88 changed files with 7475 additions and 11878 deletions
--- a/ClinicalTrialsEstimation.Rproj
+++ b/ClinicalTrialsEstimation.Rproj
@ -1,13 +0,0 @@
-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
@ -0,0 +1,62 @@
+
+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
@ -0,0 +1,783 @@
+---
+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
@ -0,0 +1,239 @@
+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
@ -19,15 +19,13 @@ data {
  array[N] row_vector[D] x; // independent variables
  real mu_mean; //hyperprior
  real<lower=0> mu_stdev; //hyperprior
-  real sigma_location; //hyperprior
-  real<lower=0> sigma_scale; //hyperprior
+  real sigma_shape; //hyperprior
+  real sigma_rate; //hyperprior
  //counterfactuals
  int<lower=0> Nx;
-  array[Nx] int<lower=1, upper=L> llx;//vec of categories
+  array[Nx] int<lower=1, upper=L> llx;
  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;
@ -35,7 +33,7 @@ parameters {
  array[L] vector[D] beta;
 }
 model {
-  sigma ~ lognormal(sigma_location,sigma_scale); //hyperprior for stdev: shape and inverse scale
+  sigma ~ gamma(sigma_shape,sigma_rate); //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);
@ -60,20 +58,13 @@ 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] = lognormal_rng(sigma_location,sigma_scale);
+            sigma_prior[d] = gamma_rng(sigma_shape,sigma_rate);
        }
        for (l in 1:L) {
            for (d in 1:D) {
@ -96,7 +87,6 @@ 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

-        //intervention - base case
-        predicted_difference[n] = p_predicted_intervention[n] - p_predicted_default[n];
+        predicted_difference[n] = p_predicted_default[n] - p_predicted_intervention[n];
    }
 }
--- a/r-analysis/Logistic.stan
+++ b/r-analysis/Logistic.stan
@ -0,0 +1,30 @@
+//
+// 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
@ -0,0 +1,32 @@
+//
+// 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
@ -1,34 +0,0 @@
-% 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
@ -1,10 +0,0 @@
-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
@ -1,10 +0,0 @@
-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
@ -1,10 +0,0 @@
-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
@ -1,10 +0,0 @@
-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
@ -1,10 +0,0 @@
-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
@ -1,10 +0,0 @@
-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
@ -1,10 +0,0 @@
-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
@ -1,6 +0,0 @@
-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
@ -1,6 +0,0 @@
-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
@ -1,6 +0,0 @@
-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
@ -1,6 +0,0 @@
-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
@ -1,6 +0,0 @@
-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
@ -1,6 +0,0 @@
-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,
@ -1,6 +0,0 @@
-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
@ -1,6 +0,0 @@
-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
@ -1,6 +0,0 @@
-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
@ -1,6 +0,0 @@
-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,
@ -1,6 +0,0 @@
-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
@ -1,6 +0,0 @@
-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
@ -1,6 +0,0 @@
-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
@ -1,6 +0,0 @@
-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
@ -1,6 +0,0 @@
-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
@ -1,33 +0,0 @@
-% 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