.0374509867623648, -0.114801759701441, -0.0421991751741288, -0.0268927396537672, -0.0431305204973958, -0.0260913808525182, -0.147540114797909, -0.027116517230545, -0.0572148794918185, -0.0567196164994895, -0.0431781674057524, -0.0247442525263859, -0.148857275259831, 0.173176112299651, -0.745438348426778, 0.514928808041493, -0.0262970064646734, -0.0273698856833142, -0.0519713531203428, -0.0813059076860515, -0.0258722297716816, -0.0348374667962509)
 c(`2.5%` = -0.519252573751591, `2.5%` = -0.588446720987112, `2.5%` = -0.518375863151681, `2.5%` = -0.504192330494074, `2.5%` = -0.521537560181517, `2.5%` = -0.501531706057096, `2.5%` = -0.605455988019874, `2.5%` = -0.50319865217871, `2.5%` = -0.529409792355877, `2.5%` = -0.537648621232678, `2.5%` = -0.392971765289971, `2.5%` = -0.499643963973294, `2.5%` = -0.654106115127152, `2.5%` = -0.223573014616573, `2.5%` = -1.12441685579914, `2.5%` = 0.0216592886163218, `2.5%` = -0.492165903236766, `2.5%` = -0.506906607161406, 
 `2.5%` = -0.530569224373654, `2.5%` = -0.560352091813988, `2.5%` = -0.502878744623734, `2.5%` = -0.516492014350377)
 c(`97.5%` = 0.438156824615533, `97.5%` = 0.326892147498506, `97.5%` = 0.439646865645069, `97.5%` = 0.45479809614294, `97.5%` = 0.429980507934071, `97.5%` = 0.452354448697589, `97.5%` = 0.271710367913333, `97.5%` = 0.452356456142033, `97.5%` = 0.408657439741233, `97.5%` = 0.413237173559078, `97.5%` = 0.304781682760948, `97.5%` = 0.462872494668127, `97.5%` = 0.307321465786219, `97.5%` = 0.625146449622561, `97.5%` = -0.396240166662077, `97.5%` = 1.17318649525454, `97.5%` = 0.448797338514388, `97.5%` = 0.456641082563543, 
 `97.5%` = 0.416128814653975, `97.5%` = 0.375467600073314, `97.5%` = 0.455154502226114, `97.5%` = 0.445072841208461)
 c(`5%` = -0.43284681982114, `5%` = -0.498779889626974, `5%` = -0.434007392588596, `5%` = -0.417440686797088, `5%` = -0.43620401741364, `5%` = -0.415561086942261, `5%` = -0.520616290327113, `5%` = -0.415868472350007, `5%` = -0.445215274649135, `5%` = -0.450348871660559, `5%` = -0.335854611911698, `5%` = -0.417370214498537, `5%` = -0.55799947646926, `5%` = -0.165197877092618, `5%` = -1.05984134753029, `5%` = 0.0851401986791414, `5%` = -0.412294843116399, `5%` = -0.421537839042216, `5%` = -0.443110847973097, 
 `5%` = -0.472054693822914, `5%` = -0.4154217625395, `5%` = -0.430050287469576)
 c(`95%` = 0.353300713490598, `95%` = 0.251902327224973, `95%` = 0.353130963560884, `95%` = 0.367385797642419, `95%` = 0.346576238409847, `95%` = 0.364713198282056, `95%` = 0.20347262465658, `95%` = 0.366348980622731, `95%` = 0.328590570058774, `95%` = 0.329135185773538, `95%` = 0.24519101313148, `95%` = 0.372694013964094, `95%` = 0.231568717349548, `95%` = 0.544725565756189, `95%` = -0.449885315105183, `95%` = 1.03873143227049, `95%` = 0.364171369034129, `95%` = 0.369255306736876, `95%` = 0.335218886894626, 
 `95%` = 0.30207955992225, `95%` = 0.367493122771963, `95%` = 0.360231402349028)
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/parameters_tables_3_asinh(Competitors
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/parameters_tables_3_asinh(Competitors
@ -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(0.0177091826256191, -0.0174830982131017, 0.0197148498472504, 0.0267207484850693, 0.01865599931714, 0.0273355925564759, -0.0557624513358899, 0.0285704704444676, 0.0105781672515, 0.0120682740554864, 0.117270560237318, 0.0273743158412062, 0.0202098736769506, -0.0761301686125453, 0.14948178706425, 0.363548114620147, 0.027441418729276, 0.0271631725930479, 0.0175175823493799, 0.0472688930727053, 0.0278787334069728, 0.0236727211994869)
 c(`2.5%` = -0.321412408332877, `2.5%` = -0.360033194654434, `2.5%` = -0.317756875891385, `2.5%` = -0.310112746266069, `2.5%` = -0.321047087440019, `2.5%` = -0.307177857312271, `2.5%` = -0.406584875693101, `2.5%` = -0.302290577496734, `2.5%` = -0.329787608837027, `2.5%` = -0.325741416529682, `2.5%` = -0.15743258849236, `2.5%` = -0.31119118357692, `2.5%` = -0.317021340734379, `2.5%` = -0.431037121901406, `2.5%` = -0.095511016993371, `2.5%` = 0.02041143019079, `2.5%` = -0.305991043265743, `2.5%` = -0.30655610684263, 
 `2.5%` = -0.319852685329391, `2.5%` = -0.276270824232982, `2.5%` = -0.304861628467873, `2.5%` = -0.311752202314792)
 c(`97.5%` = 0.343837322664501, `97.5%` = 0.295622079182328, `97.5%` = 0.349107813430285, `97.5%` = 0.355203673240806, `97.5%` = 0.349024698020337, `97.5%` = 0.360808144158849, `97.5%` = 0.249517798279291, `97.5%` = 0.358750370961986, `97.5%` = 0.33508908205543, `97.5%` = 0.337658598779989, `97.5%` = 0.421101378052922, `97.5%` = 0.354555256028663, `97.5%` = 0.350403071909054, `97.5%` = 0.22676112069751, `97.5%` = 0.422731018753381, `97.5%` = 0.85608235301133, `97.5%` = 0.355759840642227, `97.5%` = 0.356934774044409, 
 `97.5%` = 0.341627016198812, `97.5%` = 0.376205200194986, `97.5%` = 0.358952915535868, `97.5%` = 0.352180233725197)
 c(`5%` = -0.25720973558682, `5%` = -0.294903363605129, `5%` = -0.257137304097466, `5%` = -0.248399306392637, `5%` = -0.25489751531943, `5%` = -0.247102777254339, `5%` = -0.339933290032092, `5%` = -0.242533928470576, `5%` = -0.263620928816618, `5%` = -0.263152155503003, `5%` = -0.112232006084965, `5%` = -0.246916752820145, `5%` = -0.255886271822305, `5%` = -0.360056361494536, `5%` = -0.0582870053082083, `5%` = 0.0634526303087552, `5%` = -0.245278803753055, `5%` = -0.246842321323342, `5%` = -0.256280286172, 
 `5%` = -0.217436243551559, `5%` = -0.244210801606863, `5%` = -0.250885342909289)
 c(`95%` = 0.285370104912777, `95%` = 0.243010994205006, `95%` = 0.289287710051617, `95%` = 0.297571077262736, `95%` = 0.291325959161563, `95%` = 0.299270475058976, `95%` = 0.200063580704579, `95%` = 0.298824142626635, `95%` = 0.27722780854366, `95%` = 0.278261424943046, `95%` = 0.365441233493439, `95%` = 0.297355276712413, `95%` = 0.290718931165702, `95%` = 0.178465709918192, `95%` = 0.373392398003416, `95%` = 0.753336516739962, `95%` = 0.297312097796899, `95%` = 0.297460388207995, `95%` = 0.285527407612361, 
 `95%` = 0.317952486884081, `95%` = 0.296773071637121, `95%` = 0.292674820916317)
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/parameters_tables_9_status_NYR.tex
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/parameters_tables_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(-0.00521081730211856, 0.0102255609545677, -0.00496344785285301, -0.00412970481483706, -0.00482782618675194, -0.0050365287595942, -0.0244992731496998, -0.00474024131164134, -0.00556304034621756, -0.00514633902980756, 0.0549642831190738, -0.00505116955549756, 0.000222710855606745, -0.0139890440514832, -0.101150807225687, 0.00328213613579688, -0.00511716442489377, -0.00389750763874108, -0.00529064338687045, 0.00342256724316399, -0.00450674475013338, -0.00565678046765618)
 c(`2.5%` = -0.283907488368931, `2.5%` = -0.264718520415862, `2.5%` = -0.281159012081347, `2.5%` = -0.28202830088485, `2.5%` = -0.284419420868419, `2.5%` = -0.281327857726233, `2.5%` = -0.301921658351706, `2.5%` = -0.281271371584173, `2.5%` = -0.284249194010694, `2.5%` = -0.284012477531593, `2.5%` = -0.210012739588992, `2.5%` = -0.283029586218827, `2.5%` = -0.27410283519672, `2.5%` = -0.290631236010586, `2.5%` = -0.392051441442553, `2.5%` = -0.267503827888536, `2.5%` = -0.281737736791382, `2.5%` = -0.279312077145091, 
 `2.5%` = -0.282292027611709, `2.5%` = -0.271858847048387, `2.5%` = -0.282095546296768, `2.5%` = -0.283135697264375)
 c(`97.5%` = 0.273419332183028, `97.5%` = 0.291348978934726, `97.5%` = 0.271017033043418, `97.5%` = 0.274111097548879, `97.5%` = 0.273436192936731, `97.5%` = 0.269026268053048, `97.5%` = 0.248170504583118, `97.5%` = 0.271591216677986, `97.5%` = 0.273523419916202, `97.5%` = 0.274995321113636, `97.5%` = 0.342573757255475, `97.5%` = 0.272937984720548, `97.5%` = 0.274132679138622, `97.5%` = 0.258354914663837, `97.5%` = 0.156606469826098, `97.5%` = 0.279853103150131, `97.5%` = 0.270902691342387, `97.5%` = 0.272104479124203, 
 `97.5%` = 0.273263017556272, `97.5%` = 0.279847543183877, `97.5%` = 0.272441175448396, `97.5%` = 0.274036307386916)
 c(`5%` = -0.233812893025361, `5%` = -0.217746444191205, `5%` = -0.233921233193929, `5%` = -0.232257851816202, `5%` = -0.233567219181413, `5%` = -0.233477104858465, `5%` = -0.252962421653837, `5%` = -0.232486743487047, `5%` = -0.235070814746299, `5%` = -0.234533692066961, `5%` = -0.167107728841027, `5%` = -0.234659301068703, `5%` = -0.226762629571076, `5%` = -0.243190415663175, `5%` = -0.337956677281885, `5%` = -0.221951335704386, `5%` = -0.233061969747887, `5%` = -0.230188221811804, `5%` = -0.234247825650107, 
 `5%` = -0.223218309293191, `5%` = -0.235479873959263, `5%` = -0.234291225207645)
 c(`95%` = 0.225018049213407, `95%` = 0.240749705129402, `95%` = 0.223576635602984, `95%` = 0.22490531538246, `95%` = 0.225150662214719, `95%` = 0.222530040783611, `95%` = 0.200572258529045, `95%` = 0.224085208533272, `95%` = 0.223765301903729, `95%` = 0.224205184960909, `95%` = 0.289724198406613, `95%` = 0.224221721966219, `95%` = 0.226948329518661, `95%` = 0.212010272021156, `95%` = 0.117095735429865, `95%` = 0.231832837318039, `95%` = 0.222364411332351, `95%` = 0.223927230144499, `95%` = 0.223829194578265, 
 `95%` = 0.231111983676153, `95%` = 0.225668354476114, `95%` = 0.223106259041378)
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_10_Respiratory.png
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_10_Respiratory.png
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_11_Digestive.png
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_11_Digestive.png
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_12_Skin
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_12_Skin
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_13_Musculoskeletal.png
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_13_Musculoskeletal.png
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_14_Genitourinary.png
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_14_Genitourinary.png
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_17_Congential.png
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_17_Congential.png
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_18_Symptoms,
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_18_Symptoms,
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_1_Infections
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_1_Infections
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_2_Neoplasms.png
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_2_Neoplasms.png
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_3_Blood
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_3_Blood
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_4_Endocrine,
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_4_Endocrine,
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_5_Mental
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_5_Mental
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_6_Nervous
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_6_Nervous
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_7_Eye
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_7_Eye
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_9_Circulatory.png
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_9_Circulatory.png
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_10_Respiratory.tex
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_10_Respiratory.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(-0.0494676291479126, 0.0175175823493799, -0.0519713531203428, -0.141092626419518, -0.100269478864353, -0.0483011770497731, -0.0706566951944514, -0.0903067809957733, -0.0545919114607711, -0.00242272398017638, -0.00529064338687045, 0.00672386515176627)
 c(`2.5%` = -0.353960823288628, `2.5%` = -0.319852685329391, `2.5%` = -0.530569224373654, `2.5%` = -0.422086178248832, `2.5%` = -0.35533612512543, `2.5%` = -0.30969751701722, `2.5%` = -0.328762771558244, `2.5%` = -0.347450401239575, `2.5%` = -0.34467924126698, `2.5%` = -0.271895651649722, `2.5%` = -0.282292027611709, `2.5%` = -0.264848607612733)
 c(`97.5%` = 0.256663885447249, `97.5%` = 0.341627016198812, `97.5%` = 0.416128814653975, `97.5%` = 0.122884801270876, `97.5%` = 0.140134372918192, `97.5%` = 0.193515113672737, `97.5%` = 0.167756086960966, `97.5%` = 0.148714819749431, `97.5%` = 0.239492781782234, `97.5%` = 0.270026733830649, `97.5%` = 0.273263017556272, `97.5%` = 0.280170359985892)
 c(`5%` = -0.29885389568916, `5%` = -0.256280286172, `5%` = -0.443110847973097, `5%` = -0.3719291117473, `5%` = -0.309480163754538, `5%` = -0.2632490885577, `5%` = -0.281813066410411, `5%` = -0.302200597441508, `5%` = -0.294475437187543, `5%` = -0.224288571128251, `5%` = -0.234247825650107, `5%` = -0.21829426001738)
 c(`95%` = 0.201106127451977, `95%` = 0.285527407612361, `95%` = 0.335218886894626, `95%` = 0.0802166258592332, `95%` = 0.100605252625849, `95%` = 0.155768362847512, `95%` = 0.129300525656481, `95%` = 0.109558120218469, `95%` = 0.188021330440522, `95%` = 0.222159099161132, `95%` = 0.223829194578265, `95%` = 0.232756864140852)
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_11_Digestive.tex
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_11_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(-0.0505967062178304, 0.01865599931714, -0.0431305204973958, -0.164813949931452, -0.115970648900297, -0.0569474070992618, -0.0803009641524095, -0.102290027233277, -0.0546326964547788, -0.00233302952373521, -0.00482782618675194, 0.00511136013511625)
 c(`2.5%` = -0.355213104294602, `2.5%` = -0.321047087440019, `2.5%` = -0.521537560181517, `2.5%` = -0.440159920047502, `2.5%` = -0.373679807524352, `2.5%` = -0.324379242419328, `2.5%` = -0.338605161726642, `2.5%` = -0.358079997022996, `2.5%` = -0.347086498849369, `2.5%` = -0.27286212768348, `2.5%` = -0.284419420868419, `2.5%` = -0.267223017905891)
 c(`97.5%` = 0.256220116158008, `97.5%` = 0.349024698020337, `97.5%` = 0.429980507934071, `97.5%` = 0.0934098582169743, `97.5%` = 0.122782511673881, `97.5%` = 0.187764012771118, `97.5%` = 0.163449179706992, `97.5%` = 0.135463410274458, `97.5%` = 0.23894631939739, `97.5%` = 0.271595831106391, `97.5%` = 0.273436192936731, `97.5%` = 0.274044451638933)
 c(`5%` = -0.300950367218894, `5%` = -0.25489751531943, `5%` = -0.43620401741364, `5%` = -0.392282017795443, `5%` = -0.327592131875321, `5%` = -0.276552341418244, `5%` = -0.293674641980593, `5%` = -0.312900506044298, `5%` = -0.296193852536529, `5%` = -0.226336678780408, `5%` = -0.233567219181413, `5%` = -0.220927862811969)
 c(`95%` = 0.202603902019158, `95%` = 0.291325959161563, `95%` = 0.346576238409847, `95%` = 0.053045778552792, `95%` = 0.0838312787329803, `95%` = 0.149008753739683, `95%` = 0.122846563348083, `95%` = 0.0964502516045677, `95%` = 0.18656668417393, `95%` = 0.222289991457749, `95%` = 0.225150662214719, `95%` = 0.228526975656424)
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_12_Skin
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_12_Skin
@ -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(-0.0931186973375372, 0.0472688930727053, -0.0813059076860515, -0.115696459653843, -0.0668043254017594, 0.0225267378773846, -0.00783924736360468, -0.0287628443176525, -0.0809466615050335, -0.00267620974013439, 0.00342256724316399, 0.0229027326955782)
 c(`2.5%` = -0.398212841275992, `2.5%` = -0.276270824232982, `2.5%` = -0.560352091813988, `2.5%` = -0.369871784607321, `2.5%` = -0.303609657008891, `2.5%` = -0.217526831000879, `2.5%` = -0.243184811863137, `2.5%` = -0.259649581484174, `2.5%` = -0.37464941870207, `2.5%` = -0.274439135854432, `2.5%` = -0.271858847048387, `2.5%` = -0.243262927814473)
 c(`97.5%` = 0.195728293212208, `97.5%` = 0.376205200194986, `97.5%` = 0.375467600073314, `97.5%` = 0.133967945394652, `97.5%` = 0.165983963699542, `97.5%` = 0.265350540741357, `97.5%` = 0.230601337710681, `97.5%` = 0.207050118076093, `97.5%` = 0.204585540113804, `97.5%` = 0.267892275489918, `97.5%` = 0.279847543183877, `97.5%` = 0.296090615801029)
 c(`5%` = -0.341186799958823, `5%` = -0.217436243551559, `5%` = -0.472054693822914, `5%` = -0.32668730633076, `5%` = -0.26256729924706, `5%` = -0.178033167408505, `5%` = -0.204261113664472, `5%` = -0.222392523721118, `5%` = -0.322719512364534, `5%` = -0.227590395464131, `5%` = -0.223218309293191, `5%` = -0.197184265526973)
 c(`95%` = 0.145913702571692, `95%` = 0.317952486884081, `95%` = 0.30207955992225, `95%` = 0.093183366481975, `95%` = 0.125927251547133, `95%` = 0.224177921805955, `95%` = 0.189346878001297, `95%` = 0.165962633366688, `95%` = 0.155090212003947, `95%` = 0.220911069519647, `95%` = 0.231111983676153, `95%` = 0.246925692056239)
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_13_Musculoskeletal.tex
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_13_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(-0.016726156695298, -0.0761301686125453, 0.173176112299651, -0.0875007238905653, -0.0663531879724985, 0.0263716192287277, -0.00242271539233981, -0.0381494869327325, -0.109295568325447, -0.00275503221432378, -0.0139890440514832, 0.0493113026890666)
 c(`2.5%` = -0.310459552529128, `2.5%` = -0.431037121901406, `2.5%` = -0.223573014616573, `2.5%` = -0.340476599617661, `2.5%` = -0.305174376069712, `2.5%` = -0.214602193292334, `2.5%` = -0.238403789666391, `2.5%` = -0.273459885159963, `2.5%` = -0.407252271853641, `2.5%` = -0.272259113214428, `2.5%` = -0.290631236010586, `2.5%` = -0.209178899422793)
 c(`97.5%` = 0.295751559042532, `97.5%` = 0.22676112069751, `97.5%` = 0.625146449622561, `97.5%` = 0.167253531302005, `97.5%` = 0.166163888071297, `97.5%` = 0.270408213531531, `97.5%` = 0.239032427936572, `97.5%` = 0.196051431168698, `97.5%` = 0.170699887327904, `97.5%` = 0.266631371530776, `97.5%` = 0.258354914663837, `97.5%` = 0.324504387608479)
 c(`5%` = -0.260408972551825, `5%` = -0.360056361494536, `5%` = -0.165197877092618, `5%` = -0.295749142104736, `5%` = -0.263170921158664, `5%` = -0.174300938748019, `5%` = -0.199357322617647, `5%` = -0.232838007090428, `5%` = -0.352532667961251, `5%` = -0.22530608055078, `5%` = -0.243190415663175, `5%` = -0.166101667860902)
 c(`95%` = 0.23786658788661, `95%` = 0.178465709918192, `95%` = 0.544725565756189, `95%` = 0.123888229912759, `95%` = 0.128681125268866, `95%` = 0.228097063420235, `95%` = 0.196129167554776, `95%` = 0.155695563618615, `95%` = 0.124477125901656, `95%` = 0.21899037055656, `95%` = 0.212010272021156, `95%` = 0.274559112084226)
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_14_Genitourinary.tex
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_14_Genitourinary.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(-0.0478593490739956, 0.0120682740554864, -0.0567196164994895, -0.126233602235583, -0.0888817238478321, -0.0319840844510598, -0.055198586240614, -0.0772497283445443, -0.0541521176630153, -0.00310971192809426, -0.00514633902980756, 0.00869308015726206)
 c(`2.5%` = -0.348523219782961, `2.5%` = -0.325741416529682, `2.5%` = -0.537648621232678, `2.5%` = -0.408043017285209, `2.5%` = -0.343971909458341, `2.5%` = -0.293935837353889, `2.5%` = -0.316931983669652, `2.5%` = -0.333641043439836, `2.5%` = -0.343259904607912, `2.5%` = -0.270957011640022, `2.5%` = -0.284012477531593, `2.5%` = -0.263984097470059)
 c(`97.5%` = 0.258199105421347, `97.5%` = 0.337658598779989, `97.5%` = 0.413237173559078, `97.5%` = 0.141325836240884, `97.5%` = 0.154268857189929, `97.5%` = 0.215841474017224, `97.5%` = 0.188621888649733, `97.5%` = 0.163079486802808, `97.5%` = 0.237777766149638, `97.5%` = 0.266687930004995, `97.5%` = 0.274995321113636, `97.5%` = 0.279840596472466)
 c(`5%` = -0.297395770121559, `5%` = -0.263152155503003, `5%` = -0.450348871660559, `5%` = -0.357452526309299, `5%` = -0.298995454954135, `5%` = -0.246714915420225, `5%` = -0.268903804112122, `5%` = -0.285856831702031, `5%` = -0.2933097076324, `5%` = -0.224824888562106, `5%` = -0.234533692066961, `5%` = -0.215840640492215)
 c(`95%` = 0.203709821019864, `95%` = 0.278261424943046, `95%` = 0.329135185773538, `95%` = 0.0975164848465818, `95%` = 0.11201114117772, `95%` = 0.174468036775523, `95%` = 0.148448833659993, `95%` = 0.122321755852338, `95%` = 0.188406047073467, `95%` = 0.218989193656767, `95%` = 0.224205184960909, `95%` = 0.230966741806046)
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_17_Congential.tex
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_17_Congential.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(-0.0499660268658585, 0.0197148498472504, -0.0421991751741288, -0.13476563953491, -0.0956058294147287, -0.0442534026160268, -0.0662861597218824, -0.0865335314057803, -0.0550797452225853, -0.00252250078632693, -0.00496344785285301, 0.00806097077518296)
 c(`2.5%` = -0.350165746673952, `2.5%` = -0.317756875891385, `2.5%` = -0.518375863151681, `2.5%` = -0.414723657705109, `2.5%` = -0.35057979600666, `2.5%` = -0.306304745881982, `2.5%` = -0.323256686118706, `2.5%` = -0.342841852065351, `2.5%` = -0.345063660691308, `2.5%` = -0.272430725186905, `2.5%` = -0.281159012081347, `2.5%` = -0.262583809560454)
 c(`97.5%` = 0.252568610015387, `97.5%` = 0.349107813430285, `97.5%` = 0.439646865645069, `97.5%` = 0.129542894513277, `97.5%` = 0.145184905982789, `97.5%` = 0.197603443786488, `97.5%` = 0.173229891076501, `97.5%` = 0.153760033992607, `97.5%` = 0.236924146027621, `97.5%` = 0.263988376114375, `97.5%` = 0.271017033043418, `97.5%` = 0.279367210952163)
 c(`5%` = -0.298500654906565, `5%` = -0.257137304097466, `5%` = -0.434007392588596, `5%` = -0.363849520269935, `5%` = -0.30377336167253, `5%` = -0.259427189054046, `5%` = -0.277378887591161, `5%` = -0.29778671716257, `5%` = -0.294243091294835, `5%` = -0.225155935562034, `5%` = -0.233921233193929, `5%` = -0.215580888141684)
 c(`95%` = 0.20049984396355, `95%` = 0.289287710051617, `95%` = 0.353130963560884, `95%` = 0.0859110309743907, `95%` = 0.105692185919512, `95%` = 0.15909647715405, `95%` = 0.13405804651131, `95%` = 0.114598752695363, `95%` = 0.18316007227917, `95%` = 0.218357594565282, `95%` = 0.223576635602984, `95%` = 0.232261579219567)
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_18_Symptoms,
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_18_Symptoms,
@ -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(-0.0486654863146441, 0.0236727211994869, -0.0348374667962509, -0.115895916505032, -0.0800513158310921, -0.023475914516512, -0.0466582461817656, -0.0684715865431922, -0.0537090394695628, -0.00206395314166231, -0.00565678046765618, 0.00840626979762671)
 c(`2.5%` = -0.352062830672535, `2.5%` = -0.311752202314792, `2.5%` = -0.516492014350377, `2.5%` = -0.394971716793324, `2.5%` = -0.333434811543346, `2.5%` = -0.286384363759695, `2.5%` = -0.304309764526848, `2.5%` = -0.325218632099741, `2.5%` = -0.34255968704254, `2.5%` = -0.274505781494588, `2.5%` = -0.283135697264375, `2.5%` = -0.260710033426863)
 c(`97.5%` = 0.257403541589351, `97.5%` = 0.352180233725197, `97.5%` = 0.445072841208461, `97.5%` = 0.154546638365113, `97.5%` = 0.162204869037247, `97.5%` = 0.222312513558577, `97.5%` = 0.200387773947608, `97.5%` = 0.175953830420771, `97.5%` = 0.240534823211121, `97.5%` = 0.271501986023693, `97.5%` = 0.274036307386916, `97.5%` = 0.276519814652463)
 c(`5%` = -0.297336353504652, `5%` = -0.250885342909289, `5%` = -0.430050287469576, `5%` = -0.345255477437627, `5%` = -0.288017242271703, `5%` = -0.239293183426376, `5%` = -0.258607586912473, `5%` = -0.280518378138114, `5%` = -0.291661527567577, `5%` = -0.227689137616937, `5%` = -0.234291225207645, `5%` = -0.21336683632312)
 c(`95%` = 0.202755710634796, `95%` = 0.292674820916317, `95%` = 0.360231402349028, `95%` = 0.109134691618903, `95%` = 0.123319005948664, `95%` = 0.182494091485978, `95%` = 0.158914432925447, `95%` = 0.135737605682531, `95%` = 0.187492448246279, `95%` = 0.223599356920508, `95%` = 0.223106259041378, `95%` = 0.230370019187908)
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_1_Infections
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_1_Infections
@ -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(-0.0816317285457012, 0.117270560237318, -0.0431781674057524, -0.322939255980837, -0.0445097363713965, 0.0831185962473774, 0.054469735486804, 0.00439061656478571, -0.111933746601916, -0.00900529540694934, 0.0549642831190738, 0.0234704825434495)
 c(`2.5%` = -0.369067201876791, `2.5%` = -0.15743258849236, `2.5%` = -0.392971765289971, `2.5%` = -0.495385786097959, `2.5%` = -0.279907428049715, `2.5%` = -0.126136965980224, `2.5%` = -0.164686552393619, `2.5%` = -0.221766129169443, `2.5%` = -0.397011060181461, `2.5%` = -0.281051472827658, `2.5%` = -0.210012739588992, `2.5%` = -0.233328888684629)
 c(`97.5%` = 0.196240544658593, `97.5%` = 0.421101378052922, `97.5%` = 0.304781682760948, `97.5%` = -0.159810477185182, `97.5%` = 0.194502628737617, `97.5%` = 0.305301279386911, `97.5%` = 0.287025096307068, `97.5%` = 0.239372401831851, `97.5%` = 0.156402075952097, `97.5%` = 0.260632526818398, `97.5%` = 0.342573757255475, `97.5%` = 0.282219317765516)
 c(`5%` = -0.318536205133514, `5%` = -0.112232006084965, `5%` = -0.335854611911698, `5%` = -0.466181244375375, `5%` = -0.239813977632262, `5%` = -0.0931509430887361, `5%` = -0.13026348611388, `5%` = -0.18443291032113, `5%` = -0.347474604177594, `5%` = -0.232874112114568, `5%` = -0.167107728841027, `5%` = -0.190195428873606)
 c(`95%` = 0.149606773044668, `95%` = 0.365441233493439, `95%` = 0.24519101313148, `95%` = -0.184792569485545, `95%` = 0.15345194844924, `95%` = 0.266763338434242, `95%` = 0.246022644974912, `95%` = 0.199837947046417, `95%` = 0.112535221760718, `95%` = 0.213804656879867, `95%` = 0.289724198406613, `95%` = 0.237452575327)
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_2_Neoplasms.tex
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_2_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(-0.315526037006588, 0.14948178706425, -0.745438348426778, 0.0393966359152392, -0.0457348007757995, 0.064106280747042, -0.0140917847275379, -0.0473660509007094, -0.242704066590673, -0.00295173909211749, -0.101150807225687, -0.0318651891745962)
 c(`2.5%` = -0.642054194627079, `2.5%` = -0.095511016993371, `2.5%` = -1.12441685579914, `2.5%` = -0.194160502625767, `2.5%` = -0.277926578990489, `2.5%` = -0.162654732653132, `2.5%` = -0.246821463107613, `2.5%` = -0.281736932939805, `2.5%` = -0.543673509898535, `2.5%` = -0.271867873121147, `2.5%` = -0.392051441442553, `2.5%` = -0.272496159194591)
 c(`97.5%` = -0.0498087194116947, `97.5%` = 0.422731018753381, `97.5%` = -0.396240166662077, `97.5%` = 0.291104489058118, `97.5%` = 0.186629528620266, `97.5%` = 0.30340806969423, `97.5%` = 0.218889942875867, `97.5%` = 0.182247266485996, `97.5%` = 0.0114150292406248, `97.5%` = 0.265171319133342, `97.5%` = 0.156606469826098, `97.5%` = 0.198395249398707)
 c(`5%` = -0.580798660746861, `5%` = -0.0582870053082083, `5%` = -1.05984134753029, `5%` = -0.157477096682293, `5%` = -0.237601446017153, `5%` = -0.12601793998578, `5%` = -0.207070786292008, `5%` = -0.242358894302708, `5%` = -0.488379411138948, `5%` = -0.226179910621409, `5%` = -0.337956677281885, `5%` = -0.228981038446028)
 c(`95%` = -0.0876108144400803, `95%` = 0.373392398003416, `95%` = -0.449885315105183, `95%` = 0.246925701935553, `95%` = 0.146465971010273, `95%` = 0.261260827352338, `95%` = 0.178110810416068, `95%` = 0.144856415039965, `95%` = -0.0267022930175043, `95%` = 0.218796743068272, `95%` = 0.117095735429865, `95%` = 0.161143364484194)
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_3_Blood
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_3_Blood
@ -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(-0.0505321895701756, 0.0177091826256191, -0.0374509867623648, -0.125776066126894, -0.0902292099576031, -0.036944329702934, -0.0591309519669422, -0.0793088083304893, -0.0543082831893662, -0.00287140656905416, -0.00521081730211856, 0.00652882287222823)
 c(`2.5%` = -0.351333711658477, `2.5%` = -0.321412408332877, `2.5%` = -0.519252573751591, `2.5%` = -0.406597880159647, `2.5%` = -0.344254022808293, `2.5%` = -0.297343859139922, `2.5%` = -0.315544785108668, `2.5%` = -0.331413856081863, `2.5%` = -0.3460926221197, `2.5%` = -0.275944463803495, `2.5%` = -0.283907488368931, `2.5%` = -0.26412401679988)
 c(`97.5%` = 0.252996678658013, `97.5%` = 0.343837322664501, `97.5%` = 0.438156824615533, `97.5%` = 0.142015362351499, `97.5%` = 0.154012266834766, `97.5%` = 0.207443984276725, `97.5%` = 0.181870296298027, `97.5%` = 0.158756949640892, `97.5%` = 0.23859370882391, `97.5%` = 0.268525663885088, `97.5%` = 0.273419332183028, `97.5%` = 0.279506924359323)
 c(`5%` = -0.29803422118825, `5%` = -0.25720973558682, `5%` = -0.43284681982114, `5%` = -0.356137939234695, `5%` = -0.299115224405783, `5%` = -0.252322537371494, `5%` = -0.269748806995291, `5%` = -0.286403912298463, `5%` = -0.295316756003101, `5%` = -0.228422342352796, `5%` = -0.233812893025361, `5%` = -0.217405377189081)
 c(`95%` = 0.199805705380915, `95%` = 0.285370104912777, `95%` = 0.353300713490598, `95%` = 0.0994967769999591, `95%` = 0.113509581613765, `95%` = 0.167490802315082, `95%` = 0.14240768840267, `95%` = 0.120221139002118, `95%` = 0.186170625673217, `95%` = 0.2234974899362, `95%` = 0.225018049213407, `95%` = 0.231060746721114)
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_4_Endocrine,
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_4_Endocrine,
@ -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(-0.0585983827703775, -0.0557624513358899, -0.147540114797909, -0.0438548071229014, -0.0394212013879011, 0.0495779683778335, -0.000453054277997355, -0.0472027857512424, -0.101183732558573, -0.00681543732790809, -0.0244992731496998, 0.0756452851959799)
 c(`2.5%` = -0.356636178298712, `2.5%` = -0.406584875693101, `2.5%` = -0.605455988019874, `2.5%` = -0.295750042124458, `2.5%` = -0.273112681569153, `2.5%` = -0.186331398654033, `2.5%` = -0.236827961184312, `2.5%` = -0.283031319821082, `2.5%` = -0.402573667581036, `2.5%` = -0.277987677303119, `2.5%` = -0.301921658351706, `2.5%` = -0.182738362310984)
 c(`97.5%` = 0.237496943950342, `97.5%` = 0.249517798279291, `97.5%` = 0.271710367913333, `97.5%` = 0.217593297859276, `97.5%` = 0.19504307737622, `97.5%` = 0.294769414462677, `97.5%` = 0.239952858853215, `97.5%` = 0.184326204801509, `97.5%` = 0.180340815442861, `97.5%` = 0.26497995981819, `97.5%` = 0.248170504583118, `97.5%` = 0.359198759844572)
 c(`5%` = -0.304028644400323, `5%` = -0.339933290032092, `5%` = -0.520616290327113, `5%` = -0.254196188061864, `5%` = -0.232559443411886, `5%` = -0.147666860670196, `5%` = -0.197308104756834, `5%` = -0.242030280687931, `5%` = -0.347024621453625, `5%` = -0.230488468962024, `5%` = -0.252962421653837, `5%` = -0.139947007794807)
 c(`95%` = 0.187816936293634, `95%` = 0.200063580704579, `95%` = 0.20347262465658, `95%` = 0.173544980297132, `95%` = 0.155038635197841, `95%` = 0.25235933990905, `95%` = 0.196860730183825, `95%` = 0.146126493852613, `95%` = 0.133329444707804, `95%` = 0.216270931014924, `95%` = 0.200572258529045, `95%` = 0.306122759153545)
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_5_Mental
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_5_Mental
@ -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(-0.0774004055701482, 0.0202098736769506, -0.148857275259831, -0.034756624929762, -0.0209198662802634, 0.0737245631655913, 0.0355843232922172, -0.0257770506361907, -0.101358093383466, -0.00444905498743218, 0.000222710855606745, 0.0410008650026277)
 c(`2.5%` = -0.384222770661754, `2.5%` = -0.317021340734379, `2.5%` = -0.654106115127152, `2.5%` = -0.28447613031784, `2.5%` = -0.251665449535869, `2.5%` = -0.161795305781745, `2.5%` = -0.197888724987928, `2.5%` = -0.256373204834308, `2.5%` = -0.398857333530158, `2.5%` = -0.273927630545993, `2.5%` = -0.27410283519672, `2.5%` = -0.219982381931615)
 c(`97.5%` = 0.218570279154564, `97.5%` = 0.350403071909054, `97.5%` = 0.307321465786219, `97.5%` = 0.226059193399154, `97.5%` = 0.218536405219445, `97.5%` = 0.325200306839703, `97.5%` = 0.279312654411686, `97.5%` = 0.207355980521146, `97.5%` = 0.182559370226462, `97.5%` = 0.264287404395679, `97.5%` = 0.274132679138622, `97.5%` = 0.3146382824935)
 c(`5%` = -0.328832973524529, `5%` = -0.255886271822305, `5%` = -0.55799947646926, `5%` = -0.243764242401936, `5%` = -0.214018963569841, `5%` = -0.12427108412664, `5%` = -0.159713441998239, `5%` = -0.217062488812283, `5%` = -0.344628024841975, `5%` = -0.227858560023144, `5%` = -0.226762629571076, `5%` = -0.175242262948575)
 c(`95%` = 0.168803960348435, `95%` = 0.290718931165702, `95%` = 0.231568717349548, `95%` = 0.181638021717836, `95%` = 0.175300930108115, `95%` = 0.280768005851167, `95%` = 0.236582791839493, `95%` = 0.16844496482049, `95%` = 0.132004530075709, `95%` = 0.220133939553423, `95%` = 0.226948329518661, `95%` = 0.264306220644107)
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_6_Nervous
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_6_Nervous
@ -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(-0.0578472700681722, 0.363548114620147, 0.514928808041493, -0.179789393683086, -0.0908978444606774, 0.0485959237871361, -0.00145174053575294, -0.0416302406262986, -0.0324576251191776, -0.00517241124667845, 0.00328213613579688, -0.0230137010884716)
 c(`2.5%` = -0.359167198818045, `2.5%` = 0.02041143019079, `2.5%` = 0.0216592886163218, `2.5%` = -0.430812731714158, `2.5%` = -0.333639714164675, `2.5%` = -0.181367177838171, `2.5%` = -0.231956837333007, `2.5%` = -0.27766810096831, `2.5%` = -0.315411836398564, `2.5%` = -0.275526164264352, `2.5%` = -0.267503827888536, `2.5%` = -0.293189864865698)
 c(`97.5%` = 0.238629751570793, `97.5%` = 0.85608235301133, `97.5%` = 1.17318649525454, `97.5%` = 0.0570528188192439, `97.5%` = 0.140764053180901, `97.5%` = 0.287579065151981, `97.5%` = 0.231839325930911, `97.5%` = 0.196383580001578, `97.5%` = 0.259944925352525, `97.5%` = 0.267696135908434, `97.5%` = 0.279853103150131, `97.5%` = 0.238015436387141)
 c(`5%` = -0.303098555574414, `5%` = 0.0634526303087552, `5%` = 0.0851401986791414, `5%` = -0.386505748941182, `5%` = -0.290084333457996, `5%` = -0.143728029163889, `5%` = -0.193940136889492, `5%` = -0.237273417600727, `5%` = -0.266550224886603, `5%` = -0.228284369418676, `5%` = -0.221951335704386, `5%` = -0.246615923137647)
 c(`95%` = 0.186931912303368, `95%` = 0.753336516739962, `95%` = 1.03873143227049, `95%` = 0.0190438837776769, `95%` = 0.102564973004199, `95%` = 0.243855515697918, `95%` = 0.193187180905901, `95%` = 0.155692997386599, `95%` = 0.205343484029368, `95%` = 0.220217569663176, `95%` = 0.231832837318039, `95%` = 0.195044859439803)
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_7_Eye
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_7_Eye
@ -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(-0.0507912436647474, 0.0105781672515, -0.0572148794918185, -0.161324485000642, -0.11718865552377, -0.0580549952623519, -0.0821974616540054, -0.105025588883719, -0.055296341966815, -0.00219896995482389, -0.00556304034621756, 0.00596509106734287)
 c(`2.5%` = -0.356770047469594, `2.5%` = -0.329787608837027, `2.5%` = -0.529409792355877, `2.5%` = -0.44209572260938, `2.5%` = -0.373553837099913, `2.5%` = -0.325433458075037, `2.5%` = -0.344635367302557, `2.5%` = -0.361334615741075, `2.5%` = -0.348019495991554, `2.5%` = -0.272173859797933, `2.5%` = -0.284249194010694, `2.5%` = -0.264838971088612)
 c(`97.5%` = 0.255694095814266, `97.5%` = 0.33508908205543, `97.5%` = 0.408657439741233, `97.5%` = 0.101657962161228, `97.5%` = 0.11864634098921, `97.5%` = 0.186079165001043, `97.5%` = 0.157815589258783, `97.5%` = 0.131395254082946, `97.5%` = 0.235175513197134, `97.5%` = 0.268729163024419, `97.5%` = 0.273523419916202, `97.5%` = 0.274146522142649)
 c(`5%` = -0.301053900765771, `5%` = -0.263620928816618, `5%` = -0.445215274649135, `5%` = -0.391137998579351, `5%` = -0.328258365252273, `5%` = -0.276463334286846, `5%` = -0.297022738992198, `5%` = -0.315922917337871, `5%` = -0.297136938616546, `5%` = -0.224813386072165, `5%` = -0.235070814746299, `5%` = -0.216692707544591)
 c(`95%` = 0.200369696005793, `95%` = 0.27722780854366, `95%` = 0.328590570058774, `95%` = 0.0578686050827066, `95%` = 0.0817009261712685, `95%` = 0.147963763976249, `95%` = 0.119977584917821, `95%` = 0.0949818456263718, `95%` = 0.186846293810823, `95%` = 0.221009192368411, `95%` = 0.223765301903729, `95%` = 0.226800701259279)
--- a/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_9_Circulatory.tex
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_9_Circulatory.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(-0.0561236542390722, -0.0174830982131017, -0.114801759701441, -0.0489532404314588, -0.00995833322809683, 0.0764993233097176, 0.0602798840227049, 0.0304789175922322, -0.0491179148659016, -0.0020229135961933, 0.0102255609545677, -0.0121276026193075)
 c(`2.5%` = -0.358192918691032, `2.5%` = -0.360033194654434, `2.5%` = -0.588446720987112, `2.5%` = -0.297868077807792, `2.5%` = -0.240899712415657, `2.5%` = -0.162465078674946, `2.5%` = -0.17414438609744, `2.5%` = -0.201017363333911, `2.5%` = -0.340513734259116, `2.5%` = -0.272921161056328, `2.5%` = -0.264718520415862, `2.5%` = -0.284982548735974)
 c(`97.5%` = 0.245199739323497, `97.5%` = 0.295622079182328, `97.5%` = 0.326892147498506, `97.5%` = 0.210710410280741, `97.5%` = 0.229119750480018, `97.5%` = 0.332077723102122, `97.5%` = 0.312602029054938, `97.5%` = 0.277230334088767, `97.5%` = 0.247596369898235, `97.5%` = 0.269687555821123, `97.5%` = 0.291348978934726, `97.5%` = 0.25166727687992)
 c(`5%` = -0.304624731177692, `5%` = -0.294903363605129, `5%` = -0.498779889626974, `5%` = -0.256277233535303, `5%` = -0.202637633223219, `5%` = -0.125070759586198, `5%` = -0.13517180498734, `5%` = -0.163740873087956, `5%` = -0.29096401042673, `5%` = -0.225192615705392, `5%` = -0.217746444191205, `5%` = -0.235700053008877)
 c(`95%` = 0.193775727317953, `95%` = 0.243010994205006, `95%` = 0.251902327224973, `95%` = 0.165019140778464, `95%` = 0.188705917246998, `95%` = 0.287176225295342, `95%` = 0.267926957380469, `95%` = 0.233147083774771, `95%` = 0.194446427538144, `95%` = 0.222074828728455, `95%` = 0.240749705129402, `95%` = 0.206639994505824)
--- a/r-analysis/output/EffectsOfEnrollmentDelay/diagnostics/parcoord_mu.png
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/diagnostics/parcoord_mu.png
--- a/r-analysis/output/EffectsOfEnrollmentDelay/diagnostics/trace_plot_mu.png
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/diagnostics/trace_plot_mu.png
--- a/r-analysis/output/EffectsOfEnrollmentDelay/diagnostics/trace_rank_plot_mu_1-4.png
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/diagnostics/trace_rank_plot_mu_1-4.png
--- a/r-analysis/output/EffectsOfEnrollmentDelay/diagnostics/trace_rank_plot_mu_5-8.png
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/diagnostics/trace_rank_plot_mu_5-8.png
--- a/r-analysis/output/EffectsOfEnrollmentDelay/diagnostics/trace_rank_plot_mu_9-12.png
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/diagnostics/trace_rank_plot_mu_9-12.png
--- a/r-analysis/output/EffectsOfEnrollmentDelay/diagnostics/trace_rank_plot_sigma_1-4.png
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/diagnostics/trace_rank_plot_sigma_1-4.png
--- a/r-analysis/output/EffectsOfEnrollmentDelay/diagnostics/trace_rank_plot_sigma_5-8.png
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/diagnostics/trace_rank_plot_sigma_5-8.png
--- a/r-analysis/output/EffectsOfEnrollmentDelay/diagnostics/trace_rank_plot_sigma_9-12.png
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/diagnostics/trace_rank_plot_sigma_9-12.png
--- a/r-analysis/output/EffectsOfEnrollmentDelay/diagnostics/traceplot_sigma.png
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/diagnostics/traceplot_sigma.png
--- a/r-analysis/output/EffectsOfEnrollmentDelay/dist_diff_analysis/p_delay_intervention.png
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/dist_diff_analysis/p_delay_intervention.png
--- a/r-analysis/output/EffectsOfEnrollmentDelay/dist_diff_analysis/p_delay_intervention_cumulative_distdiff.png
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/dist_diff_analysis/p_delay_intervention_cumulative_distdiff.png
--- a/r-analysis/output/EffectsOfEnrollmentDelay/dist_diff_analysis/p_delay_intervention_distdiff_1.png
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/dist_diff_analysis/p_delay_intervention_distdiff_1.png
--- a/r-analysis/output/EffectsOfEnrollmentDelay/dist_diff_analysis/p_delay_intervention_distdiff_boxplot.png
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/dist_diff_analysis/p_delay_intervention_distdiff_boxplot.png
--- a/r-analysis/output/EffectsOfEnrollmentDelay/dist_diff_analysis/p_delay_intervention_distdiff_by_group.png
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/dist_diff_analysis/p_delay_intervention_distdiff_by_group.png
--- a/r-analysis/output/EffectsOfEnrollmentDelay/dist_diff_analysis/p_delay_intervention_distdiff_styled.png
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/dist_diff_analysis/p_delay_intervention_distdiff_styled.png
--- a/r-analysis/output/EffectsOfEnrollmentDelay/dist_diff_analysis/p_delay_intervention_histdiff_boxplot.png
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/dist_diff_analysis/p_delay_intervention_histdiff_boxplot.png
--- a/r-analysis/output/EffectsOfEnrollmentDelay/dist_diff_analysis/posterior_p.png
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/dist_diff_analysis/posterior_p.png
--- a/r-analysis/output/EffectsOfEnrollmentDelay/dist_diff_analysis/prior_mu.png
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/dist_diff_analysis/prior_mu.png
--- a/r-analysis/output/EffectsOfEnrollmentDelay/dist_diff_analysis/prior_p.png
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/dist_diff_analysis/prior_p.png
--- a/r-analysis/output/EffectsOfEnrollmentDelay/dist_diff_analysis/prior_sigma.png
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/dist_diff_analysis/prior_sigma.png
--- a/r-analysis/output/EffectsOfEnrollmentDelay/dist_diff_analysisp_delay_intervention_histdiff_by_group.png
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/dist_diff_analysisp_delay_intervention_histdiff_by_group.png
--- a/r-analysis/output/EffectsOfEnrollmentDelay/dist_diff_analysisp_no_intervention.png
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/dist_diff_analysisp_no_intervention.png
--- a/r-analysis/output/EffectsOfEnrollmentDelay/distdiff_5percentile_table.tex
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/distdiff_5percentile_table.tex
@ -0,0 +1,33 @@
 % latex table generated in R 4.4.2 by xtable 1.8-4 package
 % Sun Feb  2 01:37:17 2025
 \begin{table}[ht]
 \centering
 \begin{tabular}{r}
  \hline
 Value \\ 
  \hline
 -0.226 \\ 
  -0.013 \\ 
  -0.005 \\ 
  -0.001 \\ 
  -0.000 \\ 
  0.000 \\ 
  0.000 \\ 
  0.002 \\ 
  0.005 \\ 
  0.008 \\ 
  0.011 \\ 
  0.014 \\ 
  0.018 \\ 
  0.022 \\ 
  0.027 \\ 
  0.032 \\ 
  0.039 \\ 
  0.047 \\ 
  0.057 \\ 
  0.073 \\ 
  0.272 \\ 
   \hline
 \end{tabular}
 \end{table}
--- a/r-analysis/output/EffectsOfEnrollmentDelay/trials_details/CategoryCounts.png
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/trials_details/CategoryCounts.png
--- a/r-analysis/output/EffectsOfEnrollmentDelay/trials_details/HistSnapshots.png
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/trials_details/HistSnapshots.png
--- a/r-analysis/output/EffectsOfEnrollmentDelay/trials_details/HistTrialDurations_Faceted.png
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/trials_details/HistTrialDurations_Faceted.png
--- a/r-analysis/output/EffectsOfEnrollmentDelay/trials_details/SnapshotsVsDurationVsTermination.png
+++ b/r-analysis/output/EffectsOfEnrollmentDelay/trials_details/SnapshotsVsDurationVsTermination.png
Author	SHA1	Message	Date
will king	5b271a41c5	Merge branch 'lognorm_sigma_prior'	1 year ago
will king	f17f88728f	Merge branch 'exploring_fixed_effects'	1 year ago
Will King	cd90d8e28b	reran analysis to get up to date stuff	1 year ago
Will King	7074fa6c32	Updated graphing code	1 year ago
Will King	6fcc8cda22	Fixed column issue in enrollment delay	1 year ago
Will King	be77d1d38a	Fixed error in column order that invalidated results The columns for competitor drugs were placed in the wrong spot.	1 year ago
Will King	c1ebd42639	Began exploring fixed effects' interpretation	1 year ago
will king	73407ac215	Merge branch 'main' of https://git.youainti.com/youainti/ClinicalTrialsEstimation	1 year ago
will king	c29d696e5e	recording home-pc version of recruitment status	1 year ago
Will	2f71e63e22	Removed results (images) from prior analyses. There are a lot of images that have been collected from prior analyses. I'm cleaning that up and changing the directory tree where they are stored.	1 year ago
Will King	87d2077f6e	Got new images for analysis Based on updated data.	1 year ago
Will King	5a7e6ff299	Merge branch 'main' of https://git.youainti.com/youainti/ClinicalTrialsEstimation	1 year ago
Will King	26b3ce9b17	Updated analysis graphs and interpretation. Also renamed some images to match what they represent. The new .png files are from that. The renamed ones are from running the analysis earlier.	1 year ago
will king	c255652744	Merge branch 'main' of ssh://git.youainti.com:3022/youainti/ClinicalTrialsEstimation	2 years ago
will king	9656a59a19	removed unused Julia models	2 years ago
Will King	d25f5c2a0e	Ran an analysis for pharma and included all the output.	2 years ago