Merge branch 'lognorm_sigma_prior'

r-analysis/EffectsOfEnrollmentDelay.qmd

@@ -35,7 +35,6 @@ image_dist_diff_analysis <- paste0(image_root,"/dist_diff_analysis")
 image_trial_details <-paste0(image_root,"/trials_details")
 image_parameters_by_groups <-paste0(image_root,"/betas/parameters_by_group")
 image_parameters_across_groups <-paste0(image_root,"/betas/parameter_across_groups")
-
 ```
 
 ```{r}
@@ -219,8 +218,8 @@ counterfact_base <- data_formatter(df_counterfact_base)
 
 categories <- df$category_id
 
-x <- train$x
-y <- train$y
+x <- as.matrix(train$x)
+y <- as.vector(train$y)
 
 x_cf_base <- counterfact_base$x
 y_cf_base <- counterfact_base$y
@@ -238,14 +237,14 @@ cf_categories <- df_counterfact_base$category_id
 ################################# FIT MODEL #########################################
 inherited_cols <- c(
     "elapsed_duration"
-    #,"identical_brands"
-    #,"brand_name_counts"
+    ,"identical_brands"
+    ,"brand_name_counts"
     ,"h_sdi_val"
     ,"hm_sdi_val"
     ,"m_sdi_val"
     ,"lm_sdi_val"
     ,"l_sdi_val"
-    ,"status_NYR"# TODO: may need to remove
+    ,"status_NYR"
     ,"status_EBI"
     ,"status_Rec"
     ,"status_ANR"
@@ -404,27 +403,30 @@ parameter_mcmc_areas <- function(
 Plan: select all snapshots that are the first to have closed enrollment (Rec -> ANR)
 ```{r}
 #delay intervention
-intervention_enrollment <- x_cf_base[c(inherited_cols,"brand_name_counts", "identical_brands")]
+intervention_enrollment <- x_cf_base[c(inherited_cols)]
 intervention_enrollment["status_ANR"] <- 0
 intervention_enrollment["status_Rec"] <- 1
 ```
 
 ```{r}
+
 counterfact_delay <- list(
-    D = ncol(x),#
+    D = ncol(x),
     N = nrow(x),
     L = n_categories$count,
-    y = as.vector(y),
+    y = y,
     ll = as.vector(categories),
-    x = as.matrix(x),
+    x = x,
     mu_mean = 0,
     mu_stdev = 0.05,
-    sigma_shape = 4,
-    sigma_rate = 20,
+    sigma_location = -2.1,
+    sigma_scale = 0.2,
     Nx = nrow(x_cf_base),
     llx = as.vector(cf_categories),
     counterfact_x_tilde = as.matrix(intervention_enrollment),
-    counterfact_x = as.matrix(x_cf_base)
+    counterfact_x = as.matrix(x_cf_base),
+    status_indexes = c(11,12) #subtract anr from recruiting to get movement from anr to recruiting
+
 )
 ```
 
@@ -432,14 +434,24 @@ counterfact_delay <- list(
 fit <- stan(
     file='Hierarchal_Logistic.stan', 
     data = counterfact_delay,
-    chains = 4,
-    iter = 5000,
+    chains = 8,
+    iter = 12000,
+    warmup = 4000,
     seed = 11021585
     )
 ```
 
 
+## Fit Results
+```{r}
+print(check_hmc_diagnostics(fit))
+print(get_bfmi(fit))
+```
+
 
+```{r}
+print(fit)
+```
 
 
 
@@ -557,21 +569,15 @@ ggsave(paste0(image_trial_details,"/CategoryCounts.png"))
 
 summary(df5)
 
-cor(df5$duration,df5$snapshot_count)
-sum(df5$snapshot_count)
+cor_dur_count <- cor(df5$duration,df5$snapshot_count)
+count_snapshots <- sum(df5$snapshot_count)
 ```
 
+the correlation value is `r cor_dur_count` between duration and snapshot count.
+There are `r count_snapshots` snapshots in total.
 
 
 
-## Fit Results
-
-
-```{r}
-################################# ANALYZE #####################################
-print(fit)
-```
-
 # Parameter Distributions
 
 
@@ -594,13 +600,16 @@ for (i in category_count$category_id[category_count$n >= 0]) {
     gx <- c(gx,gi)
 
     #Get Quantiles and means for parameters
-#    table <- xtable(gi$quantiles,
-#      floating=FALSE
-#      ,latex.environments = NULL
-#      ,booktabs = TRUE
-#      ,zap=getOption("digits")
-#      )
-#    write_lines(table,paste0("./latex_output/DirectEffects/group_",gi$name,".tex"))
+    table <- xtable(gi$quantiles,
+      floating=FALSE
+      ,latex.environments = NULL
+      ,booktabs = TRUE
+      ,zap=getOption("digits")
+      )
+    write_lines(
+        table,
+        paste0(image_parameters_by_groups,"/group_table_",i,"_",gi$name,".tex")
+    )
 }
 ```
 
@@ -621,13 +630,16 @@ for (i in c(1,2,3,9,10,11,12)) {
     px <- c(px,pi)
 
     #Get Quantiles and means for parameters
-#    table <- xtable(pi$quantiles,
-#      floating=FALSE
-#      ,latex.environments = NULL
-#      ,booktabs = TRUE
-#      ,zap=getOption("digits")
-#      )
-#    write_lines(table,paste0("./latex_output/DirectEffects/parameters_",i,"_",pi$name,".tex"))
+    table <- xtable(pi$quantiles,
+      floating=FALSE
+      ,latex.environments = NULL
+      ,booktabs = TRUE
+      ,zap=getOption("digits")
+      )
+    write_lines(
+        table,
+        paste0(image_parameters_across_groups,"/parameters_tables_",i,"_",pi$name,".tex")
+    )
     
 }
 ```
@@ -638,18 +650,8 @@ Note these have 95% outer CI and 80% inner (shaded)
 
 
 
-```{r}
-#get the generic and uspdc parameters
-print(px[4]$plot + px[7]$plot)
-ggsave(paste0(image_parameters_across_groups,"2+3_generic_and_uspdc.png"))
-
-#get the parameters associated with duration
-px[16]$plot + px[19]$plot
-ggsave(paste0(image_parameters_across_groups,"11+12_statusREC_and_statusANR.png"))
-```
-
 
-# Counterfactuals
+# Counterfactuals calculation
 
 ```{r}
 generated_ib <- gqs(
@@ -659,7 +661,7 @@ generated_ib <- gqs(
     seed=11021585
     )
 ```
-
+# Priors
 ```{r}
 df_ib_p <- data.frame(
     p_prior=as.vector(extract(generated_ib, pars="p_prior")$p_prior)
@@ -670,7 +672,10 @@ df_ib_prior <- data.frame(
     mu_prior = as.vector(extract(generated_ib, pars="mu_prior")$mu_prior)
     ,sigma_prior = as.vector(extract(generated_ib, pars="sigma_prior")$sigma_prior)
 )
+```
+
 
+```{r}
 #p_prior
 ggplot(df_ib_p, aes(x=p_prior)) +
     geom_density() + 
@@ -718,11 +723,6 @@ ggsave(paste0(image_dist_diff_analysis,"/prior_sigma.png"))
 
 
 
-```{r}
-check_hmc_diagnostics(fit)
-```
-
-
 
 
 
@@ -744,7 +744,7 @@ ggplot(counterfact_predicted_ib, aes(x=p_predicted_default)) +
         ,x="Probability Domain 'p'"
         ,y="Probability Density"
     )
-ggsave(paste0(image_dist_diff_analysis,"p_no_intervention.png"))
+ggsave(paste0(image_dist_diff_analysis,"/p_no_intervention.png"))
 
 ggplot(counterfact_predicted_ib, aes(x=p_predicted_intervention)) +
     geom_density() + 
@@ -792,9 +792,10 @@ pddf_ib["category_name"] <- sapply(
     pddf_ib$category, 
     function(i) category_names[i]
     )
-  
+```
 
 
+```{r}
 ggplot(pddf_ib, aes(x=value,)) +
     geom_density(adjust=1/5) +
     labs(
@@ -805,7 +806,10 @@ ggplot(pddf_ib, aes(x=value,)) +
     ) + 
     geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") 
 ggsave(paste0(image_dist_diff_analysis,"/p_delay_intervention_distdiff_styled.png"))
+```
 
+
+```{r}
 ggplot(pddf_ib, aes(x=value,)) +
     geom_density(adjust=1/5) +
     facet_wrap(
@@ -824,7 +828,10 @@ ggplot(pddf_ib, aes(x=value,)) +
     geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") +
     theme(strip.text.x = element_text(size = 8))
 ggsave(paste0(image_dist_diff_analysis,"/p_delay_intervention_distdiff_by_group.png"))
+```
+
 
+```{r}
 ggplot(pddf_ib, aes(x=value,)) +
     geom_histogram(bins=300) +
     facet_wrap(
@@ -842,7 +849,7 @@ ggplot(pddf_ib, aes(x=value,)) +
     ) + 
     geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") +
     theme(strip.text.x = element_text(size = 8))
-ggsave(paste0(image_dist_diff_analysis,"p_delay_intervention_histdiff_by_group.png"))
+ggsave(paste0(image_dist_diff_analysis,"/p_delay_intervention_histdiff_by_group.png"))
 ```
 
 
@@ -922,8 +929,10 @@ p3 +
    y = stats$y_offset * 1.5
  ), aes(x = x, y = y))
 ggsave(paste0(image_dist_diff_analysis,"/p_delay_intervention_histdiff_boxplot.png"))
+```
 
 
+```{r}
 p4 <- ggplot(pddf_ib, aes(x = value)) +
   geom_density() +
     labs(
@@ -980,6 +989,8 @@ p4 <- ggplot(pddf_ib, aes(x = value)) +
    y = stats$y_offset_density * 1.5
  ), aes(x = x, y = y))
 ggsave(paste0(image_dist_diff_analysis,"/p_delay_intervention_distdiff_boxplot.png"))
+
+p4
 ```
 
 ```{r}
@@ -1030,13 +1041,29 @@ quants <- quantile(pddf_ib$value, probs = seq(0,1,0.05), type=4)
 
 # Convert to a data frame
 quant_df <- data.frame(
-  Percentile = names(quants),
+  #Percentile = names(quants),
   Value = quants
 )
+# Convert to LaTeX
+table <- xtable(quant_df,
+    digits = rep(3, ncol(quant_df) + 1),
+    floating = FALSE,
+    latex.environments = NULL,
+    booktabs = TRUE
+)
+
+# Write to file
+write_lines(
+    print(table, include.rownames = FALSE),
+    paste0(image_root,"/distdiff_5percentile_table.tex")
+)
+
 kable(quant_df)
+
+proportion_increase <- mean(pddf_ib$value >= 0)
 ```
 
-There seems to be some trials that are highly suceptable to this enrollment delay. Specifically, there were some 
+about `r proportion_increase * 100` percent probability increase in the probability of terminations
 
 ```{r}
 n = length(counterfact_predicted_ib$p_predicted_intervention)
@@ -1047,10 +1074,59 @@ simulated_terminations_base <-mean(rbinom(n,k,as.vector(counterfact_predicted_ib
 simulated_percentages <- (simulated_terminations_intervention - simulated_terminations_base)/k
 ```
 
-The simulation above shows that this results in a percentage-point increase of about 
+The simulation above shows that this results in a percentage-point change in terminations of about 
 `r simulated_percentages * 100`.
 
+## fixed effects distributions
+
+```{r}
+#Get dataframe with only the rows of interest
+filtdata <- as.data.frame(extract(fit, pars="status_diff"))
+#rename columns
+dimnames(filtdata)[[2]] <- beta_list$groups
+#create area plot with appropriate title
+mcmc_areas(filtdata,prob = 0.8, prob_outer = 0.95) +
+    ggtitle("Differences in Fixed Effects | By ICD-10 Category", 
+            subtitle = "Moving from 'Active, not recruiting' to 'Recruiting'"
+        ) +
+    geom_vline(xintercept=seq(-0.25,0.5,0.25),color="grey",alpha=0.750)  
+ggsave(paste0(image_parameters_across_groups,"/fixed_effects_anr_vs_rec_by_group.png"))
+    
+d <- pivot_longer(filtdata, everything()) |> 
+    group_by(name) |> 
+    summarize(
+        mean = mean(value),
+        `P(≥0)` = mean(value >= 0),
+        `2.5%` = quantile(value, probs = 0.025),
+        `5%` = quantile(value, probs = 0.05),
+        `25%` = quantile(value, probs = 0.25),
+        `50% median` = quantile(value, probs = 0.5),
+        `75%` = quantile(value, probs = 0.75),
+        `95%` = quantile(value, probs = 0.95),
+        `97.5%` = quantile(value, probs = 0.975)
+    )
+
+# Rename the name column
+names(d)[1] <- "ICD-10 Category"
 
+# Convert to LaTeX
+table <- xtable(d,
+    digits = rep(3, ncol(d) + 1),
+    floating = FALSE,
+    latex.environments = NULL,
+    booktabs = TRUE
+)
+
+
+# Write to file
+write_lines(
+    print(table, include.rownames = FALSE),
+    paste0(image_parameters_across_groups,"/fixed_effects_anr_vs_rec_by_group.tex")
+)
+
+
+
+```
 
 
 # Diagnostics
@@ -1207,4 +1283,4 @@ for (i in 1:3) {
 
 
 
-  
+  

r-analysis/EffectsOfEnrollmentDelay.rmarkdown

@@ -6,8 +6,12 @@ editor: source
 ---
 
 
+
+
 # Setup
 
+
+
 ```{r}
 library(knitr)
 library(bayesplot)
@@ -30,12 +34,11 @@ options(mc.cores = parallel::detectCores())
 #example(stan_model, package = "rstan", run.dontrun = TRUE)
 
 
-image_root <- "./output/withdiff/EffectsOfEnrollmentDelay"
+image_root <- "./output/EffectsOfEnrollmentDelay"
 image_dist_diff_analysis <- paste0(image_root,"/dist_diff_analysis")
 image_trial_details <-paste0(image_root,"/trials_details")
 image_parameters_by_groups <-paste0(image_root,"/betas/parameters_by_group")
 image_parameters_across_groups <-paste0(image_root,"/betas/parameter_across_groups")
-
 ```
 
 ```{r}
@@ -200,7 +203,6 @@ x["l_sdi_val"] <- asinh(df$l_sdi_val)
 
 
 #Setup fixed effects
-
 x["status_NYR"] <- ifelse(df["current_status"]=="Not yet recruiting",1,0)
 x["status_EBI"] <- ifelse(df["current_status"]=="Enrolling by invitation",1,0)
 x["status_Rec"] <- ifelse(df["current_status"]=="Recruiting",1,0) 
@@ -220,8 +222,8 @@ counterfact_base <- data_formatter(df_counterfact_base)
 
 categories <- df$category_id
 
-x <- train$x
-y <- train$y
+x <- as.matrix(train$x)
+y <- as.vector(train$y)
 
 x_cf_base <- counterfact_base$x
 y_cf_base <- counterfact_base$y
@@ -230,11 +232,15 @@ cf_categories <- df_counterfact_base$category_id
 
 
 
+
+
 # Fit Model
 
 
 
 
+
+
 ```{r}
 ################################# FIT MODEL #########################################
 inherited_cols <- c(
@@ -253,8 +259,6 @@ inherited_cols <- c(
 )
 ```
 
-
-
 ```{r}
 beta_list <- list(
         groups = c(
@@ -402,33 +406,37 @@ parameter_mcmc_areas <- function(
 
 ```
 
-Plan: select all snapshots that are the first to have closed 
-enrollment (Rec -> ANR when comparing across snapshots), and then 
+
+
+Plan: select all snapshots that are the first to have closed enrollment (Rec -> ANR)
+
+
 ```{r}
 #delay intervention
 intervention_enrollment <- x_cf_base[c(inherited_cols)]
-#TOFIX: ^^^ This ordering of columns is
 intervention_enrollment["status_ANR"] <- 0
 intervention_enrollment["status_Rec"] <- 1
 ```
 
 ```{r}
+
 counterfact_delay <- list(
-    D = ncol(x),#
+    D = ncol(x),
     N = nrow(x),
     L = n_categories$count,
-    y = as.vector(y),
+    y = y,
     ll = as.vector(categories),
-    x = as.matrix(x),
+    x = x,
     mu_mean = 0,
     mu_stdev = 0.05,
-    sigma_shape = 4,
-    sigma_rate = 20,
+    sigma_location = -2.1,
+    sigma_scale = 0.2,
     Nx = nrow(x_cf_base),
     llx = as.vector(cf_categories),
     counterfact_x_tilde = as.matrix(intervention_enrollment),
     counterfact_x = as.matrix(x_cf_base),
     status_indexes = c(11,12) #subtract anr from recruiting to get movement from anr to recruiting
+
 )
 ```
 
@@ -436,31 +444,39 @@ counterfact_delay <- list(
 fit <- stan(
     file='Hierarchal_Logistic.stan', 
     data = counterfact_delay,
-    chains = 4,
-    iter = 5000,
+    chains = 8,
+    iter = 12000,
+    warmup = 4000,
     seed = 11021585
     )
 ```
 
 
 
-## Calculate relative difference in parameters between recruiting and active not recruiting states
 
+## Fit Results
+
+
+```{r}
+print(check_hmc_diagnostics(fit))
+print(get_bfmi(fit))
+```
 
 ```{r}
-filt_data <- as.data.frame(extract(fit,pars="status_diff"))
-dimnames(filt_data)[[2]] <- beta_list$groups
-mcmc_areas(filt_data,prob = 0.8, prob_outer = 0.95) +
-  ggtitle("Relative fixed effects across groups", subtitle ="moving from `Active, not recruiting` to `Recruiting`") +
-        geom_vline(xintercept=seq(-2,2,0.5),color="grey",alpha=0.750) 
+print(fit)
 ```
 
-I've got an issue here, because this should be a movement from ANR -> recruiting
-but that is suggesting that 
+
+
+
+
+
 
 ## Explore data
 
 
+
+
 ```{r}
 #get number of trials and snapshots in each category
 group_trials_by_category <- as.data.frame(aggregate(category_id ~ nct_id, df, max))
@@ -470,7 +486,6 @@ category_count <- group_trials_by_category |> group_by(category_id) |> count()
 
 ```
 
-
 ```{r}
 ################################# DATA EXPLORATION ############################
 driver <- dbDriver("PostgreSQL")
@@ -570,24 +585,22 @@ ggsave(paste0(image_trial_details,"/CategoryCounts.png"))
 
 summary(df5)
 
-cor(df5$duration,df5$snapshot_count)
-sum(df5$snapshot_count)
+cor_dur_count <- cor(df5$duration,df5$snapshot_count)
+count_snapshots <- sum(df5$snapshot_count)
 ```
 
 
 
+the correlation value is `r cor_dur_count` between duration and snapshot count.
+There are `r count_snapshots` snapshots in total.
 
-## Fit Results
 
 
-```{r}
-################################# ANALYZE #####################################
-#print(fit)
-```
-
 # Parameter Distributions
 
 
+
+
 ```{r}
 #g1 <- group_mcmc_areas("beta",beta_list,fit,1)
 
@@ -607,18 +620,19 @@ for (i in category_count$category_id[category_count$n >= 0]) {
     gx <- c(gx,gi)
 
     #Get Quantiles and means for parameters
-#    table <- xtable(gi$quantiles,
-#      floating=FALSE
-#      ,latex.environments = NULL
-#      ,booktabs = TRUE
-#      ,zap=getOption("digits")
-#      )
-#    write_lines(table,paste0("./latex_output/DirectEffects/group_",gi$name,".tex"))
+    table <- xtable(gi$quantiles,
+      floating=FALSE
+      ,latex.environments = NULL
+      ,booktabs = TRUE
+      ,zap=getOption("digits")
+      )
+    write_lines(
+        table,
+        paste0(image_parameters_by_groups,"/group_table_",i,"_",gi$name,".tex")
+    )
 }
 ```
 
-
-
 ```{r}
 px <- c()
 
@@ -634,35 +648,32 @@ for (i in c(1,2,3,9,10,11,12)) {
     px <- c(px,pi)
 
     #Get Quantiles and means for parameters
-#    table <- xtable(pi$quantiles,
-#      floating=FALSE
-#      ,latex.environments = NULL
-#      ,booktabs = TRUE
-#      ,zap=getOption("digits")
-#      )
-#    write_lines(table,paste0("./latex_output/DirectEffects/parameters_",i,"_",pi$name,".tex"))
+    table <- xtable(pi$quantiles,
+      floating=FALSE
+      ,latex.environments = NULL
+      ,booktabs = TRUE
+      ,zap=getOption("digits")
+      )
+    write_lines(
+        table,
+        paste0(image_parameters_across_groups,"/parameters_tables_",i,"_",pi$name,".tex")
+    )
     
 }
 ```
 
+
+
 Note these have 95% outer CI and 80% inner (shaded)
 
 
 
 
 
-```{r}
-#get the generic and uspdc parameters
-print(px[4]$plot + px[7]$plot)
-ggsave(paste0(image_parameters_across_groups,"2+3_generic_and_uspdc.png"))
 
-#get the parameters associated with duration
-px[16]$plot + px[19]$plot
-ggsave(paste0(image_parameters_across_groups,"11+12_statusREC_and_statusANR.png"))
-```
+# Counterfactuals calculation
 
 
-# Counterfactuals
 
 ```{r}
 generated_ib <- gqs(
@@ -673,7 +684,10 @@ generated_ib <- gqs(
     )
 ```
 
-### Get priors
+
+# Priors
+
+
 ```{r}
 df_ib_p <- data.frame(
     p_prior=as.vector(extract(generated_ib, pars="p_prior")$p_prior)
@@ -684,15 +698,17 @@ df_ib_prior <- data.frame(
     mu_prior = as.vector(extract(generated_ib, pars="mu_prior")$mu_prior)
     ,sigma_prior = as.vector(extract(generated_ib, pars="sigma_prior")$sigma_prior)
 )
+```
 
+```{r}
 #p_prior
 ggplot(df_ib_p, aes(x=p_prior)) +
     geom_density() + 
     labs(
-        title="implied prior distribution p"
+        title="Implied Prior Distribution P"
         ,subtitle=""
-        ,x="probability domain 'p'"
-        ,y="probability density"
+        ,x="Probability Domain 'p'"
+        ,y="Probability Density"
     )
 ggsave(paste0(image_dist_diff_analysis,"/prior_p.png"))
 
@@ -700,9 +716,9 @@ ggsave(paste0(image_dist_diff_analysis,"/prior_p.png"))
 ggplot(df_ib_p, aes(x=p_predicted)) +
     geom_density() + 
     labs(
-        title="implied posterior distribution p"
+        title="Implied Posterior Distribution P"
         ,subtitle=""
-        ,x="probability domain 'p'"
+        ,x="Probability Domain 'p'"
         ,y="Probability Density"
     )
 ggsave(paste0(image_dist_diff_analysis,"/posterior_p.png"))
@@ -732,9 +748,6 @@ ggsave(paste0(image_dist_diff_analysis,"/prior_sigma.png"))
 
 
 
-```{r}
-check_hmc_diagnostics(fit)
-```
 
 
 
@@ -742,6 +755,8 @@ check_hmc_diagnostics(fit)
 
 ### Intervention: Delay close of enrollment
 
+
+
 ```{r}
 counterfact_predicted_ib <- data.frame(
     p_predicted_default = as.vector(extract(generated_ib, pars="p_predicted_default")$p_predicted_default)
@@ -781,7 +796,6 @@ ggplot(counterfact_predicted_ib, aes(x=predicted_difference)) +
 ggsave(paste0(image_dist_diff_analysis,"/p_delay_intervention_distdiff_1.png"))
 ```
 
-
 ```{r}
 get_category_count <- function(tbl, id) {
   result <- tbl$n[tbl$category_id == id]
@@ -806,9 +820,9 @@ pddf_ib["category_name"] <- sapply(
     pddf_ib$category, 
     function(i) category_names[i]
     )
-  
-
+```
 
+```{r}
 ggplot(pddf_ib, aes(x=value,)) +
     geom_density(adjust=1/5) +
     labs(
@@ -819,7 +833,9 @@ ggplot(pddf_ib, aes(x=value,)) +
     ) + 
     geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") 
 ggsave(paste0(image_dist_diff_analysis,"/p_delay_intervention_distdiff_styled.png"))
+```
 
+```{r}
 ggplot(pddf_ib, aes(x=value,)) +
     geom_density(adjust=1/5) +
     facet_wrap(
@@ -838,7 +854,9 @@ ggplot(pddf_ib, aes(x=value,)) +
     geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") +
     theme(strip.text.x = element_text(size = 8))
 ggsave(paste0(image_dist_diff_analysis,"/p_delay_intervention_distdiff_by_group.png"))
+```
 
+```{r}
 ggplot(pddf_ib, aes(x=value,)) +
     geom_histogram(bins=300) +
     facet_wrap(
@@ -859,7 +877,6 @@ ggplot(pddf_ib, aes(x=value,)) +
 ggsave(paste0(image_dist_diff_analysis,"p_delay_intervention_histdiff_by_group.png"))
 ```
 
-
 ```{r}
 p3 <- ggplot(pddf_ib, aes(x=value,)) +
     geom_histogram(bins=500) +
@@ -936,8 +953,9 @@ p3 +
    y = stats$y_offset * 1.5
  ), aes(x = x, y = y))
 ggsave(paste0(image_dist_diff_analysis,"/p_delay_intervention_histdiff_boxplot.png"))
+```
 
-
+```{r}
 p4 <- ggplot(pddf_ib, aes(x = value)) +
   geom_density() +
     labs(
@@ -994,6 +1012,8 @@ p4 <- ggplot(pddf_ib, aes(x = value)) +
    y = stats$y_offset_density * 1.5
  ), aes(x = x, y = y))
 ggsave(paste0(image_dist_diff_analysis,"/p_delay_intervention_distdiff_boxplot.png"))
+
+p4
 ```
 
 ```{r}
@@ -1010,7 +1030,11 @@ ggsave(paste0(image_dist_diff_analysis,"/p_delay_intervention_cumulative_distdif
 ```
 
 
+
+
 Get the % of differences in the spike around zero
+
+
 ```{r}
 # get values around and above/below spike
 width <- 0.02
@@ -1021,6 +1045,8 @@ below_spike_band <- mean( pddf_ib$value <= -width/2)
 # get mass above and mass below zero
 mass_below_zero <- mean( pddf_ib$value <= 0)
 ```
+
+
 Looking at the spike around zero, we find that `r spike_band_centered_zero*100`% 
 of the probability mass is contained within the band from 
 [`r -width*100/2`,`r width*100/2`].
@@ -1034,6 +1060,8 @@ On average, if you keep the trial open instead of closing it,
 but, due to the high increase in probability of termination given termination was increased,
 the mean probability of termination increases by `r stats$mean`.
 
+
+
 ```{r}
 # 5%-iles
 
@@ -1044,13 +1072,33 @@ quants <- quantile(pddf_ib$value, probs = seq(0,1,0.05), type=4)
 
 # Convert to a data frame
 quant_df <- data.frame(
-  Percentile = names(quants),
+  #Percentile = names(quants),
   Value = quants
 )
+# Convert to LaTeX
+table <- xtable(quant_df,
+    digits = rep(3, ncol(d) + 1),
+    floating = FALSE,
+    latex.environments = NULL,
+    booktabs = TRUE
+)
+
+# Write to file
+write_lines(
+    print(table, include.rownames = FALSE),
+    paste0(image_root,"/distdiff_5percentile_table.tex")
+)
+
 kable(quant_df)
+
+proportion_increase <- mean(pddf_ib$value >= 0)
 ```
 
-There seems to be some trials that are highly suceptable to this enrollment delay. Specifically, there were some 
+
+
+about `r proportion_increase * 100` percent probability increase in the probability of terminations
+
+
 
 ```{r}
 n = length(counterfact_predicted_ib$p_predicted_intervention)
@@ -1061,14 +1109,71 @@ simulated_terminations_base <-mean(rbinom(n,k,as.vector(counterfact_predicted_ib
 simulated_percentages <- (simulated_terminations_intervention - simulated_terminations_base)/k
 ```
 
-The simulation above shows that this results in a percentage-point increase of about 
+
+
+The simulation above shows that this results in a percentage-point change in terminations of about 
 `r simulated_percentages * 100`.
 
+## fixed effects distributions
+
+
+
+```{r}
+#Get dataframe with only the rows of interest
+filtdata <- as.data.frame(extract(fit, pars="status_diff"))
+#rename columns
+dimnames(filtdata)[[2]] <- beta_list$groups
+#create area plot with appropriate title
+mcmc_areas(filtdata,prob = 0.8, prob_outer = 0.95) +
+    ggtitle("Differences in Fixed Effects | By ICD-10 Category", 
+            subtitle = "Moving from 'Active, not recruiting' to 'Recruiting'"
+        ) +
+    geom_vline(xintercept=seq(-0.25,0.5,0.25),color="grey",alpha=0.750)  
+ggsave(paste0(image_parameters_across_groups,"/fixed_effects_anr_vs_rec_by_group.png"))
+    
+d <- pivot_longer(filtdata, everything()) |> 
+    group_by(name) |> 
+    summarize(
+        mean = mean(value),
+        `P(≥0)` = mean(value >= 0),
+        `2.5%` = quantile(value, probs = 0.025),
+        `5%` = quantile(value, probs = 0.05),
+        `25%` = quantile(value, probs = 0.25),
+        `50% median` = quantile(value, probs = 0.5),
+        `75%` = quantile(value, probs = 0.75),
+        `95%` = quantile(value, probs = 0.95),
+        `97.5%` = quantile(value, probs = 0.975)
+    )
+
+# Rename the name column
+names(d)[1] <- "ICD-10 Category"
+
+# Convert to LaTeX
+table <- xtable(d,
+    digits = rep(3, ncol(d) + 1),
+    floating = FALSE,
+    latex.environments = NULL,
+    booktabs = TRUE
+)
+
+
+# Write to file
+write_lines(
+    print(table, include.rownames = FALSE),
+    paste0(image_parameters_across_groups,"/fixed_effects_anr_vs_rec_by_group.tex")
+)
+
+
+
+```
+
 
 
 
 # Diagnostics
 
+
+
 ```{r}
 #| eval: true
 #trace plots
@@ -1192,7 +1297,6 @@ for (i in 1:3) {
 }
 ```
 
-
 ```{r}
 #| eval: true
 for (k in 1:22) {
@@ -1216,9 +1320,3 @@ for (i in 1:3) {
 }}
 ```
 
-
-
-
-
-
-  

r-analysis/Hierarchal_Logistic.stan

@@ -19,8 +19,8 @@ data {
   array[N] row_vector[D] x; // independent variables
   real mu_mean; //hyperprior
   real<lower=0> mu_stdev; //hyperprior
-  real sigma_shape; //hyperprior
-  real sigma_rate; //hyperprior
+  real sigma_location; //hyperprior
+  real<lower=0> sigma_scale; //hyperprior
   //counterfactuals
   int<lower=0> Nx;
   array[Nx] int<lower=1, upper=L> llx;//vec of categories
@@ -35,7 +35,7 @@ parameters {
   array[L] vector[D] beta;
 }
 model {
-  sigma ~ gamma(sigma_shape,sigma_rate); //hyperprior for stdev: shape and inverse scale
+  sigma ~ lognormal(sigma_location,sigma_scale); //hyperprior for stdev: shape and inverse scale
   mu ~ normal(mu_mean, mu_stdev); //hyperprior for mean //TODO: convert to mvnormal
   for (l in 1:L) {
     beta[l] ~ normal(mu, sigma);
@@ -59,8 +59,8 @@ generated quantities {
     real p_predicted_default[Nx]; //predicted p_values
     real p_predicted_intervention[Nx]; //predicted p_values
     real predicted_difference[Nx]; //difference in predicted values
-    
-    //collect array of relative status differences between 
+
+    //collect array of relative status differences between
     array[L] real status_diff;
     //GENERATE RELATIVE DIFFERENCES BETWEEN STATUSES
     for (l in 1:L) {
@@ -73,7 +73,7 @@ generated quantities {
         //sample parameters
         for (d in 1:D) {
             mu_prior[d] = normal_rng(mu_mean,mu_stdev);
-            sigma_prior[d] = gamma_rng(sigma_shape,sigma_rate);
+            sigma_prior[d] = lognormal_rng(sigma_location,sigma_scale);
         }
         for (l in 1:L) {
             for (d in 1:D) {

r-analysis/Hierarchal_Logistic_with_status_diff.stan

@@ -1,102 +0,0 @@
-//
-// This Stan program defines a simple model, with a
-// vector of values 'y' modeled as normally distributed
-// with mean 'mu' and standard deviation 'sigma'.
-//
-// Learn more about model development with Stan at:
-//
-//    http://mc-stan.org/users/interfaces/rstan.html
-//    https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started
-//
-
-// The input data is a vector 'y' of length 'N'.
-data {
-  int<lower=1> D; // number of features
-  int<lower=1> N; // number of observations
-  int<lower=1> L; // number of categories
-  array[N] int<lower=0, upper=1> y; // vector of dependent variables
-  array[N] int<lower=1, upper=L> ll; // vector of categories
-  array[N] row_vector[D] x; // independent variables
-  real mu_mean; //hyperprior
-  real<lower=0> mu_stdev; //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] 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;
-  array[D] real<lower=0> sigma;
-  array[L] vector[D] beta;
-}
-model {
-  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);
-  }
-  {
-    vector[N] x_beta_ll;
-    for (n in 1:N) {
-      x_beta_ll[n] = x[n] * beta[ll[n]];
-    }
-    y ~ bernoulli_logit(x_beta_ll);
-  }
-}
-generated quantities {
-    //SETUP PRIOR PREDICTION
-    //preallocate
-    real mu_prior[D];
-    real sigma_prior[D];
-    real p_prior[N]; // what I have priors about
-    real p_predicted[N]; //predicted p_values
-    //intervention
-    real p_predicted_default[Nx]; //predicted p_values
-    real p_predicted_intervention[Nx]; //predicted p_values
-    real predicted_difference[Nx]; //difference in predicted values
-    
-    //collect array of relative status differences between 
-    array[L] real status_diff;
-    //GENERATE RELATIVE DIFFERENCES BETWEEN STATUSES
-    for (l in 1:L) {
-      status_diff[l] = beta[l,status_indexes[1]] - beta[l,status_indexes[2]];
-    }
-
-    //GENERATE PRIOR PREDICTIONS
-    {
-        vector[D] beta_prior[L];//local var
-        //sample parameters
-        for (d in 1:D) {
-            mu_prior[d] = normal_rng(mu_mean,mu_stdev);
-            sigma_prior[d] = gamma_rng(sigma_shape,sigma_rate);
-        }
-        for (l in 1:L) {
-            for (d in 1:D) {
-                beta_prior[l,d] = normal_rng(mu_prior[d],sigma_prior[d]);
-            }
-        }
-        //generate probabilities
-        vector[D] b_prior[N];//local var
-        for (n in 1:N){
-            b_prior[n] = beta_prior[ll[n]];
-            p_prior[n] = inv_logit(  x[n] * b_prior[n] );
-        }
-    }
-    //GENERATE POSTERIOR PREDICTIONS
-    for (n in 1:N) {
-        p_predicted[n] = inv_logit( x[n] * beta[ll[n]] );
-    }
-    //GENERATE POSTERIOR  DISTRIBUTION OF DIFFERENCES
-    for (n in 1:Nx) {
-        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];
-    }
-}

r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/fixed_effects_anr_vs_rec_by_group.tex

@@ -0,0 +1,34 @@
+% latex table generated in R 4.4.2 by xtable 1.8-4 package
+% Sun Feb  2 01:37:36 2025
+\begin{table}[ht]
+\centering
+\begin{tabular}{lrrrrrrrrr}
+  \hline
+ICD-10 Category & mean & P(≥0) & 2.5\% & 5\% & 25\% & 50\% median & 75\% & 95\% & 97.5\% \\ 
+  \hline
+Blood \& Immune system & 0.061 & 0.624 & -0.333 & -0.267 & -0.071 & 0.061 & 0.194 & 0.388 & 0.454 \\ 
+  Circulatory & 0.037 & 0.577 & -0.362 & -0.293 & -0.094 & 0.038 & 0.170 & 0.363 & 0.426 \\ 
+  Congential & 0.063 & 0.628 & -0.332 & -0.264 & -0.068 & 0.063 & 0.195 & 0.391 & 0.456 \\ 
+  Contact with Healthcare & 0.063 & 0.627 & -0.335 & -0.267 & -0.070 & 0.064 & 0.194 & 0.390 & 0.454 \\ 
+  Digestive & 0.060 & 0.621 & -0.339 & -0.270 & -0.071 & 0.059 & 0.192 & 0.388 & 0.454 \\ 
+  Ear and Mastoid & 0.063 & 0.625 & -0.332 & -0.263 & -0.069 & 0.064 & 0.196 & 0.393 & 0.457 \\ 
+  Endocrine, Nutritional, and Metabolic & 0.177 & 0.820 & -0.201 & -0.137 & 0.045 & 0.173 & 0.304 & 0.505 & 0.573 \\ 
+  External Causes & 0.063 & 0.625 & -0.330 & -0.266 & -0.070 & 0.064 & 0.194 & 0.392 & 0.460 \\ 
+  Eye and Adnexa & 0.061 & 0.624 & -0.333 & -0.265 & -0.069 & 0.062 & 0.192 & 0.386 & 0.452 \\ 
+  Genitourinary & 0.063 & 0.627 & -0.337 & -0.270 & -0.070 & 0.065 & 0.195 & 0.389 & 0.456 \\ 
+  Infections \& Parasites & 0.135 & 0.772 & -0.223 & -0.163 & 0.013 & 0.134 & 0.257 & 0.438 & 0.498 \\ 
+  Injury etc. & 0.063 & 0.625 & -0.337 & -0.268 & -0.069 & 0.062 & 0.196 & 0.392 & 0.459 \\ 
+  Mental \& Behavioral & 0.142 & 0.767 & -0.239 & -0.175 & 0.010 & 0.140 & 0.270 & 0.469 & 0.537 \\ 
+  Musculoskeletal & 0.159 & 0.794 & -0.218 & -0.156 & 0.027 & 0.156 & 0.286 & 0.482 & 0.550 \\ 
+  Neoplasms & 0.211 & 0.902 & -0.107 & -0.056 & 0.099 & 0.208 & 0.320 & 0.489 & 0.546 \\ 
+  Nervous System & 0.009 & 0.524 & -0.383 & -0.315 & -0.120 & 0.011 & 0.140 & 0.327 & 0.389 \\ 
+  Perinatal Period & 0.062 & 0.626 & -0.338 & -0.270 & -0.068 & 0.062 & 0.194 & 0.390 & 0.457 \\ 
+  Pregancy, Childbirth, \& Puerperium & 0.061 & 0.622 & -0.336 & -0.270 & -0.071 & 0.061 & 0.194 & 0.392 & 0.458 \\ 
+  Respiratory & 0.061 & 0.622 & -0.335 & -0.269 & -0.072 & 0.061 & 0.195 & 0.391 & 0.458 \\ 
+  Skin \& Subcutaneaous tissue & 0.104 & 0.704 & -0.283 & -0.220 & -0.027 & 0.103 & 0.234 & 0.429 & 0.494 \\ 
+  Special Purposes & 0.062 & 0.625 & -0.335 & -0.268 & -0.070 & 0.062 & 0.194 & 0.391 & 0.456 \\ 
+  Symptoms, Signs etc. & 0.062 & 0.625 & -0.332 & -0.265 & -0.069 & 0.062 & 0.194 & 0.390 & 0.456 \\ 
+   \hline
+\end{tabular}
+\end{table}
+

r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/parameters_tables_10_status_EBI.tex

@@ -0,0 +1,10 @@
+c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.")
+c(-0.00287140656905416, -0.0020229135961933, -0.00252250078632693, -0.00209236160817254, -0.00233302952373521, -0.00216616648896081, -0.00681543732790809, -0.00273617241938979, -0.00219896995482389, -0.00310971192809426, -0.00900529540694934, -0.00252589644802287, -0.00444905498743218, -0.00275503221432378, -0.00295173909211749, -0.00517241124667845, -0.00201260276746087, -0.00257497154836192, -0.00242272398017638, -0.00267620974013439, -0.00248385412497201, -0.00206395314166231)
+c(`2.5%` = -0.275944463803495, `2.5%` = -0.272921161056328, `2.5%` = -0.272430725186905, `2.5%` = -0.272911375314851, `2.5%` = -0.27286212768348, `2.5%` = -0.276406977245084, `2.5%` = -0.277987677303119, `2.5%` = -0.270429258845101, `2.5%` = -0.272173859797933, `2.5%` = -0.270957011640022, `2.5%` = -0.281051472827658, `2.5%` = -0.274123164780341, `2.5%` = -0.273927630545993, `2.5%` = -0.272259113214428, `2.5%` = -0.271867873121147, `2.5%` = -0.275526164264352, `2.5%` = -0.275736741086972, `2.5%` = -0.274025059638234, 
+`2.5%` = -0.271895651649722, `2.5%` = -0.274439135854432, `2.5%` = -0.272777026543343, `2.5%` = -0.274505781494588)
+c(`97.5%` = 0.268525663885088, `97.5%` = 0.269687555821123, `97.5%` = 0.263988376114375, `97.5%` = 0.268376674723849, `97.5%` = 0.271595831106391, `97.5%` = 0.27398054865504, `97.5%` = 0.26497995981819, `97.5%` = 0.264914777867797, `97.5%` = 0.268729163024419, `97.5%` = 0.266687930004995, `97.5%` = 0.260632526818398, `97.5%` = 0.270701536658997, `97.5%` = 0.264287404395679, `97.5%` = 0.266631371530776, `97.5%` = 0.265171319133342, `97.5%` = 0.267696135908434, `97.5%` = 0.269792405742312, `97.5%` = 0.267738792080357, 
+`97.5%` = 0.270026733830649, `97.5%` = 0.267892275489918, `97.5%` = 0.266305005411752, `97.5%` = 0.271501986023693)
+c(`5%` = -0.228422342352796, `5%` = -0.225192615705392, `5%` = -0.225155935562034, `5%` = -0.227577347541465, `5%` = -0.226336678780408, `5%` = -0.227963356496732, `5%` = -0.230488468962024, `5%` = -0.226216509362732, `5%` = -0.224813386072165, `5%` = -0.224824888562106, `5%` = -0.232874112114568, `5%` = -0.227479714082238, `5%` = -0.227858560023144, `5%` = -0.22530608055078, `5%` = -0.226179910621409, `5%` = -0.228284369418676, `5%` = -0.228444224989461, `5%` = -0.226622261032726, `5%` = -0.224288571128251, 
+`5%` = -0.227590395464131, `5%` = -0.225531022618535, `5%` = -0.227689137616937)
+c(`95%` = 0.2234974899362, `95%` = 0.222074828728455, `95%` = 0.218357594565282, `95%` = 0.22222430007764, `95%` = 0.222289991457749, `95%` = 0.223327488400761, `95%` = 0.216270931014924, `95%` = 0.220095617146775, `95%` = 0.221009192368411, `95%` = 0.218989193656767, `95%` = 0.213804656879867, `95%` = 0.222889299900387, `95%` = 0.220133939553423, `95%` = 0.21899037055656, `95%` = 0.218796743068272, `95%` = 0.220217569663176, `95%` = 0.224044557893232, `95%` = 0.220936167501407, `95%` = 0.222159099161132, 
+`95%` = 0.220911069519647, `95%` = 0.220781640962889, `95%` = 0.223599356920508)

r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/parameters_tables_11_status_Rec.tex

@@ -0,0 +1,10 @@
+c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.")
+c(0.00652882287222823, -0.0121276026193075, 0.00806097077518296, 0.00921072076176892, 0.00511136013511625, 0.00923452323640744, 0.0756452851959799, 0.00901967004583639, 0.00596509106734287, 0.00869308015726206, 0.0234704825434495, 0.00891347968211811, 0.0410008650026277, 0.0493113026890666, -0.0318651891745962, -0.0230137010884716, 0.00907668265519354, 0.0082773304353959, 0.00672386515176627, 0.0229027326955782, 0.00903950397151059, 0.00840626979762671)
+c(`2.5%` = -0.26412401679988, `2.5%` = -0.284982548735974, `2.5%` = -0.262583809560454, `2.5%` = -0.259623047912961, `2.5%` = -0.267223017905891, `2.5%` = -0.262824783073146, `2.5%` = -0.182738362310984, `2.5%` = -0.261194421023313, `2.5%` = -0.264838971088612, `2.5%` = -0.263984097470059, `2.5%` = -0.233328888684629, `2.5%` = -0.260381735988535, `2.5%` = -0.219982381931615, `2.5%` = -0.209178899422793, `2.5%` = -0.272496159194591, `2.5%` = -0.293189864865698, `2.5%` = -0.259442015177563, `2.5%` = -0.265643699376373, 
+`2.5%` = -0.264848607612733, `2.5%` = -0.243262927814473, `2.5%` = -0.263583321279326, `2.5%` = -0.260710033426863)
+c(`97.5%` = 0.279506924359323, `97.5%` = 0.25166727687992, `97.5%` = 0.279367210952163, `97.5%` = 0.278659096264822, `97.5%` = 0.274044451638933, `97.5%` = 0.280562384576216, `97.5%` = 0.359198759844572, `97.5%` = 0.282464614440408, `97.5%` = 0.274146522142649, `97.5%` = 0.279840596472466, `97.5%` = 0.282219317765516, `97.5%` = 0.279650795194328, `97.5%` = 0.3146382824935, `97.5%` = 0.324504387608479, `97.5%` = 0.198395249398707, `97.5%` = 0.238015436387141, `97.5%` = 0.279664049019525, `97.5%` = 0.282472259620116, 
+`97.5%` = 0.280170359985892, `97.5%` = 0.296090615801029, `97.5%` = 0.278827396276502, `97.5%` = 0.276519814652463)
+c(`5%` = -0.217405377189081, `5%` = -0.235700053008877, `5%` = -0.215580888141684, `5%` = -0.212836780879927, `5%` = -0.220927862811969, `5%` = -0.215245283188846, `5%` = -0.139947007794807, `5%` = -0.214372400370517, `5%` = -0.216692707544591, `5%` = -0.215840640492215, `5%` = -0.190195428873606, `5%` = -0.214974248730335, `5%` = -0.175242262948575, `5%` = -0.166101667860902, `5%` = -0.228981038446028, `5%` = -0.246615923137647, `5%` = -0.214004181031889, `5%` = -0.218493362752846, `5%` = -0.21829426001738, 
+`5%` = -0.197184265526973, `5%` = -0.216831943794109, `5%` = -0.21336683632312)
+c(`95%` = 0.231060746721114, `95%` = 0.206639994505824, `95%` = 0.232261579219567, `95%` = 0.231058781781102, `95%` = 0.228526975656424, `95%` = 0.233653720301541, `95%` = 0.306122759153545, `95%` = 0.234530990151044, `95%` = 0.226800701259279, `95%` = 0.230966741806046, `95%` = 0.237452575327, `95%` = 0.23322865073513, `95%` = 0.264306220644107, `95%` = 0.274559112084226, `95%` = 0.161143364484194, `95%` = 0.195044859439803, `95%` = 0.232513302970324, `95%` = 0.233960896032084, `95%` = 0.232756864140852, 
+`95%` = 0.246925692056239, `95%` = 0.231685551132494, `95%` = 0.230370019187908)

r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/parameters_tables_12_status_ANR.tex

@@ -0,0 +1,10 @@
+c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.")
+c(-0.0543082831893662, -0.0491179148659016, -0.0550797452225853, -0.0532946953959887, -0.0546326964547788, -0.054263670489829, -0.101183732558573, -0.0537419015558274, -0.055296341966815, -0.0541521176630153, -0.111933746601916, -0.0536195743237358, -0.101358093383466, -0.109295568325447, -0.242704066590673, -0.0324576251191776, -0.0530684599999257, -0.0529707690770896, -0.0545919114607711, -0.0809466615050335, -0.0530673747164349, -0.0537090394695628)
+c(`2.5%` = -0.3460926221197, `2.5%` = -0.340513734259116, `2.5%` = -0.345063660691308, `2.5%` = -0.343936968361375, `2.5%` = -0.347086498849369, `2.5%` = -0.346730664863283, `2.5%` = -0.402573667581036, `2.5%` = -0.344748248866169, `2.5%` = -0.348019495991554, `2.5%` = -0.343259904607912, `2.5%` = -0.397011060181461, `2.5%` = -0.347299904964005, `2.5%` = -0.398857333530158, `2.5%` = -0.407252271853641, `2.5%` = -0.543673509898535, `2.5%` = -0.315411836398564, `2.5%` = -0.344614905885746, `2.5%` = -0.345987208689716, 
+`2.5%` = -0.34467924126698, `2.5%` = -0.37464941870207, `2.5%` = -0.346982193867717, `2.5%` = -0.34255968704254)
+c(`97.5%` = 0.23859370882391, `97.5%` = 0.247596369898235, `97.5%` = 0.236924146027621, `97.5%` = 0.239608837398885, `97.5%` = 0.23894631939739, `97.5%` = 0.238196795290067, `97.5%` = 0.180340815442861, `97.5%` = 0.240218974320717, `97.5%` = 0.235175513197134, `97.5%` = 0.237777766149638, `97.5%` = 0.156402075952097, `97.5%` = 0.242638519639189, `97.5%` = 0.182559370226462, `97.5%` = 0.170699887327904, `97.5%` = 0.0114150292406248, `97.5%` = 0.259944925352525, `97.5%` = 0.242795694071721, `97.5%` = 0.242602907693202, 
+`97.5%` = 0.239492781782234, `97.5%` = 0.204585540113804, `97.5%` = 0.238328250302865, `97.5%` = 0.240534823211121)
+c(`5%` = -0.295316756003101, `5%` = -0.29096401042673, `5%` = -0.294243091294835, `5%` = -0.293355784827148, `5%` = -0.296193852536529, `5%` = -0.294348519148639, `5%` = -0.347024621453625, `5%` = -0.293279771169653, `5%` = -0.297136938616546, `5%` = -0.2933097076324, `5%` = -0.347474604177594, `5%` = -0.294318945943837, `5%` = -0.344628024841975, `5%` = -0.352532667961251, `5%` = -0.488379411138948, `5%` = -0.266550224886603, `5%` = -0.293396519481707, `5%` = -0.295913858425759, `5%` = -0.294475437187543, 
+`5%` = -0.322719512364534, `5%` = -0.294234511411049, `5%` = -0.291661527567577)
+c(`95%` = 0.186170625673217, `95%` = 0.194446427538144, `95%` = 0.18316007227917, `95%` = 0.185398130082336, `95%` = 0.18656668417393, `95%` = 0.186918825584728, `95%` = 0.133329444707804, `95%` = 0.188140180351712, `95%` = 0.186846293810823, `95%` = 0.188406047073467, `95%` = 0.112535221760718, `95%` = 0.191176019972506, `95%` = 0.132004530075709, `95%` = 0.124477125901656, `95%` = -0.0267022930175043, `95%` = 0.205343484029368, `95%` = 0.187850282416095, `95%` = 0.190035921589226, `95%` = 0.188021330440522, 
+`95%` = 0.155090212003947, `95%` = 0.188333556494078, `95%` = 0.187492448246279)

r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/parameters_tables_1_Elapsed Duration.tex

@@ -0,0 +1,10 @@
+c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.")
+c(-0.0505321895701756, -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)

r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/parameters_tables_2_asinh(Generic Brands).tex

@@ -0,0 +1,10 @@
+c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.")
+c(-0.0374509867623648, -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)

r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/parameters_tables_3_asinh(Competitors USPDC).tex

@@ -0,0 +1,10 @@
+c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.")
+c(0.0177091826256191, -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)

r-analysis/output/EffectsOfEnrollmentDelay/betas/parameter_across_groups/parameters_tables_9_status_NYR.tex

@@ -0,0 +1,10 @@
+c("Blood & Immune system", "Circulatory", "Congential", "Contact with Healthcare", "Digestive", "Ear and Mastoid", "Endocrine, Nutritional, and Metabolic", "External Causes", "Eye and Adnexa", "Genitourinary", "Infections & Parasites", "Injury etc.", "Mental & Behavioral", "Musculoskeletal", "Neoplasms", "Nervous System", "Perinatal Period", "Pregancy, Childbirth, & Puerperium", "Respiratory", "Skin & Subcutaneaous tissue", "Special Purposes", "Symptoms, Signs etc.")
+c(-0.00521081730211856, 0.0102255609545677, -0.00496344785285301, -0.00412970481483706, -0.00482782618675194, -0.0050365287595942, -0.0244992731496998, -0.00474024131164134, -0.00556304034621756, -0.00514633902980756, 0.0549642831190738, -0.00505116955549756, 0.000222710855606745, -0.0139890440514832, -0.101150807225687, 0.00328213613579688, -0.00511716442489377, -0.00389750763874108, -0.00529064338687045, 0.00342256724316399, -0.00450674475013338, -0.00565678046765618)
+c(`2.5%` = -0.283907488368931, `2.5%` = -0.264718520415862, `2.5%` = -0.281159012081347, `2.5%` = -0.28202830088485, `2.5%` = -0.284419420868419, `2.5%` = -0.281327857726233, `2.5%` = -0.301921658351706, `2.5%` = -0.281271371584173, `2.5%` = -0.284249194010694, `2.5%` = -0.284012477531593, `2.5%` = -0.210012739588992, `2.5%` = -0.283029586218827, `2.5%` = -0.27410283519672, `2.5%` = -0.290631236010586, `2.5%` = -0.392051441442553, `2.5%` = -0.267503827888536, `2.5%` = -0.281737736791382, `2.5%` = -0.279312077145091, 
+`2.5%` = -0.282292027611709, `2.5%` = -0.271858847048387, `2.5%` = -0.282095546296768, `2.5%` = -0.283135697264375)
+c(`97.5%` = 0.273419332183028, `97.5%` = 0.291348978934726, `97.5%` = 0.271017033043418, `97.5%` = 0.274111097548879, `97.5%` = 0.273436192936731, `97.5%` = 0.269026268053048, `97.5%` = 0.248170504583118, `97.5%` = 0.271591216677986, `97.5%` = 0.273523419916202, `97.5%` = 0.274995321113636, `97.5%` = 0.342573757255475, `97.5%` = 0.272937984720548, `97.5%` = 0.274132679138622, `97.5%` = 0.258354914663837, `97.5%` = 0.156606469826098, `97.5%` = 0.279853103150131, `97.5%` = 0.270902691342387, `97.5%` = 0.272104479124203, 
+`97.5%` = 0.273263017556272, `97.5%` = 0.279847543183877, `97.5%` = 0.272441175448396, `97.5%` = 0.274036307386916)
+c(`5%` = -0.233812893025361, `5%` = -0.217746444191205, `5%` = -0.233921233193929, `5%` = -0.232257851816202, `5%` = -0.233567219181413, `5%` = -0.233477104858465, `5%` = -0.252962421653837, `5%` = -0.232486743487047, `5%` = -0.235070814746299, `5%` = -0.234533692066961, `5%` = -0.167107728841027, `5%` = -0.234659301068703, `5%` = -0.226762629571076, `5%` = -0.243190415663175, `5%` = -0.337956677281885, `5%` = -0.221951335704386, `5%` = -0.233061969747887, `5%` = -0.230188221811804, `5%` = -0.234247825650107, 
+`5%` = -0.223218309293191, `5%` = -0.235479873959263, `5%` = -0.234291225207645)
+c(`95%` = 0.225018049213407, `95%` = 0.240749705129402, `95%` = 0.223576635602984, `95%` = 0.22490531538246, `95%` = 0.225150662214719, `95%` = 0.222530040783611, `95%` = 0.200572258529045, `95%` = 0.224085208533272, `95%` = 0.223765301903729, `95%` = 0.224205184960909, `95%` = 0.289724198406613, `95%` = 0.224221721966219, `95%` = 0.226948329518661, `95%` = 0.212010272021156, `95%` = 0.117095735429865, `95%` = 0.231832837318039, `95%` = 0.222364411332351, `95%` = 0.223927230144499, `95%` = 0.223829194578265, 
+`95%` = 0.231111983676153, `95%` = 0.225668354476114, `95%` = 0.223106259041378)

r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_10_Respiratory.tex

@@ -0,0 +1,6 @@
+c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec")
+c(-0.0494676291479126, 0.0175175823493799, -0.0519713531203428, -0.141092626419518, -0.100269478864353, -0.0483011770497731, -0.0706566951944514, -0.0903067809957733, -0.0545919114607711, -0.00242272398017638, -0.00529064338687045, 0.00672386515176627)
+c(`2.5%` = -0.353960823288628, `2.5%` = -0.319852685329391, `2.5%` = -0.530569224373654, `2.5%` = -0.422086178248832, `2.5%` = -0.35533612512543, `2.5%` = -0.30969751701722, `2.5%` = -0.328762771558244, `2.5%` = -0.347450401239575, `2.5%` = -0.34467924126698, `2.5%` = -0.271895651649722, `2.5%` = -0.282292027611709, `2.5%` = -0.264848607612733)
+c(`97.5%` = 0.256663885447249, `97.5%` = 0.341627016198812, `97.5%` = 0.416128814653975, `97.5%` = 0.122884801270876, `97.5%` = 0.140134372918192, `97.5%` = 0.193515113672737, `97.5%` = 0.167756086960966, `97.5%` = 0.148714819749431, `97.5%` = 0.239492781782234, `97.5%` = 0.270026733830649, `97.5%` = 0.273263017556272, `97.5%` = 0.280170359985892)
+c(`5%` = -0.29885389568916, `5%` = -0.256280286172, `5%` = -0.443110847973097, `5%` = -0.3719291117473, `5%` = -0.309480163754538, `5%` = -0.2632490885577, `5%` = -0.281813066410411, `5%` = -0.302200597441508, `5%` = -0.294475437187543, `5%` = -0.224288571128251, `5%` = -0.234247825650107, `5%` = -0.21829426001738)
+c(`95%` = 0.201106127451977, `95%` = 0.285527407612361, `95%` = 0.335218886894626, `95%` = 0.0802166258592332, `95%` = 0.100605252625849, `95%` = 0.155768362847512, `95%` = 0.129300525656481, `95%` = 0.109558120218469, `95%` = 0.188021330440522, `95%` = 0.222159099161132, `95%` = 0.223829194578265, `95%` = 0.232756864140852)

r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_11_Digestive.tex

@@ -0,0 +1,6 @@
+c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec")
+c(-0.0505967062178304, 0.01865599931714, -0.0431305204973958, -0.164813949931452, -0.115970648900297, -0.0569474070992618, -0.0803009641524095, -0.102290027233277, -0.0546326964547788, -0.00233302952373521, -0.00482782618675194, 0.00511136013511625)
+c(`2.5%` = -0.355213104294602, `2.5%` = -0.321047087440019, `2.5%` = -0.521537560181517, `2.5%` = -0.440159920047502, `2.5%` = -0.373679807524352, `2.5%` = -0.324379242419328, `2.5%` = -0.338605161726642, `2.5%` = -0.358079997022996, `2.5%` = -0.347086498849369, `2.5%` = -0.27286212768348, `2.5%` = -0.284419420868419, `2.5%` = -0.267223017905891)
+c(`97.5%` = 0.256220116158008, `97.5%` = 0.349024698020337, `97.5%` = 0.429980507934071, `97.5%` = 0.0934098582169743, `97.5%` = 0.122782511673881, `97.5%` = 0.187764012771118, `97.5%` = 0.163449179706992, `97.5%` = 0.135463410274458, `97.5%` = 0.23894631939739, `97.5%` = 0.271595831106391, `97.5%` = 0.273436192936731, `97.5%` = 0.274044451638933)
+c(`5%` = -0.300950367218894, `5%` = -0.25489751531943, `5%` = -0.43620401741364, `5%` = -0.392282017795443, `5%` = -0.327592131875321, `5%` = -0.276552341418244, `5%` = -0.293674641980593, `5%` = -0.312900506044298, `5%` = -0.296193852536529, `5%` = -0.226336678780408, `5%` = -0.233567219181413, `5%` = -0.220927862811969)
+c(`95%` = 0.202603902019158, `95%` = 0.291325959161563, `95%` = 0.346576238409847, `95%` = 0.053045778552792, `95%` = 0.0838312787329803, `95%` = 0.149008753739683, `95%` = 0.122846563348083, `95%` = 0.0964502516045677, `95%` = 0.18656668417393, `95%` = 0.222289991457749, `95%` = 0.225150662214719, `95%` = 0.228526975656424)

r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_12_Skin & Subcutaneaous tissue.tex

@@ -0,0 +1,6 @@
+c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec")
+c(-0.0931186973375372, 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)

r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_13_Musculoskeletal.tex

@@ -0,0 +1,6 @@
+c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec")
+c(-0.016726156695298, -0.0761301686125453, 0.173176112299651, -0.0875007238905653, -0.0663531879724985, 0.0263716192287277, -0.00242271539233981, -0.0381494869327325, -0.109295568325447, -0.00275503221432378, -0.0139890440514832, 0.0493113026890666)
+c(`2.5%` = -0.310459552529128, `2.5%` = -0.431037121901406, `2.5%` = -0.223573014616573, `2.5%` = -0.340476599617661, `2.5%` = -0.305174376069712, `2.5%` = -0.214602193292334, `2.5%` = -0.238403789666391, `2.5%` = -0.273459885159963, `2.5%` = -0.407252271853641, `2.5%` = -0.272259113214428, `2.5%` = -0.290631236010586, `2.5%` = -0.209178899422793)
+c(`97.5%` = 0.295751559042532, `97.5%` = 0.22676112069751, `97.5%` = 0.625146449622561, `97.5%` = 0.167253531302005, `97.5%` = 0.166163888071297, `97.5%` = 0.270408213531531, `97.5%` = 0.239032427936572, `97.5%` = 0.196051431168698, `97.5%` = 0.170699887327904, `97.5%` = 0.266631371530776, `97.5%` = 0.258354914663837, `97.5%` = 0.324504387608479)
+c(`5%` = -0.260408972551825, `5%` = -0.360056361494536, `5%` = -0.165197877092618, `5%` = -0.295749142104736, `5%` = -0.263170921158664, `5%` = -0.174300938748019, `5%` = -0.199357322617647, `5%` = -0.232838007090428, `5%` = -0.352532667961251, `5%` = -0.22530608055078, `5%` = -0.243190415663175, `5%` = -0.166101667860902)
+c(`95%` = 0.23786658788661, `95%` = 0.178465709918192, `95%` = 0.544725565756189, `95%` = 0.123888229912759, `95%` = 0.128681125268866, `95%` = 0.228097063420235, `95%` = 0.196129167554776, `95%` = 0.155695563618615, `95%` = 0.124477125901656, `95%` = 0.21899037055656, `95%` = 0.212010272021156, `95%` = 0.274559112084226)

r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_14_Genitourinary.tex

@@ -0,0 +1,6 @@
+c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec")
+c(-0.0478593490739956, 0.0120682740554864, -0.0567196164994895, -0.126233602235583, -0.0888817238478321, -0.0319840844510598, -0.055198586240614, -0.0772497283445443, -0.0541521176630153, -0.00310971192809426, -0.00514633902980756, 0.00869308015726206)
+c(`2.5%` = -0.348523219782961, `2.5%` = -0.325741416529682, `2.5%` = -0.537648621232678, `2.5%` = -0.408043017285209, `2.5%` = -0.343971909458341, `2.5%` = -0.293935837353889, `2.5%` = -0.316931983669652, `2.5%` = -0.333641043439836, `2.5%` = -0.343259904607912, `2.5%` = -0.270957011640022, `2.5%` = -0.284012477531593, `2.5%` = -0.263984097470059)
+c(`97.5%` = 0.258199105421347, `97.5%` = 0.337658598779989, `97.5%` = 0.413237173559078, `97.5%` = 0.141325836240884, `97.5%` = 0.154268857189929, `97.5%` = 0.215841474017224, `97.5%` = 0.188621888649733, `97.5%` = 0.163079486802808, `97.5%` = 0.237777766149638, `97.5%` = 0.266687930004995, `97.5%` = 0.274995321113636, `97.5%` = 0.279840596472466)
+c(`5%` = -0.297395770121559, `5%` = -0.263152155503003, `5%` = -0.450348871660559, `5%` = -0.357452526309299, `5%` = -0.298995454954135, `5%` = -0.246714915420225, `5%` = -0.268903804112122, `5%` = -0.285856831702031, `5%` = -0.2933097076324, `5%` = -0.224824888562106, `5%` = -0.234533692066961, `5%` = -0.215840640492215)
+c(`95%` = 0.203709821019864, `95%` = 0.278261424943046, `95%` = 0.329135185773538, `95%` = 0.0975164848465818, `95%` = 0.11201114117772, `95%` = 0.174468036775523, `95%` = 0.148448833659993, `95%` = 0.122321755852338, `95%` = 0.188406047073467, `95%` = 0.218989193656767, `95%` = 0.224205184960909, `95%` = 0.230966741806046)

r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_17_Congential.tex

@@ -0,0 +1,6 @@
+c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec")
+c(-0.0499660268658585, 0.0197148498472504, -0.0421991751741288, -0.13476563953491, -0.0956058294147287, -0.0442534026160268, -0.0662861597218824, -0.0865335314057803, -0.0550797452225853, -0.00252250078632693, -0.00496344785285301, 0.00806097077518296)
+c(`2.5%` = -0.350165746673952, `2.5%` = -0.317756875891385, `2.5%` = -0.518375863151681, `2.5%` = -0.414723657705109, `2.5%` = -0.35057979600666, `2.5%` = -0.306304745881982, `2.5%` = -0.323256686118706, `2.5%` = -0.342841852065351, `2.5%` = -0.345063660691308, `2.5%` = -0.272430725186905, `2.5%` = -0.281159012081347, `2.5%` = -0.262583809560454)
+c(`97.5%` = 0.252568610015387, `97.5%` = 0.349107813430285, `97.5%` = 0.439646865645069, `97.5%` = 0.129542894513277, `97.5%` = 0.145184905982789, `97.5%` = 0.197603443786488, `97.5%` = 0.173229891076501, `97.5%` = 0.153760033992607, `97.5%` = 0.236924146027621, `97.5%` = 0.263988376114375, `97.5%` = 0.271017033043418, `97.5%` = 0.279367210952163)
+c(`5%` = -0.298500654906565, `5%` = -0.257137304097466, `5%` = -0.434007392588596, `5%` = -0.363849520269935, `5%` = -0.30377336167253, `5%` = -0.259427189054046, `5%` = -0.277378887591161, `5%` = -0.29778671716257, `5%` = -0.294243091294835, `5%` = -0.225155935562034, `5%` = -0.233921233193929, `5%` = -0.215580888141684)
+c(`95%` = 0.20049984396355, `95%` = 0.289287710051617, `95%` = 0.353130963560884, `95%` = 0.0859110309743907, `95%` = 0.105692185919512, `95%` = 0.15909647715405, `95%` = 0.13405804651131, `95%` = 0.114598752695363, `95%` = 0.18316007227917, `95%` = 0.218357594565282, `95%` = 0.223576635602984, `95%` = 0.232261579219567)

r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_18_Symptoms, Signs etc..tex

@@ -0,0 +1,6 @@
+c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec")
+c(-0.0486654863146441, 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)

r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_1_Infections & Parasites.tex

@@ -0,0 +1,6 @@
+c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec")
+c(-0.0816317285457012, 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)

r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_2_Neoplasms.tex

@@ -0,0 +1,6 @@
+c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec")
+c(-0.315526037006588, 0.14948178706425, -0.745438348426778, 0.0393966359152392, -0.0457348007757995, 0.064106280747042, -0.0140917847275379, -0.0473660509007094, -0.242704066590673, -0.00295173909211749, -0.101150807225687, -0.0318651891745962)
+c(`2.5%` = -0.642054194627079, `2.5%` = -0.095511016993371, `2.5%` = -1.12441685579914, `2.5%` = -0.194160502625767, `2.5%` = -0.277926578990489, `2.5%` = -0.162654732653132, `2.5%` = -0.246821463107613, `2.5%` = -0.281736932939805, `2.5%` = -0.543673509898535, `2.5%` = -0.271867873121147, `2.5%` = -0.392051441442553, `2.5%` = -0.272496159194591)
+c(`97.5%` = -0.0498087194116947, `97.5%` = 0.422731018753381, `97.5%` = -0.396240166662077, `97.5%` = 0.291104489058118, `97.5%` = 0.186629528620266, `97.5%` = 0.30340806969423, `97.5%` = 0.218889942875867, `97.5%` = 0.182247266485996, `97.5%` = 0.0114150292406248, `97.5%` = 0.265171319133342, `97.5%` = 0.156606469826098, `97.5%` = 0.198395249398707)
+c(`5%` = -0.580798660746861, `5%` = -0.0582870053082083, `5%` = -1.05984134753029, `5%` = -0.157477096682293, `5%` = -0.237601446017153, `5%` = -0.12601793998578, `5%` = -0.207070786292008, `5%` = -0.242358894302708, `5%` = -0.488379411138948, `5%` = -0.226179910621409, `5%` = -0.337956677281885, `5%` = -0.228981038446028)
+c(`95%` = -0.0876108144400803, `95%` = 0.373392398003416, `95%` = -0.449885315105183, `95%` = 0.246925701935553, `95%` = 0.146465971010273, `95%` = 0.261260827352338, `95%` = 0.178110810416068, `95%` = 0.144856415039965, `95%` = -0.0267022930175043, `95%` = 0.218796743068272, `95%` = 0.117095735429865, `95%` = 0.161143364484194)

r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_3_Blood & Immune system.tex

@@ -0,0 +1,6 @@
+c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec")
+c(-0.0505321895701756, 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)

r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_4_Endocrine, Nutritional, and Metabolic.tex

@@ -0,0 +1,6 @@
+c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec")
+c(-0.0585983827703775, -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)

r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_5_Mental & Behavioral.tex

@@ -0,0 +1,6 @@
+c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec")
+c(-0.0774004055701482, 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)

r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_6_Nervous System.tex

@@ -0,0 +1,6 @@
+c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec")
+c(-0.0578472700681722, 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)

r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_7_Eye and Adnexa.tex

@@ -0,0 +1,6 @@
+c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec")
+c(-0.0507912436647474, 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)

r-analysis/output/EffectsOfEnrollmentDelay/betas/parameters_by_group/group_table_9_Circulatory.tex

@@ -0,0 +1,6 @@
+c("Elapsed Duration", "asinh(Competitors USPDC)", "asinh(Generic Brands)", "asinh(High SDI)", "asinh(High-Medium SDI)", "asinh(Low SDI)", "asinh(Low-Medium SDI)", "asinh(Medium SDI)", "status_ANR", "status_EBI", "status_NYR", "status_Rec")
+c(-0.0561236542390722, -0.0174830982131017, -0.114801759701441, -0.0489532404314588, -0.00995833322809683, 0.0764993233097176, 0.0602798840227049, 0.0304789175922322, -0.0491179148659016, -0.0020229135961933, 0.0102255609545677, -0.0121276026193075)
+c(`2.5%` = -0.358192918691032, `2.5%` = -0.360033194654434, `2.5%` = -0.588446720987112, `2.5%` = -0.297868077807792, `2.5%` = -0.240899712415657, `2.5%` = -0.162465078674946, `2.5%` = -0.17414438609744, `2.5%` = -0.201017363333911, `2.5%` = -0.340513734259116, `2.5%` = -0.272921161056328, `2.5%` = -0.264718520415862, `2.5%` = -0.284982548735974)
+c(`97.5%` = 0.245199739323497, `97.5%` = 0.295622079182328, `97.5%` = 0.326892147498506, `97.5%` = 0.210710410280741, `97.5%` = 0.229119750480018, `97.5%` = 0.332077723102122, `97.5%` = 0.312602029054938, `97.5%` = 0.277230334088767, `97.5%` = 0.247596369898235, `97.5%` = 0.269687555821123, `97.5%` = 0.291348978934726, `97.5%` = 0.25166727687992)
+c(`5%` = -0.304624731177692, `5%` = -0.294903363605129, `5%` = -0.498779889626974, `5%` = -0.256277233535303, `5%` = -0.202637633223219, `5%` = -0.125070759586198, `5%` = -0.13517180498734, `5%` = -0.163740873087956, `5%` = -0.29096401042673, `5%` = -0.225192615705392, `5%` = -0.217746444191205, `5%` = -0.235700053008877)
+c(`95%` = 0.193775727317953, `95%` = 0.243010994205006, `95%` = 0.251902327224973, `95%` = 0.165019140778464, `95%` = 0.188705917246998, `95%` = 0.287176225295342, `95%` = 0.267926957380469, `95%` = 0.233147083774771, `95%` = 0.194446427538144, `95%` = 0.222074828728455, `95%` = 0.240749705129402, `95%` = 0.206639994505824)