Merge branch 'main' of https://git.youainti.com/youainti/ClinicalTrialsEstimation

Julia-turing models/Model.jl

@@ -1,62 +0,0 @@
-
-using Turing, StatsFuns
-using Distributions, StatsPlots
-
-using FillArrays
-
-begin #creating synthetic data
-	βs = [1 2 3]'
-	x = Matrix([1:10 1:2:20 1:3:30])
-	X = (x .- mean(x,dims=1)) ./ std(x,dims=1)
-	t = [x for x=1:0.5:5.5]
-	s = rand([1,2],10)
-	σ= [2 3]
-	rand_draw1 = randn(10)
-
-	y = x*βs .+ σ[s]
-end
-
-@model function JointDurationStateModel(
-	DeviationFromExpectedDuration,
-	ConclusionStatus,
-	SnapshotState,
-	CurrentDuration,
-)
-	# get dimensions
-	n,k = size(SnapshotState)
-	
-	#hyperpriors priors
-
-	#Heirarchal parameters
-	#β ~ MvNormal(Fill(0,k),2)
-	η ~ MvNormal(Fill(0,k),2)
-
-	#Direct Priors
-	#σ_DFED ~ filldist(Exponential(1),2) #TODO: check implication of this form
-	
-	#model
-	#μ = SnapshotState * β
-	p = StatsFuns.logistic.(SnapshotState * η)
-	
-	#estimate ConclusionStatus model
-	ConclusionStatus .~ Bernoulli(p)
-	#Estimate DFED model
-	#=
-	for i in eachindex(ConclusionStatus)
-		DeviationFromExpectedDuration ~ Normal(
-			μ[ConclusionStatus[i]],
-			σ_DFED[ConclusionStatus[i]]
-		)
-	end
-	=#
-end
-
-model = JointDurationStateModel(y,s,X,t)
-prior = JointDurationStateModel(fill(missing,size(y)),fill(missing,size(s)),X,t)
-
-chain = sample(model,NUTS(0.85),2000)
-
-
-
-plot(chain)
-

r-analysis/EffectsOfEnrollmentDelay.qmd

@@ -9,6 +9,7 @@ editor: source
 # Setup
 
 ```{r}
+library(knitr)
 library(bayesplot)
 available_mcmc(pattern = "_nuts_")
 library(ggplot2)
@@ -32,6 +33,7 @@ options(mc.cores = parallel::detectCores())
 ```{r}
 ################ Pull data from database ######################
 library(RPostgreSQL)
+host <- 'aact_db-restored-2025-01-07'
 
 driver <- dbDriver("PostgreSQL")
 
@@ -42,7 +44,7 @@ con <- dbConnect(
     user='root',
     password='root',
     dbname='aact_db',
-    host='will-office'
+    host=host
     )
 on.exit(dbDisconnect(con))
 
@@ -59,7 +61,7 @@ select
     ,fdqpe.earliest_date_observed 
     ,fdqpe.elapsed_duration
     ,fdqpe.n_brands as identical_brands
-    ,ntbtu.brand_name_count 
+    ,ntbtu.brand_name_counts 
     ,fdqpe.category_id
     ,fdqpe.final_status
     ,fdqpe.h_sdi_val
@@ -78,7 +80,7 @@ select
     --,fdqpe.l_sdi_u95
     --,fdqpe.l_sdi_l95
 from formatted_data_with_planned_enrollment fdqpe
-    join \"Formularies\".nct_to_brands_through_uspdc ntbtu
+    join \"Formularies\".nct_to_brand_counts_through_uspdc ntbtu
         on fdqpe.nct_id = ntbtu.nct_id 
 order by fdqpe.nct_id, fdqpe.earliest_date_observed 
 ;
@@ -101,7 +103,7 @@ con <- dbConnect(
     user='root',
     password='root',
     dbname='aact_db',
-    host='will-office'
+    host=host
     )
 on.exit(dbDisconnect(con))
 
@@ -127,7 +129,7 @@ query <- dbSendQuery(
         ,fdqpe.earliest_date_observed 
         ,fdqpe.elapsed_duration
         ,fdqpe.n_brands as identical_brands
-        ,ntbtu.brand_name_count 
+        ,ntbtu.brand_name_counts 
         ,fdqpe.category_id
         ,fdqpe.final_status
         ,fdqpe.h_sdi_val
@@ -146,7 +148,7 @@ query <- dbSendQuery(
         --,fdqpe.l_sdi_u95
         --,fdqpe.l_sdi_l95
     from formatted_data_with_planned_enrollment fdqpe
-        join \"Formularies\".nct_to_brands_through_uspdc ntbtu
+        join \"Formularies\".nct_to_brand_counts_through_uspdc ntbtu
             on fdqpe.nct_id = ntbtu.nct_id 
     	join cte 
     		on fdqpe.nct_id = cte.nct_id 
@@ -235,7 +237,7 @@ inherited_cols <- c(
     ,"m_sdi_val"
     ,"lm_sdi_val"
     ,"l_sdi_val"
-    ,"status_NYR"
+    ,"status_NYR"# TODO: may need to remove
     ,"status_EBI"
     ,"status_Rec"
     ,"status_ANR"
@@ -325,6 +327,7 @@ group_mcmc_areas <- function(
         rename=TRUE,
         filter=NULL
         ) {
+    
     #get all parameter names
     params <- get_parameters(stem,class_list)
     
@@ -339,7 +342,7 @@ group_mcmc_areas <- function(
     #create area plot with appropriate title
     p <- mcmc_areas(filtdata,prob = 0.8, prob_outer = 0.95) +
         ggtitle(paste("Parameter distributions for ICD-10 class:",group_name)) +
-        geom_vline(xintercept=0,color="grey",alpha=0.75)
+        geom_vline(xintercept=seq(-2,2,0.5),color="grey",alpha=0.750)  
     
     d <- pivot_longer(filtdata, everything()) |> 
         group_by(name) |> 
@@ -372,7 +375,8 @@ parameter_mcmc_areas <- function(
     parameter_name <- class_list$parameters[parameter_id]
     #create area plot with appropriate title
     p <- mcmc_areas(filtdata,prob = 0.8, prob_outer = 0.95) +
-        ggtitle(parameter_name,"Parameter Distribution")
+        ggtitle(parameter_name,"Parameter Distribution") +
+        geom_vline(xintercept=seq(-2,2,0.5),color="grey",alpha=0.750)  
     
     d <- pivot_longer(filtdata, everything()) |> 
         group_by(name) |> 
@@ -446,6 +450,109 @@ category_count <- group_trials_by_category |> group_by(category_id) |> count()
 ```
 
 
+```{r}
+################################# DATA EXPLORATION ############################
+driver <- dbDriver("PostgreSQL")
+
+con <- dbConnect(
+    driver,
+    user='root',
+    password='root',
+    dbname='aact_db',
+    host=host
+    )
+#Plot histogram of count of snapshots
+df3 <- dbGetQuery(
+    con,
+    "select nct_id,final_status,count(*) from formatted_data_with_planned_enrollment fdwpe 
+    group by nct_id,final_status ;"
+    )
+#df3 <- fetch(query3, n = -1)
+
+ggplot(data=df3, aes(x=count, fill=final_status)) + 
+    geom_histogram(binwidth=1) +
+    ggtitle("Histogram of snapshots per trial (matched trials)") +
+    xlab("Snapshots per trial")
+ggsave("./Images/HistSnapshots.png")
+
+#Plot duration for terminated vs completed
+df4 <- dbGetQuery(
+    con,
+    "
+    select 
+        nct_id, 
+        start_date , 
+        primary_completion_date, 
+        overall_status ,
+        primary_completion_date - start_date as duration
+    from ctgov.studies s 
+    where nct_id in (select distinct nct_id from http.download_status ds)
+    ;"
+    )
+#df4 <- fetch(query4, n = -1)
+
+ggplot(data=df4, aes(x=duration,fill=overall_status)) +
+    geom_histogram()+
+    ggtitle("Histogram of trial durations") +
+    xlab("duration")+
+    facet_wrap(~overall_status)
+ggsave("./Images/HistTrialDurations_Faceted.png")
+
+df5 <- dbGetQuery(
+    con,
+    "
+    with cte1 as (
+    select 
+        nct_id, 
+        start_date , 
+        primary_completion_date, 
+        overall_status ,
+        primary_completion_date - start_date as duration
+    from ctgov.studies s 
+    where nct_id in (select distinct nct_id from http.download_status ds)
+    ), cte2 as (
+    select nct_id,count(*) as snapshot_count from formatted_data_with_planned_enrollment fdwpe
+    group by nct_id
+    )
+    select a.nct_id, a.overall_status, a.duration,b.snapshot_count
+    from cte1 as a
+        join cte2 as b
+            on a.nct_id=b.nct_id
+    ;"
+    )
+df5$overall_status <- as.factor(df5$overall_status)
+
+ggplot(data=df5, aes(x=duration,y=snapshot_count,color=overall_status)) +
+    geom_jitter() +
+    ggtitle("Comparison of duration, status, and snapshot_count") +
+    xlab("duration") +
+    ylab("snapshot count") 
+ggsave("./Images/SnapshotsVsDurationVsTermination.png")
+
+dbDisconnect(con)
+
+#get number of trials and snapshots in each category
+group_trials_by_category <- as.data.frame(aggregate(category_id ~ nct_id, df, max))
+group_trials_by_category <- as.data.frame(group_trials_by_category)
+
+ggplot(data = group_trials_by_category, aes(x=category_id)) +
+    geom_bar(binwidth=1,color="black",fill="seagreen") +
+    scale_x_continuous(breaks=scales::pretty_breaks(n=22)) + 
+    labs(
+        title="bar chart of trial categories"
+        ,x="Category ID"
+        ,y="Count"
+    )
+ggsave("./Images/CategoryCounts.png")
+    
+
+
+summary(df5)
+
+cor(df5$duration,df5$snapshot_count)
+sum(df5$snapshot_count)
+```
+
 
 
 
@@ -457,6 +564,76 @@ category_count <- group_trials_by_category |> group_by(category_id) |> count()
 print(fit)
 ```
 
+# Parameter Distributions
+
+
+```{r}
+#g1 <- group_mcmc_areas("beta",beta_list,fit,1)
+
+
+gx <- c()
+
+#grab parameters for every category with more than 8 observations
+for (i in category_count$category_id[category_count$n >= 8]) {
+    print(i)
+    
+    #Print parameter distributions
+    gi <- group_mcmc_areas("beta",beta_list,fit,i) #add way to filter groups
+    ggsave(
+        paste0("./Images/DirectEffects/Parameters/group_",i,"_",gi$name,".png")
+        ,plot=gi$plot
+        )
+    gx <- c(gx,gi)
+
+    #Get Quantiles and means for parameters
+    table <- xtable(gi$quantiles,
+      floating=FALSE
+      ,latex.environments = NULL
+      ,booktabs = TRUE
+      ,zap=getOption("digits")
+      )
+    write_lines(table,paste0("./latex_output/DirectEffects/group_",gi$name,".tex"))
+}
+```
+
+
+
+```{r}
+px <- c()
+
+
+for (i in c(1,2,3,9,10,11,12)) {
+    
+    #Print parameter distributions
+    pi <- parameter_mcmc_areas("beta",beta_list,fit,i) #add way to filter groups
+    ggsave(
+        paste0("./Images/DirectEffects/Parameters/parameters_",i,"_",pi$name,".png")
+        ,plot=pi$plot
+        )
+    px <- c(px,pi)
+
+    #Get Quantiles and means for parameters
+    table <- xtable(pi$quantiles,
+      floating=FALSE
+      ,latex.environments = NULL
+      ,booktabs = TRUE
+      ,zap=getOption("digits")
+      )
+    write_lines(table,paste0("./latex_output/DirectEffects/parameters_",i,"_",pi$name,".tex"))
+    
+}
+```
+
+Note these have 95% outer CI and 80% inner (shaded)
+
+
+
+
+
+```{r}
+print(px[4]$plot + px[7]$plot)
+ggsave("./Images/DirectEffects/Parameters/2+3_generic_and_uspdc.png")
+```
 
 
 # Counterfactuals
@@ -579,19 +756,35 @@ ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/default_p_generic_interv
 
 
 ```{r}
+get_category_count <- function(tbl, id) {
+  result <- tbl$n[tbl$category_id == id]
+  if(length(result) == 0) 0 else result
+}
+
+category_names <- sapply(1:length(beta_list$groups), 
+    function(i) sprintf("ICD-10 #%d: %s (n=%d)", 
+                       i, 
+                       beta_list$groups[i],
+                       get_category_count(category_count, i)))
 
+```
 
+```{r}
 pddf_ib <- data.frame(extract(generated_ib, pars="predicted_difference")$predicted_difference) |>
-    pivot_longer(X1:X169)
+    pivot_longer(X1:X168) #CHANGE_NOTE: moved from X169 to X168
 
 #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$groups[i])
+pddf_ib["category_name"] <- sapply(
+    pddf_ib$category, 
+    function(i) category_names[i]
+    )
+  
 
 
 ggplot(pddf_ib, aes(x=value,)) +
-    geom_density(bins=100) +
+    geom_density(adjust=1/5) +
     labs(
         title = "Distribution of predicted differences"
         ,subtitle = "Intervention: Delay close of enrollment"
@@ -599,14 +792,15 @@ ggplot(pddf_ib, aes(x=value,)) +
         ,y = "Probability Density"
     ) + 
     geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") 
-ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/p_generic_intervention_distdiff_styled.png")
+    #todo: add median, mean, 40/60 quantiles as well as 
+ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/p_delay_intervention_distdiff_styled.png")
 
 ggplot(pddf_ib, aes(x=value,)) +
-    geom_density(bins=100) +
+    geom_density(adjust=1/5) +
     facet_wrap(
         ~factor(
             category_name, 
-            levels=beta_list$groups
+            levels=category_names
             )
         , labeller = label_wrap_gen(multi_line = TRUE)
         , ncol=4) +
@@ -618,57 +812,185 @@ ggplot(pddf_ib, aes(x=value,)) +
     ) + 
     geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") +
     theme(strip.text.x = element_text(size = 8))
-ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/p_generic_intervention_distdiff_by_group.png")
+ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/p_delay_intervention_distdiff_by_group.png")
 
 ggplot(pddf_ib, aes(x=value,)) +
-    geom_histogram(bins=100) +
+    geom_histogram(bins=300) +
     facet_wrap(
         ~factor(
             category_name, 
-            levels=beta_list$groups
+            levels=category_names
             )
         , labeller = label_wrap_gen(multi_line = TRUE)
-        , ncol=5) +
+        , ncol=4) +
     labs(
         title = "Histogram of predicted differences | By Group"
         ,subtitle = "Intervention: Delay close of enrollment"
         ,x = "Difference in probability due to intervention"
         ,y = "Predicted counts"
     ) + 
-    #xlim(-0.25,0.1) +
     geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") +
     theme(strip.text.x = element_text(size = 8))
-ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/p_generic_intervention_histdiff_by_group.png")
+ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/p_delay_intervention_histdiff_by_group.png")
 ```
 
-Get the probability of increase over probability of a decrease
 
 ```{r}
-mean(counterfact_predicted_ib$predicted_difference)
+p3 <- ggplot(pddf_ib, aes(x=value,)) +
+    geom_histogram(bins=500) +
+    labs(
+        title = "Distribution of predicted differences"
+        ,subtitle = "Intervention: Delay close of enrollment"
+        ,x = "Difference in probability due to intervention"
+        ,y = "Probability Density"
+        ,caption = "Vertical marks: 5/10/25/50/75/90/95th percentiles. Dot shows mean."
+    ) + 
+    geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") 
+
+stats <- list(
+ p5 = quantile(pddf_ib$value, 0.05),
+ p10 = quantile(pddf_ib$value, 0.10),
+ q1 = quantile(pddf_ib$value, 0.25),
+ med = median(pddf_ib$value),
+ mean = mean(pddf_ib$value),
+ q3 = quantile(pddf_ib$value, 0.75),
+ p90 = quantile(pddf_ib$value, 0.90),
+ p95 = quantile(pddf_ib$value, 0.95),
+ max_height = max(ggplot_build(p3)$data[[1]]$count),
+ y_offset = -max(ggplot_build(p3)$data[[1]]$count) * 0.05
+)
+
+p3 + 
+ # Box
+ geom_segment(data = data.frame(
+   x = c(stats$q1, stats$q3, stats$med),
+   xend = c(stats$q1, stats$q3, stats$med),
+   y = rep(stats$y_offset, 3), 
+   yend = rep(stats$y_offset * 2, 3)
+ ), aes(x = x, xend = xend, y = y, yend = yend)) +
+ geom_segment(data = data.frame(
+   x = rep(stats$q1, 2),
+   xend = rep(stats$q3, 2),
+   y = c(stats$y_offset, stats$y_offset * 2),
+   yend = c(stats$y_offset, stats$y_offset * 2)
+ ), aes(x = x, xend = xend, y = y, yend = yend)) +
+ # Inner whiskers (Q1->P10, Q3->P90)
+ geom_segment(data = data.frame(
+   x = c(stats$q1, stats$q3),
+   xend = c(stats$p10, stats$p90),
+   y = rep(stats$y_offset * 1.5, 2),
+   yend = rep(stats$y_offset * 1.5, 2)
+ ), aes(x = x, xend = xend, y = y, yend = yend)) +
+ # Crossbars at P10/P90
+ geom_segment(data = data.frame(
+   x = c(stats$p10, stats$p90),
+   xend = c(stats$p10, stats$p90),
+   y = stats$y_offset * 1.3,
+   yend = stats$y_offset * 1.7
+ ), aes(x = x, xend = xend, y = y, yend = yend)) +
+ # Outer whiskers (P10->P5, P90->P95)
+ geom_segment(data = data.frame(
+   x = c(stats$p10, stats$p90),
+   xend = c(stats$p5, stats$p95),
+   y = rep(stats$y_offset * 1.5, 2),
+   yend = rep(stats$y_offset * 1.5, 2)
+ ), aes(x = x, xend = xend, y = y, yend = yend)) +
+ # Crossbars at P5/P95
+ geom_segment(data = data.frame(
+   x = c(stats$p5, stats$p95),
+   xend = c(stats$p5, stats$p95),
+   y = stats$y_offset * 1.3,
+   yend = stats$y_offset * 1.7
+ ), aes(x = x, xend = xend, y = y, yend = yend)) +
+ # Mean dot
+ geom_point(data = data.frame(
+   x = stats$mean,
+   y = stats$y_offset * 1.5
+ ), aes(x = x, y = y))
+ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/p_delay_intervention_histdiff_boxplot.png")
 ```
-Thus adding a Delay close of enrollment increases the probability of termination by 16.72% on average for
-the snapshots investigated.
 
+```{r}
+ ggplot(pddf_ib, aes(x=value)) +
+    stat_ecdf(geom='step') +
+    labs(
+        title = "Cumulative distribution of predicted differences",
+        subtitle = "Intervention: Delay close of enrollment",
+        x = "Difference in probability of termination due to intervention",
+        y = "Cumulative Proportion"
+    ) 
+
+ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/p_delay_intervention_cumulative_distdiff.png")
+```
 
 
+Get the % of differences in the spike around zero
+```{r}
+# get values around and above/below spike
+width <- 0.02
+spike_band_centered_zero <- mean( pddf_ib$value >= -width/2 & pddf_ib$value <= width/2)
+above_spike_band <- mean( pddf_ib$value >= width/2)
+below_spike_band <- mean( pddf_ib$value <= -width/2)
+
+# get mass above and mass below zero
+mass_below_zero <- mean( pddf_ib$value <= 0)
+```
+Looking at the spike around zero, we find that `r spike_band_centered_zero*100`% 
+of the probability mass is contained within the band from 
+[`r -width*100/2`,`r width*100/2`].
+Additionally, there was `r above_spike_band*100`% of the probability above that 
+-- representing those with a general increase in the probability of termination --
+and `r below_spike_band*100`% of the probability mass below the band
+-- representing a decrease in the probability of termination.
+
+On average, if you keep the trial open instead of closing it,
+`r mass_below_zero`% of trials will see a decrease in the probability of termination,
+but, due to the high increase in probability of termination given termination was increased,
+the mean probability of termination increases by `r stats$mean`.
+
+```{r}
+# 5%-iles
+
+summary(pddf_ib$value)
+
+# Create your quantiles
+quants <- quantile(pddf_ib$value, probs = seq(0,1,0.05), type=4)
+
+# Convert to a data frame
+quant_df <- data.frame(
+  Percentile = names(quants),
+  Value = quants
+)
+kable(quant_df)
+```
+
+There seems to be some trials that are highly suceptable to this enrollment delay. Specifically, there were some 
+
 ```{r}
 n = length(counterfact_predicted_ib$p_predicted_intervention)
-mean(rbinom(n,1,as.vector(counterfact_predicted_ib$p_predicted_intervention)))
-mean(rbinom(n,1,as.vector(counterfact_predicted_ib$p_predicted_default)))
+k = 100
+simulated_terminations_intervention <- mean(rbinom(n,k,as.vector(counterfact_predicted_ib$p_predicted_intervention)))
+simulated_terminations_base <-mean(rbinom(n,k,as.vector(counterfact_predicted_ib$p_predicted_default)))
+
+simulated_percentages <- (simulated_terminations_intervention - simulated_terminations_base)/k
 ```
 
+The simulation above shows that this results in a percentage-point increase of about 
+`r simulated_percentages * 100`.
+
 
 
 
 # Diagnostics
 
 ```{r}
-#| eval: false
+#| eval: true
 #trace plots
 plot(fit, pars=c("mu"), plotfun="trace")
+ggsave("./EffectsOfEnrollmentDelay/Images/diagnostics/trace_plot_mu.png")
 
 
-for (i in 1:4) {
+for (i in 1:3) {
     print(
         mcmc_rank_overlay(
         fit, 
@@ -682,14 +1004,18 @@ for (i in 1:4) {
         )+  legend_move("top") +
              scale_colour_ghibli_d("KikiMedium")
     )
+    mu_range <- paste0(4*i-3,"-",4*i)
+    filename <- paste0("./EffectsOfEnrollmentDelay/Images/diagnostics/trace_rank_plot_mu_",mu_range,".png")
+    ggsave(filename)
 }
 ```
 
 ```{r}
-#| eval: false
+#| eval: true
 plot(fit, pars=c("sigma"), plotfun="trace")
+ggsave("./EffectsOfEnrollmentDelay/Images/diagnostics/traceplot_sigma.png")
 
-for (i in 1:4) {
+for (i in 1:3) {
     print(
         mcmc_rank_overlay(
         fit, 
@@ -703,11 +1029,14 @@ for (i in 1:4) {
         )+  legend_move("top") +
              scale_colour_ghibli_d("KikiMedium")
     )
+    sigma_range <- paste0(4*i-3,"-",4*i)
+    filename <- paste0("./EffectsOfEnrollmentDelay/Images/diagnostics/trace_rank_plot_sigma_",sigma_range,".png")
+    ggsave(filename)
 }
 ```
 
 ```{r}
-#| eval: false
+#| eval: true
 #other diagnostics
 logpost <- log_posterior(fit)
 nuts_prmts <- nuts_params(fit)
@@ -716,15 +1045,16 @@ posterior <- as.array(fit)
 ```
 
 ```{r}
-#| eval: false
+#| eval: true
 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)
+ggsave("./EffectsOfEnrollmentDelay/Images/diagnostics/parcoord_mu.png")
 ```
 
 ```{r}
-#| eval: false
-for (i in 1:4) {
+#| eval: true
+for (i in 1:3) {
     mus = sapply(3:0, function(j) paste0("mu[",4*i-j ,"]"))
     print(
         mcmc_pairs(
@@ -737,6 +1067,9 @@ for (i in 1:4) {
             off_diag_args = list(size = 0.75)
         )
     )
+    mu_range <- paste0(4*i-3,"-",4*i)
+    filename <- paste0("./EffectsOfEnrollmentDelay/Images/diagnostics/correlation_plot_mu_",mu_range,".png")
+    ggsave(filename)
 }
 
 
@@ -744,14 +1077,15 @@ for (i in 1:4) {
 ```
 
 ```{r}
-#| eval: false
+#| eval: true
 mcmc_parcoord(posterior,regex_pars = "sigma", np=nuts_prmts, alpha=0.05)
+ggsave("./EffectsOfEnrollmentDelay/Images/diagnostics/parcoord_sigma.png")
 ```
 
 ```{r}
-#| eval: false
+#| eval: true
 
-for (i in 1:4) {
+for (i in 1:3) {
     params = sapply(3:0, function(j) paste0("sigma[",4*i-j ,"]"))
     print(
         mcmc_pairs(
@@ -764,14 +1098,17 @@ for (i in 1:4) {
             off_diag_args = list(size = 0.75)
         )
     )
+    sigma_range <- paste0(4*i-3,"-",4*i)
+    filename <- paste0("./EffectsOfEnrollmentDelay/Images/diagnostics/correlation_plot_sigma_",sigma_range,".png")
+    ggsave(filename)
 }
 ```
 
 
 ```{r}
-#| eval: false
+#| eval: true
 for (k in 1:22) {
-for (i in 1:4) {
+for (i in 1:3) {
     params = sapply(3:0, function(j) paste0("beta[",k,",",4*i-j ,"]"))
     print(
         mcmc_pairs(
@@ -784,6 +1121,10 @@ for (i in 1:4) {
             off_diag_args = list(size = 0.75)
         )
     )
+    
+    beta_range <- paste0("k_",k,"_i_",4*i-3,"-",4*i)
+    filename <- paste0("./EffectsOfEnrollmentDelay/Images/diagnostics/correlation_plot_beta_",beta_range,".png")
+    ggsave(filename)
 }}
 ```
 

r-analysis/EffectsOfMarketConditions_no_enrollment_status.qmd

@@ -1,850 +0,0 @@
----
-title: "The Effects of market conditions on recruitment and completion of clinical trials"
-author: "Will King"
-format: html
-editor: source
----
-
-
-# Setup
-
-```{r}
-library(bayesplot)
-available_mcmc(pattern = "_nuts_")
-library(ggplot2)
-library(patchwork)
-library(tidyverse)
-library(rstan)
-library(tidyr)
-library(ghibli)
-#Resources: https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started
-
-#save unchanged models instead of recompiling
-rstan_options(auto_write = TRUE)
-#allow for multithreaded sampling
-options(mc.cores = parallel::detectCores())
-
-#test installation, shouldn't get any errors
-#example(stan_model, package = "rstan", run.dontrun = TRUE)
-```
-
-```{r}
-################ Pull data from database ######################
-library(RPostgreSQL)
-
-driver <- dbDriver("PostgreSQL")
-
-get_data <- function(driver) {
-
-con <- dbConnect(
-    driver,
-    user='root',
-    password='root',
-    dbname='aact_db',
-    host='will-office'
-    )
-on.exit(dbDisconnect(con))
-
-query <- dbSendQuery(
-    con,
-#    "select * from formatted_data_with_planned_enrollment;"
-"
-select 
-    fdqpe.nct_id
-    --,fdqpe.start_date
-    --,fdqpe.current_enrollment
-    --,fdqpe.enrollment_category
-    ,fdqpe.current_status 
-    ,fdqpe.earliest_date_observed 
-    ,fdqpe.elapsed_duration
-    ,fdqpe.n_brands as identical_brands
-    ,ntbtu.brand_name_count 
-    ,fdqpe.category_id
-    ,fdqpe.final_status
-    ,fdqpe.h_sdi_val
-    --,fdqpe.h_sdi_u95
-    --,fdqpe.h_sdi_l95
-    ,fdqpe.hm_sdi_val
-    --,fdqpe.hm_sdi_u95
-    --,fdqpe.hm_sdi_l95
-    ,fdqpe.m_sdi_val
-    --,fdqpe.m_sdi_u95
-    --,fdqpe.m_sdi_l95
-    ,fdqpe.lm_sdi_val
-    --,fdqpe.lm_sdi_u95
-    --,fdqpe.lm_sdi_l95
-    ,fdqpe.l_sdi_val
-    --,fdqpe.l_sdi_u95
-    --,fdqpe.l_sdi_l95
-from formatted_data_with_planned_enrollment fdqpe
-    join \"Formularies\".nct_to_brands_through_uspdc ntbtu
-        on fdqpe.nct_id = ntbtu.nct_id 
-order by fdqpe.nct_id, fdqpe.earliest_date_observed 
-;
-"
-    )
-df <- fetch(query, n = -1)
-df <- na.omit(df)
-
-query2 <-dbSendQuery(con,"select count(*) from \"DiseaseBurden\".icd10_categories ic where \"level\"=1;")
-n_categories <- fetch(query2, n = -1)
-
-return(list(data=df,ncat=n_categories))
-}
-
-d <- get_data(driver)
-df <- d$data
-n_categories <- d$ncat
-
-
-
-
-################ Format Data ###########################
-
-data_formatter <- function(df) {
-
-x <- df["category_id"]
-x["identical_brands"] <- asinh(df$identical_brands)
-x["brand_name_counts"] <- asinh(df$brand_name_count)
-x["h_sdi_val"] <- asinh(df$h_sdi_val)
-x["hm_sdi_val"] <- asinh(df$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$l_sdi_val)
-
-
-
-y <- ifelse(df["final_status"]=="Terminated",1,0)
-
-
-
-return(list(x=x,y=y))
-}
-
-train <- data_formatter(df)
-
-categories <- df$category_id
-
-x <- train$x
-y <- train$y
-x$category_id <- NULL
-```
-
-
-
-### Intervention: Adding a single competitor
-```{r}
-inherited_cols <- c(
-    "identical_brands"
-    ,"brand_name_counts"
-    ,"h_sdi_val"
-    ,"hm_sdi_val"
-    ,"m_sdi_val"
-    ,"lm_sdi_val"
-    ,"l_sdi_val"
-)
-```
-
-
-
-#### Generics
-
-```{r}
-#generics intervention
-brand_intervention_ib <- x[inherited_cols]
-brand_intervention_ib["identical_brands"] <- asinh(sinh(x$identical_brands)+1) #add a single generic brand
-```
-
-```{r}
-counterfact_marketing_ib <- list(
-    D = ncol(x),#
-    N = nrow(x),
-    L = n_categories$count,
-    y = as.vector(y),
-    ll = as.vector(categories),
-    x = as.matrix(x),
-    mu_mean = 0,
-    mu_stdev = 0.05,
-    sigma_shape = 4,
-    sigma_rate = 20,
-    Nx = nrow(x),
-    llx = as.vector(categories),
-    counterfact_x_tilde = as.matrix(brand_intervention_ib),
-    counterfact_x = as.matrix(x)
-)
-```
-
-### USP DC
-
-
-```{r}
-#formulary intervention
-brand_intervention_bnc <- x[inherited_cols]
-brand_intervention_bnc["brand_name_counts"] <- asinh(sinh(x$brand_name_counts)+1) #add a single formulary competitor brand
-```
-
-```{r}
-counterfact_marketing_bnc <- list(
-    D = ncol(x),#
-    N = nrow(x),
-    L = n_categories$count,
-    y = as.vector(y),
-    ll = as.vector(categories),
-    x = as.matrix(x),
-    mu_mean = 0,
-    mu_stdev = 0.05,
-    sigma_shape = 4,
-    sigma_rate = 20,
-    Nx = nrow(x),
-    llx = as.vector(categories),
-    counterfact_x_tilde = as.matrix(brand_intervention_bnc),
-    counterfact_x = as.matrix(x)
-)
-```
-
-
-# Fit Model
-
-
-```{r}
-################################# FIT MODEL #########################################
-
-
-
-fit <- stan(
-    file='Hierarchal_Logistic.stan', 
-    data = counterfact_marketing_ib,
-    chains = 4,
-    iter = 5000,
-    seed = 11021585
-    )
-```
-
-
-```{r}
-generated_bi <- gqs(
-    fit@stanmodel,
-    data=counterfact_marketing_ib,
-    draws=as.matrix(fit),
-    seed=11021585
-    )
-```
-
-## Priors
-
-```{r}
-#| eval: false
-hist(as.vector(extract(generated_bi, pars="p_prior")$p_prior)) 
-hist(as.vector(extract(generated_bi, pars="mu_prior")$mu_prior), )
-hist(as.vector(extract(generated_bi, pars="sigma_prior")$sigma_prior))
-```
-
-```{r}
-df_ib_p <- data.frame(
-    p_prior=as.vector(extract(generated_bi, pars="p_prior")$p_prior)
-    ,p_predicted = as.vector(extract(generated_bi, pars="p_predicted")$p_predicted)
-)
-
-df_ib_prior <- data.frame(
-    mu_prior = as.vector(extract(generated_bi, pars="mu_prior")$mu_prior)
-    ,sigma_prior = as.vector(extract(generated_bi, pars="sigma_prior")$sigma_prior)
-)
-
-#p_prior
-ggplot(df_ib_p, aes(x=p_prior)) +
-    geom_density() + 
-    labs(
-        title="Implied Prior Distribution P"
-        ,subtitle=""
-        ,x="Probability Domain 'p'"
-        ,y="Probability Density"
-    )
-ggsave("./Images/TotalEffects/prior_p.png")
-
-#p_posterior
-ggplot(df_ib_p, aes(x=p_predicted)) +
-    geom_density() + 
-    labs(
-        title="Implied Posterior Distribution P"
-        ,subtitle=""
-        ,x="Probability Domain 'p'"
-        ,y="Probability Density"
-    )
-ggsave("./Images/TotalEffects/posterior_p.png")
-
-#mu_prior
-ggplot(df_ib_prior) +
-    geom_density(aes(x=mu_prior)) + 
-    labs(
-        title="Prior - Mu"
-        ,subtitle="same prior for all Mu values"
-        ,x="Mu"
-        ,y="Probability"
-    )
-ggsave("./Images/TotalEffects/prior_mu.png")
-
-#sigma_posterior
-ggplot(df_ib_prior) +
-    geom_density(aes(x=sigma_prior)) + 
-    labs(
-        title="Prior - Sigma"
-        ,subtitle="same prior for all Sigma values"
-        ,x="Sigma"
-        ,y="Probability"
-    )
-ggsave("./Images/TotalEffects/prior_sigma.png")
-```
-
-
-
-```{r}
-check_hmc_diagnostics(fit)
-#hist(as.vector(extract(generated_bi, pars="p_predicted")$p_predicted))
-```
-
-
-
-
-
-
-# Diagnostics
-
-```{r}
-#| eval: false
-#trace plots
-plot(fit, pars=c("mu"), plotfun="trace")
-
-
-mcmc_rank_overlay(
-fit, 
-pars=sapply(1:7, function(i) paste0("mu[",i,"]"))
-,n_bins=100
-)+  legend_move("top") +
-     scale_colour_ghibli_d("KikiMedium")
-
-```
-
-```{r}
-#| eval: false
-plot(fit, pars=c("sigma"), plotfun="trace")
-
-
-mcmc_rank_overlay(
-fit, 
-pars=sapply(1:7, function(i) paste0("sigma[",i,"]"))
-,n_bins=100
-)+  legend_move("top") +
-     scale_colour_ghibli_d("KikiMedium")
-```
-
-```{r}
-#| eval: false
-#other diagnostics
-logpost <- log_posterior(fit)
-nuts_prmts <- nuts_params(fit)
-posterior <- as.array(fit)
-
-```
-
-```{r}
-#| eval: false
-color_scheme_set("darkgray")
-div_style <- parcoord_style_np(div_color = "green", div_size = 0.05, div_alpha = 0.4)
-mcmc_parcoord(posterior, regex_pars = "mu", np=nuts_prmts, np_style = div_style, alpha = 0.05)
-```
-
-```{r}
-#| eval: false
-mus = sapply(1:7, function(j) paste0("mu[",j ,"]"))
-mcmc_pairs(
-    posterior,
-    np = nuts_prmts,
-    pars=c(
-        mus,
-        "lp__"
-    ),
-    off_diag_args = list(size = 0.75)
-)
-
-
-
-```
-
-```{r}
-#| eval: false
-mcmc_parcoord(posterior,regex_pars = "sigma", np=nuts_prmts, alpha=0.05)
-```
-
-```{r}
-#| eval: false
-sigmas = sapply(1:7, function(j) paste0("sigma[",j ,"]"))
-mcmc_pairs(
-    posterior,
-    np = nuts_prmts,
-    pars=c(
-        sigmas,
-        "lp__"
-    ),
-    off_diag_args = list(size = 0.75)
-)
-```
-
-
-```{r}
-#| eval: false
-#for (k in 1:22) {
-#    params = sapply(1:7, function(j) paste0("beta[",k,",",j ,"]"))
-#    print(
-#        mcmc_pairs(
-#            posterior,
-#            np = nuts_prmts,
-#            pars=c(
-#                params,
-#                "lp__"
-#            ),
-#            off_diag_args = list(size = 0.75)
-#        )
-#    )
-#}
-```
-
-
-# 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(
-        # brands
-        `1`="asinh(Generic Brands)",
-        `2`="asinh(Competitors USPDC)",
-        # population
-        `3`="asinh(High SDI)",
-        `4`="asinh(High-Medium SDI)",
-        `5`="asinh(Medium SDI)",
-        `6`="asinh(Low-Medium SDI)",
-        `7`="asinh(Low SDI)"
-    )
-)
-
-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)
-ggsave("./Images/TotalEffects/Parameters/01_generics.png")
-p2 <- parameter_mcmc_areas("beta",beta_list,fit,2)
-ggsave("./Images/TotalEffects/Parameters/02_uspdc.png")
-#p3 <- parameter_mcmc_areas("beta",beta_list,fit,3)
-#p4 <- parameter_mcmc_areas("beta",beta_list,fit,4)
-#p5 <- parameter_mcmc_areas("beta",beta_list,fit,5)
-#p6 <- parameter_mcmc_areas("beta",beta_list,fit,6)
-#p7 <- parameter_mcmc_areas("beta",beta_list,fit,7)
-
-```
-
-Note these have 95% outer CI and 80% inner (shaded)
-
-
-    1) "asinh(Generic Brands)",
-    2) "asinh(Competitors USPDC)",
-    3) "asinh(High SDI)",
-    4) "asinh(High-Medium SDI)",
-    5) "asinh(Medium SDI)",
-    6) "asinh(Low-Medium SDI)",
-    7) "asinh(Low SDI)",
-
-of interest 
-- p1 + p2
-
-
-```{r}
-p1 + p2
-ggsave("./Images/TotalEffects/Parameters/1&2_generics_and_uspdc.png")
-```
-
-
-
-# Posterior Prediction
-
-
-
-
-## Distribution of Predicted Differences 
-
-
-
-### Intervention: Adding a single competitor
-
-
-#### Generics
-
-
-
-
-
-```{r}
-#| eval: false
-#TODO: Convert to ggplot, stabilize y axis
-hist(as.vector(extract(generated_bi, pars="p_predicted_default")$p_predicted_default))
-hist(as.vector(extract(generated_bi, pars="p_predicted_intervention")$p_predicted_intervention))
-hist(as.vector(extract(generated_bi, pars="predicted_difference")$predicted_difference))
-```
-
-
-```{r}
-counterfact_predicted_ib <- data.frame(
-    p_predicted_default = as.vector(extract(generated_bi, pars="p_predicted_default")$p_predicted_default)
-    ,p_predicted_intervention = as.vector(extract(generated_bi, pars="p_predicted_intervention")$p_predicted_intervention)
-    ,predicted_difference = as.vector(extract(generated_bi, pars="predicted_difference")$predicted_difference)
-)
-```
-
-
-```{r}
-ggplot(counterfact_predicted_ib, aes(x=p_predicted_default)) +
-    geom_density() + 
-    labs(
-        title="Predicted Distribution of 'p'"
-        ,subtitle="Intervention: None"
-        ,x="Probability Domain 'p'"
-        ,y="Probability Density"
-    )
-ggsave("./Images/TotalEffects/default_p_generic_intervention_base.png")
-
-ggplot(counterfact_predicted_ib, aes(x=p_predicted_intervention)) +
-    geom_density() + 
-    labs(
-        title="Predicted Distribution of 'p'"
-        ,subtitle="Intervention: Add a single generic competitor"
-        ,x="Probability Domain 'p'"
-        ,y="Probability Density"
-    )
-ggsave("./Images/TotalEffects/default_p_generic_intervention_interv.png")
-
-ggplot(counterfact_predicted_ib, aes(x=predicted_difference)) +
-    geom_density() + 
-    labs(
-        title="Predicted Distribution of differences 'p'"
-        ,subtitle="Intervention: Add a single generic competitor"
-        ,x="Difference in 'p' under treatment"
-        ,y="Probability Density"
-    )
-ggsave("./Images/TotalEffects/default_p_generic_intervention_distdiff.png")
-```
-
-
-```{r}
-pddf_ib <- data.frame(extract(generated_bi, pars="predicted_difference")$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$groups[i])
-```
-
-
-```{r}
-ggplot(pddf_ib, aes(x=value,)) +
-    geom_density() +
-    labs(
-        title = "Distribution of predicted differences"
-        ,subtitle = "Intervention: add a single generic competitor"
-        ,x = "Difference in probability due to intervention"
-        ,y = "Probability Density"
-    ) + 
-    geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") 
-ggsave("./Images/TotalEffects/p_generic_intervention_distdiff_styled.png")
-
-ggplot(pddf_ib, aes(x=value,)) +
-    geom_density() +
-    facet_wrap(
-        ~factor(
-            category_name, 
-            levels=beta_list$groups
-            )
-        , labeller = label_wrap_gen(multi_line = TRUE)
-        , ncol=5
-        ,scales="free"
-        ) +
-    xlim(-1,1)+
-    labs(
-        title = "Distribution of predicted differences | By Group"
-        ,subtitle = "Intervention: add a single generic competitor"
-        ,x = "Difference in probability due to intervention"
-        ,y = "Probability Density"
-    ) + 
-    geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") +
-    theme(strip.text.x = element_text(size = 8))
-ggsave("./Images/TotalEffects/p_generic_intervention_distdiff_by_group.png")
-
-ggplot(pddf_ib, aes(x=value,)) +
-    geom_histogram(bins=100) +
-    facet_wrap(
-        ~factor(
-            category_name, 
-            levels=beta_list$groups
-            )
-        , labeller = label_wrap_gen(multi_line = TRUE)
-        , ncol=5) +
-    labs(
-        title = "Histogram of predicted differences | By Group"
-        ,subtitle = "Intervention: add a single generic competitor"
-        ,x = "Difference in probability due to intervention"
-        ,y = "Predicted counts"
-    ) + 
-    #xlim(-0.25,0.1) +
-    geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") +
-    theme(strip.text.x = element_text(size = 8))
-ggsave("./Images/TotalEffects/p_generic_intervention_histdiff_by_group.png")
-```
-
-
-
-
-
-#### USP DC
-
-
-```{r}
-generated_bnc <- gqs(
-    fit@stanmodel,
-    data=counterfact_marketing_bnc,
-    draws=as.matrix(fit),
-    seed=11021585
-    )
-```
-
-```{r}
-#| eval: false
-#TODO: Convert to ggplot, stabilize y axis
-hist(as.vector(extract(generated_bnc, pars="p_predicted_default")$p_predicted_default), bins=100)
-hist(as.vector(extract(generated_bnc, pars="p_predicted_intervention")$p_predicted_intervention), bins=100)
-hist(as.vector(extract(generated_bnc, pars="predicted_difference")$predicted_difference), bins=100)
-```
-
-```{r}
-counterfact_predicted_bnc <- data.frame(
-    p_predicted_default = as.vector(extract(generated_bnc, pars="p_predicted_default")$p_predicted_default)
-    ,p_predicted_intervention = as.vector(extract(generated_bnc, pars="p_predicted_intervention")$p_predicted_intervention)
-    ,predicted_difference = as.vector(extract(generated_bnc, pars="predicted_difference")$predicted_difference)
-)
-```
-
-
-
-
-
-
-
-
-
-```{r}
-pddf_bnc <- data.frame(extract(generated_bnc, pars="predicted_difference")$predicted_difference) |>
-    pivot_longer(X1:X1343)
-
-#TODO: Fix Category names
-pddf_bnc["entry_idx"] <- as.numeric(gsub("\\D","",pddf_bnc$name))
-pddf_bnc["category"] <-  sapply(pddf_bnc$entry_idx, function(i) df$category_id[i])
-pddf_bnc["category_name"] <- sapply(pddf_bnc$category, function(i) beta_list$groups[i])
-```
-
-
-```{r}
-ggplot(pddf_bnc, aes(x=value,)) +
-    geom_density() +
-    labs(
-        title = "Distribution of predicted differences"
-        ,subtitle = "Intervention: add a single USP DC competitor"
-        ,x = "Difference in probability due to intervention"
-        ,y = "Probability Density"
-    ) + 
-    geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") 
-ggsave("./Images/TotalEffects/p_uspdc_intervention_distdiff_styled.png")
-
-ggplot(pddf_bnc, aes(x=value,)) +
-    geom_density() +
-    facet_wrap(
-        ~factor(
-            category_name, 
-            levels=beta_list$groups
-            )
-        , labeller = label_wrap_gen(multi_line = TRUE)
-        , ncol=5
-        ,scales="free"
-        ) +
-    labs(
-        title = "Distribution of predicted differences | By Group"
-        ,subtitle = "Intervention: add a single USP DC competitor"
-        ,x = "Difference in probability due to intervention"
-        ,y = "Probability Density"
-    ) + 
-    geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") +
-    theme(strip.text.x = element_text(size = 8))
-ggsave("./Images/TotalEffects/p_uspdc_intervention_distdiff_by_group.png")
-
-ggplot(pddf_bnc, aes(x=value,)) +
-    geom_histogram(bins=100) +
-    facet_wrap(
-        ~factor(
-            category_name, 
-            levels=beta_list$groups
-            )
-        , labeller = label_wrap_gen(multi_line = TRUE)
-        , ncol=5) +
-    labs(
-        title = "Histogram of predicted differences | By Group"
-        ,subtitle = "Intervention: add a single USP DC competitor"
-        ,x = "Difference in probability due to intervention"
-        ,y = "Predicted counts"
-    ) + 
-    #xlim(-0.25,0.1) +
-    geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") +
-    theme(strip.text.x = element_text(size = 8))
-ggsave("./Images/TotalEffects/p_uspdc_intervention_histdiff_by_group.png")
-```
-
-
-
-

r-analysis/FullAnalysis.R

@@ -1,239 +0,0 @@
-library(bayesplot)
-available_mcmc(pattern = "_nuts_")
-library(ggplot2)
-library(posterior)
-library(cmdstanr)
-library(rstan) # for stanfit
-#Resources: https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started
-
-#save unchanged models instead of recompiling
-rstan_options(auto_write = TRUE)
-#allow for multithreaded sampling
-options(mc.cores = parallel::detectCores())
-
-#test installation, shouldn't get any errors
-#example(stan_model, package = "rstan", run.dontrun = TRUE)
-
-################ Pull data from database ######################
-library(RPostgreSQL)
-
-driver <- dbDriver("PostgreSQL")
-
-con <- dbConnect(
-    driver,
-    user='root',
-    password='root',
-    dbname='aact_db',
-    host='will-office'
-    )
-
-query <- dbSendQuery(
-    con,
-    "select * from formatted_data_with_planned_enrollment;"
-    )
-df <- fetch(query,Inf)
-df <- na.omit(df)
-
-n_categories <- 22 #number of categories in ICD-10
-
-################ Format Data ###########################
-
-
-categories <- df["category_id"]
-
-#Convert log(x+1) to arcsinh
-
-x <- df["elapsed_duration"]
-x["log_n_brands"] <- log(df$n_brands + 1)
-x["h_sdi_val"] <- log(df$h_sdi_val + 1)
-x["hm_sdi_val"] <- log(df$hm_sdi_val + 1)
-x["m_sdi_val"] <- log(df$m_sdi_val + 1)
-x["lm_sdi_val"] <- log(df$lm_sdi_val + 1)
-x["l_sdi_val"] <- log(df$l_sdi_val + 1)
-#x["enrollment"] <- df["current_enrollment"] / df["planned_enrollment"]
-#square terms
-#x["enrollment^2"] <- x["enrollment"]^2
-#x["elapsed_duration^2"] <- x["elapsed_duration"]^2
-#x["n_brands^2"] <- x["n_brands"]^2
-#break out
-x["status_NYR"] <- ifelse(df["current_status"]=="Not yet recruiting",1,0)
-x["status_Rec"] <- ifelse(df["current_status"]=="Recruiting",1,0)
-x["status_ANR"] <- ifelse(df["current_status"]=="Active, not recruiting",1,0)
-x["status_EBI"] <- ifelse(df["current_status"]=="Enrolling by invitation",1,0)
-
-y <- ifelse(df["final_status"]=="Terminated",1,0)
-
-################################# DATA EXPLORATION ############################
-
-#Plot terminated vs completed
-#Plot duration for terminated vs completed
-#Plot different times of
-
-################################# FIT #########################################
-#setup data (named list)
-trials_data <- list(
-    D = ncol(x),#
-    N = nrow(x),
-    L = n_categories,
-    y = as.vector(y),
-    ll = categories$category_id,
-    x = as.matrix(x),
-    mu_mean = 0,
-    mu_stdev = 0.5,
-    sigma_shape = 6,
-    sigma_rate = 12
-)
-
-model <- cmdstan_model(file.path("Hierarchal_Logistic.stan"))
-
-fit <- model$sample(
-    data = trials_data,
-    seed = 11021585,
-    chains = 4,
-    parallel_chains = 4,
-    refresh = 500
-)
-
-################################# ANALYZE #####################################
-color_scheme_set("darkgray")
-div_style <- parcoord_style_np(div_color = "green", div_size = 0.05, div_alpha = 0.4)
-
-print(fit$summary(),n=265)
-
-stanfitted <- rstan::read_stan_csv(fit$output_files())
-
-#analyze mu values
-draw_mu <- fit$draws("mu")
-mcmc_hist(draw_mu)
-mcmc_trace(draw_mu)
-mcmc_pairs(draw_mu)
-mcmc_parcoord(posterior,pars=c(
-    "mu[1]",
-    "mu[2]",
-    "mu[3]",
-    "mu[4]",
-    "mu[5]",
-    "mu[6]",
-    "mu[7]",
-    "mu[8]",
-    "mu[9]",
-    "mu[10]",
-    "mu[11]"
-),
-np=nuts_prmts,
-np_style = div_style
-)
-
-
-#check sigma
-draw_sigma <- fit$draws("sigma")
-mcmc_hist(draw_sigma)
-mcmc_trace(draw_sigma)
-
-mcmc_parcoord(posterior,pars=c(
-    "sigma[1]",
-    "sigma[2]",
-    "sigma[3]",
-    "sigma[4]",
-    "sigma[5]",
-    "sigma[6]",
-    "sigma[7]",
-    "sigma[8]",
-    "sigma[9]",
-    "sigma[10]",
-    "sigma[11]"
-),
-np=nuts_prmts,
-np_style = div_style
-)
-
-#other diagnostics
-logpost <- log_posterior(fit)
-nuts_prmts <- nuts_params(fit)
-posterior <- fit$draws()
-
-mcmc_pairs(
-    posterior,
-    np = nuts_prmts,
-    pars=c(
-        "mu[1]",
-        "mu[2]",
-        "mu[3]",
-        "mu[4]",
-        "mu[5]",
-        "mu[6]",
-        "mu[7]",
-        "mu[8]",
-        "mu[9]",
-        "mu[10]",
-        "mu[11]"
-    ),
-    off_diag_args = list(size = 0.75)
-)
-
-#Interpretation
-mcmc_areas(posterior,
-    pars=c(
-        "mu[1]",
-        "mu[2]",
-        "mu[3]",
-        "mu[4]",
-        "mu[5]",
-        "mu[6]",
-        "mu[7]",
-        "mu[8]",
-        "mu[9]",
-        "mu[10]",
-        "mu[11]"
-    ),
-    prob = 0.95
-)
-
-#Interpretation
-mcmc_areas(posterior,
-           pars=c(
-               "sigma[1]",
-               "sigma[2]",
-               "sigma[3]",
-               "sigma[4]",
-               "sigma[5]",
-               "sigma[6]",
-               "sigma[7]",
-               "sigma[8]",
-               "sigma[9]",
-               "sigma[10]",
-               "sigma[11]"
-           ),
-           prob = 0.95
-)
-
-#iterate through betas
-
-
-mcmc_areas(posterior,
-           pars=c(
-               "beta[1,*]"
-           ),
-           prob = 0.95
-)
-
-#generate array of betas
-betas_array <- sapply(1:11, function(param) sapply(1:22, function(group) paste0("beta[",group,",",param,"]")))
-
-for (group in 1:22) {
-    print(
-        mcmc_areas(posterior,
-               pars=betas_array[group,],
-               prob = 0.95
-        )
-    )
-}
-
-for (param in 1:11) {
-    print(
-        mcmc_areas(posterior,
-                   pars=betas_array[,param],
-                   prob = 0.95
-        )
-    )
-}

r-analysis/Hierarchal_Logistic_prior.stan

@@ -1,47 +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 parameters
-  int<lower=1> N; // Number of observations
-  int<lower=1> L; //Number of categories
-  int<lower=1, upper=L> ll[N];
-  row_vector[D] x[N];
-  real mu_m;
-  real mu_sd;
-  real sigma_shape;
-  real sigma_rate;
-}
-generated quantities {
-    //preallocate
-    real mu_prior[D];
-    real sigma_prior[D];
-    vector[D] beta_prior[L];
-    real p_prior[N]; // what I have priors about
-    //sample parameters
-    for (d in 1:D) {
-        mu_prior[d] = normal_rng(0,1);
-        sigma_prior[d] = gamma_rng(2,1);
-    }
-    for (l in 1:L) {
-        for (d in 1:D) {
-            beta_prior[l,d] = normal_rng(mu_prior[d],sigma_prior[d]);
-        }
-    }
-    //generate probabilities
-    {
-        vector[D] b_prior[N];//local var
-        for (n in 1:N){
-            b_prior[n] = beta_prior[ll[n]];
-            p_prior[n] = inv_logit(  x[n] * b_prior[n] );
-        }
-    }
-}

r-analysis/Logistic.stan

@@ -1,30 +0,0 @@
-//
-// This Stan program defines a simple model, with a
-// vector of values 'y' modeled as normally distributed
-// with mean 'mu' and standard deviation 'sigma'.
-//
-// Learn more about model development with Stan at:
-//
-//    http://mc-stan.org/users/interfaces/rstan.html
-//    https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started
-//
-
-// The input data is a vector 'y' of length 'N'.
-data {
-  int<lower=1> D;
-  int<lower=1> N;
-  int<lower=0, upper=1> y[N];
-  row_vector[D] x[N];
-}
-parameters {
-  real mu[D];
-  real<lower=0> sigma[D];
-}
-model {
-  sigma ~ gamma(2,0.1);
-  mu ~ normal(0, sigma); //convert to mvnormal
-
-  for (n in 1:N) {
-    y[n] ~ bernoulli_logit(x[n] * mu);
-  }
-}

r-analysis/basic_logit.stan

@@ -1,32 +0,0 @@
-//
-// This Stan program defines a simple model, with a
-// vector of values 'y' modeled as normally distributed
-// with mean 'mu' and standard deviation 'sigma'.
-//
-// Learn more about model development with Stan at:
-//
-//    http://mc-stan.org/users/interfaces/rstan.html
-//    https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started
-//
-
-// The input data is a vector 'y' of length 'N'.
-data {
-  int<lower=0> N;
-  int<lower=0> k;
-  matrix[N,k] X;
-  vector[N] int<lower=0, upper=1> y;
-}
-
-// The parameters accepted by the model. Our model
-// accepts two parameters 'mu' and 'sigma'.
-parameters {
-  vector[k] beta;
-}
-
-// The model to be estimated. We model the output
-// 'y' to be normally distributed with mean 'mu'
-// and standard deviation 'sigma'.
-model {
-  y ~ bernoulli_logit( X * beta);
-}
-

r-analysis/latex_output/DirectEffects/group_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(-0.0333554671965411, -0.0816048428798888, -0.14822283281126, -0.266661171653673, -0.122811504866503, -0.0730682464154404, -0.0948854818270773, -0.121457456523795, -0.0405372878690479, -0.00533663159113219, -0.000585600111380386, -0.00557138538681874)
-c(`2.5%` = -0.650918866487675, `2.5%` = -1.46567361056997, `2.5%` = -2.00373524711419, `2.5%` = -0.884014357912585, `2.5%` = -0.589078622487128, `2.5%` = -0.515799157139537, `2.5%` = -0.575525591134666, `2.5%` = -0.619592982366338, `2.5%` = -0.700807445098139, `2.5%` = -0.478359030066064, `2.5%` = -0.747738822953945, `2.5%` = -0.547236884821695)
-c(`97.5%` = 0.586408105812293, `97.5%` = 1.25686631205631, `97.5%` = 1.63450278223622, `97.5%` = 0.239311394065014, `97.5%` = 0.226457215642141, `97.5%` = 0.2713042034849, `97.5%` = 0.267732959365188, `97.5%` = 0.239658080544198, `97.5%` = 0.626632265835835, `97.5%` = 0.452833279685459, `97.5%` = 0.722592110325922, `97.5%` = 0.521616619266678)
-c(`5%` = -0.521557208692412, `5%` = -1.20092272626125, `5%` = -1.62666028114303, `5%` = -0.750874732929316, `5%` = -0.479625923706422, `5%` = -0.418969780537693, `5%` = -0.465685567964911, `5%` = -0.507989133896294, `5%` = -0.561400134198236, `5%` = -0.354956928534609, `5%` = -0.571443956067767, `5%` = -0.425566130863293)
-c(`95%` = 0.458661660656351, `95%` = 1.0103835099505, `95%` = 1.31428822112951, `95%` = 0.163309336653743, `95%` = 0.160084526533093, `95%` = 0.208252318625455, `95%` = 0.196998436891251, `95%` = 0.176224129361429, `95%` = 0.475022207136582, `95%` = 0.34504055978718, `95%` = 0.568906077580551, `95%` = 0.402307136842525)

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

@@ -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(-0.0353319092798262, -0.667659588573525, -0.426662961246809, 0.0627606452845482, -0.032122876366859, 0.0692372274974577, 0.00333854196734669, -0.0796819135752826, -0.20912536018799, -0.0110760315296536, -0.13143407853664, 0.208644014756123)
-c(`2.5%` = -0.617973586189941, `2.5%` = -1.84852850564476, `2.5%` = -1.58611083836008, `2.5%` = -0.417049157935813, `2.5%` = -0.387151005859201, `2.5%` = -0.255831609193942, `2.5%` = -0.371336426680338, `2.5%` = -0.492590738435841, `2.5%` = -0.945439609291323, `2.5%` = -0.485658627931781, `2.5%` = -0.907802053311157, `2.5%` = -0.233024875361211)
-c(`97.5%` = 0.557141349785682, `97.5%` = 0.333296222901717, `97.5%` = 0.676762640081955, `97.5%` = 0.580578562892596, `97.5%` = 0.330507422600221, `97.5%` = 0.472199940957423, `97.5%` = 0.38886451402152, `97.5%` = 0.275313479272503, `97.5%` = 0.315669435320966, `97.5%` = 0.447738518135558, `97.5%` = 0.469638387064318, `97.5%` = 0.947611094270466)
-c(`5%` = -0.508832891153791, `5%` = -1.61987740240911, `5%` = -1.374497411387, `5%` = -0.335726433843714, `5%` = -0.320537590648167, `5%` = -0.196582716266943, `5%` = -0.291245691126286, `5%` = -0.405842777306539, `5%` = -0.770582950456033, `5%` = -0.37653437100647, `5%` = -0.733995288939712, `5%` = -0.159905839522464)
-c(`95%` = 0.445024272718964, `95%` = 0.192677426246452, `95%` = 0.491906288928273, `95%` = 0.483104060696174, `95%` = 0.256114038078104, `95%` = 0.388274645012249, `95%` = 0.30403376761348, `95%` = 0.203557356084469, `95%` = 0.224931630208054, `95%` = 0.328780688143198, `95%` = 0.361698139473533, `95%` = 0.766303524326403)

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

@@ -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(-0.1350166586327, 1.42004536167462, -0.180236471981648, -0.0834031170778047, -0.114425712850253, -0.0624505479275495, -0.0770062362507839, -0.12325792019416, -0.062701663439268, -0.00370684105270185, 0.051697644596875, -0.0297390835449691)
-c(`2.5%` = -0.841164238146927, `2.5%` = 0.166228321710892, `2.5%` = -1.24185484456687, `2.5%` = -0.575720317140294, `2.5%` = -0.544731233105528, `2.5%` = -0.498602083969468, `2.5%` = -0.4980741080226, `2.5%` = -0.568847042903758, `2.5%` = -0.680839523831996, `2.5%` = -0.462623364969651, `2.5%` = -0.622045682166618, `2.5%` = -0.577014239447581)
-c(`97.5%` = 0.422370584061102, `97.5%` = 3.03047315901632, `97.5%` = 0.850790497217344, `97.5%` = 0.417839736473721, `97.5%` = 0.224427289664008, `97.5%` = 0.271253324519114, `97.5%` = 0.263581453569408, `97.5%` = 0.219054459221291, `97.5%` = 0.522372341682048, `97.5%` = 0.450642071977278, `97.5%` = 0.802350604222116, `97.5%` = 0.439020973201874)
-c(`5%` = -0.674921145513311, `5%` = 0.325020098610294, `5%` = -1.07057709615347, `5%` = -0.490154573212924, `5%` = -0.455866770748054, `5%` = -0.400298559553039, `5%` = -0.40994240294468, `5%` = -0.482628439569884, `5%` = -0.557990156682396, `5%` = -0.364772370803702, `5%` = -0.481699496200734, `5%` = -0.449023424748452)
-c(`95%` = 0.315064862057188, `95%` = 2.71868187996996, `95%` = 0.6825129130599, `95%` = 0.322993762960079, `95%` = 0.158724278745239, `95%` = 0.210081698558979, `95%` = 0.204675097314049, `95%` = 0.160771578813489, `95%` = 0.412334948233884, `95%` = 0.35762381742188, `95%` = 0.636739588170121, `95%` = 0.341991702173203)

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

@@ -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(-0.0993758150556964, 0.683653688372939, -0.420548263203837, -0.462678784625764, 0.00186329456065648, 0.0597795199040114, 0.0681148600699909, 0.0461761490923264, -0.219385658942507, -0.026301838156295, 0.32478426535094, 0.020489615259928)
-c(`2.5%` = -0.649453534111085, `2.5%` = -0.0715480521081878, `2.5%` = -1.22916631970868, `2.5%` = -0.709034383494051, `2.5%` = -0.348811194923399, `2.5%` = -0.23351441505003, `2.5%` = -0.241101891952905, `2.5%` = -0.292204493477622, `2.5%` = -0.81900840565343, `2.5%` = -0.52584405348817, `2.5%` = -0.243402705068146, `2.5%` = -0.429251090289663)
-c(`97.5%` = 0.368644030362086, `97.5%` = 1.47768415582758, `97.5%` = 0.39318191253476, `97.5%` = -0.23735311291066, `97.5%` = 0.391318541907379, `97.5%` = 0.385985888234964, `97.5%` = 0.428550766860501, `97.5%` = 0.438579304744015, `97.5%` = 0.239595636184448, `97.5%` = 0.404201638523238, `97.5%` = 1.23628887068288, `97.5%` = 0.487394572761673)
-c(`5%` = -0.52192353020523, `5%` = 0.0437719361245331, `5%` = -1.10318164735297, `5%` = -0.662637668672948, `5%` = -0.280412083599109, `5%` = -0.17959411845604, `5%` = -0.181850570524538, `5%` = -0.231355058698291, `5%` = -0.702502858669705, `5%` = -0.40133526307109, `5%` = -0.164285973529124, `5%` = -0.340507185356601)
-c(`95%` = 0.282609130587132, `95%` = 1.35108734959306, `95%` = 0.262600483963485, `95%` = -0.27266375346642, `95%` = 0.307352752974449, `95%` = 0.318231328297662, `95%` = 0.362736140529989, `95%` = 0.358037869811668, `95%` = 0.169351071503387, `95%` = 0.316946023033097, `95%` = 1.05804976340078, `95%` = 0.386482609592056)

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

@@ -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(-0.101687967791986, 0.260625956246919, -1.41311346802269, 0.0234773335237628, -0.0167675265218763, 0.0856177665463779, 0.0541663300254734, -0.0489442451897826, -0.175613009906426, -0.0129978772390341, 0.0136500560555857, 0.0828587018493729)
-c(`2.5%` = -0.75353018780598, `2.5%` = -1.00766752361942, `2.5%` = -3.401492690184, `2.5%` = -0.449334233697236, `2.5%` = -0.3813420073426, `2.5%` = -0.239325630096086, `2.5%` = -0.294701408616, `2.5%` = -0.44544207912836, `2.5%` = -0.892050792681191, `2.5%` = -0.494462890468011, `2.5%` = -0.666840078342412, `2.5%` = -0.376439176914794)
-c(`97.5%` = 0.456963518658231, `97.5%` = 1.69446395582928, `97.5%` = 0.199302958070319, `97.5%` = 0.507180220455301, `97.5%` = 0.368701157216287, `97.5%` = 0.517238144007455, `97.5%` = 0.484238515353489, `97.5%` = 0.312151961605374, `97.5%` = 0.370881206753106, `97.5%` = 0.435596577978869, `97.5%` = 0.721254548490607, `97.5%` = 0.696764948267981)
-c(`5%` = -0.598975479040644, `5%` = -0.774388773799267, `5%` = -3.03473408528801, `5%` = -0.369579631037732, `5%` = -0.302781726817594, `5%` = -0.180751834428207, `5%` = -0.227459855411846, `5%` = -0.359959289739994, `5%` = -0.726956896133474, `5%` = -0.37607446715096, `5%` = -0.511640963000957, `5%` = -0.283655680952849)
-c(`95%` = 0.334682947415185, `95%` = 1.39113469911214, `95%` = -0.0370570732959479, `95%` = 0.424927943281204, `95%` = 0.290563883495334, `95%` = 0.420212397505738, `95%` = 0.392316891142529, `95%` = 0.240788422786828, `95%` = 0.268210851959822, `95%` = 0.333041306872968, `95%` = 0.570750639989039, `95%` = 0.544517445336076)

r-analysis/latex_output/DirectEffects/group_Musculoskeletal.tex

@@ -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(0.100124327907652, -1.41625310169202, 1.52446064588643, -0.0873137500894338, -0.0677464856346281, 0.0108650892063307, 0.00612669940351482, -0.0314187576539557, -0.211716341147135, -0.00183935600163672, -0.0738493576307788, 0.115533405660669)
-c(`2.5%` = -0.406446255373983, `2.5%` = -2.60148475745005, `2.5%` = 0.527275445018903, `2.5%` = -0.584022199941087, `2.5%` = -0.463154561951676, `2.5%` = -0.346315618199531, `2.5%` = -0.362381508509159, `2.5%` = -0.409819426261724, `2.5%` = -0.936591248518692, `2.5%` = -0.457818242374006, `2.5%` = -0.798071774552942, `2.5%` = -0.329263294357893)
-c(`97.5%` = 0.797719164264997, `97.5%` = -0.457781574665431, `97.5%` = 2.69049448523487, `97.5%` = 0.374989682835585, `97.5%` = 0.295740868485436, `97.5%` = 0.377261709291995, `97.5%` = 0.395888264864696, `97.5%` = 0.341241054883828, `97.5%` = 0.314398740452913, `97.5%` = 0.452102307502102, `97.5%` = 0.547821159342026, `97.5%` = 0.740236931779179)
-c(`5%` = -0.316960032380135, `5%` = -2.39673285825223, `5%` = 0.677198726234082, `5%` = -0.48370456159241, `5%` = -0.380333066540771, `5%` = -0.271668438834234, `5%` = -0.28575489564353, `5%` = -0.331549343941397, `5%` = -0.776805336768321, `5%` = -0.356984822002284, `5%` = -0.649098498864267, `5%` = -0.244951545599123)
-c(`95%` = 0.636944255371381, `95%` = -0.579922080756115, `95%` = 2.46984046267875, `95%` = 0.301174279247803, `95%` = 0.219720584710747, `95%` = 0.305577531163344, `95%` = 0.314025671253092, `95%` = 0.263968986148209, `95%` = 0.22352188185812, `95%` = 0.343376182190238, `95%` = 0.424983622057134, `95%` = 0.586186029847553)

r-analysis/latex_output/DirectEffects/group_Neoplasms.tex

@@ -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(-0.412146324077768, 0.470951452129627, -1.2444367751168, 0.245268419893298, -0.0946502363520193, 0.0500740339796919, -0.0704987792914352, -0.124433125807786, -0.358974070196284, -0.00267878134821512, -0.476611882301335, -0.140862225354917)
-c(`2.5%` = -0.990046725218716, `2.5%` = 0.0825563723532098, `2.5%` = -1.78375367793506, `2.5%` = -0.144576058853997, `2.5%` = -0.509816834681553, `2.5%` = -0.248086617381755, `2.5%` = -0.463337090435845, `2.5%` = -0.545151665330706, `2.5%` = -0.959715545078129, `2.5%` = -0.477380824369578, `2.5%` = -1.42904260420137, `2.5%` = -0.614365567191018)
-c(`97.5%` = 0.0176791224477116, `97.5%` = 0.869227382956849, `97.5%` = -0.71625139500421, `97.5%` = 0.711161632402175, `97.5%` = 0.235929001218496, `97.5%` = 0.403217442159893, `97.5%` = 0.250453043701491, `97.5%` = 0.208799589086939, `97.5%` = 0.0761919118896935, `97.5%` = 0.485090234449767, `97.5%` = 0.0901610144027345, `97.5%` = 0.205381626274404)
-c(`5%` = -0.880856751562785, `5%` = 0.140776593909285, `5%` = -1.69975002339859, `5%` = -0.0851775550449366, `5%` = -0.413144404534561, `5%` = -0.190555995277503, `5%` = -0.374730282423007, `5%` = -0.457195436456302, `5%` = -0.860866809127253, `5%` = -0.371058543649785, `5%` = -1.24192236504004, `5%` = -0.521580687834855)
-c(`95%` = -0.0294487230376872, `95%` = 0.805026483916245, `95%` = -0.79680171298815, `95%` = 0.622920880640266, `95%` = 0.180557342384185, `95%` = 0.328458422894137, `95%` = 0.191388773507259, `95%` = 0.144775027690501, `95%` = 0.0202098111117333, `95%` = 0.368527660640565, `95%` = 0.0339253948301201, `95%` = 0.150938308973965)

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

@@ -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(-0.0605258874521727, 0.208110428258286, 2.932306880193, -0.476036244602958, -0.138197079911931, 0.0472640116153194, -0.0108024185768078, -0.0673889444925787, 0.0439801430116134, -0.00220242035797092, 0.0222560987441026, -0.0954924005742835)
-c(`2.5%` = -0.668423764362022, `2.5%` = -0.483919695463572, `2.5%` = 1.32770940293854, `2.5%` = -1.08972255835219, `2.5%` = -0.588355736454172, `2.5%` = -0.288061459094765, `2.5%` = -0.390261122641681, `2.5%` = -0.480037769561072, `2.5%` = -0.52071567059202, `2.5%` = -0.467981470659442, `2.5%` = -0.64696023851359, `2.5%` = -0.702986558104766)
-c(`97.5%` = 0.524706569957862, `97.5%` = 0.860804399758541, `97.5%` = 4.8161142366925, `97.5%` = 0.00951208071252858, `97.5%` = 0.205030274179544, `97.5%` = 0.447777447414387, `97.5%` = 0.375894039808643, `97.5%` = 0.309831367516505, `97.5%` = 0.714311594742576, `97.5%` = 0.46197268399125, `97.5%` = 0.724174566356542, `97.5%` = 0.352194753343582)
-c(`5%` = -0.540693659717227, `5%` = -0.358913993804725, `5%` = 1.56455100307583, `5%` = -0.970987430695906, `5%` = -0.492652682388389, `5%` = -0.226103001674273, `5%` = -0.310685728948158, `5%` = -0.401058959418401, `5%` = -0.416255094218329, `5%` = -0.352812984615348, `5%` = -0.514000899450722, `5%` = -0.55490968224003)
-c(`95%` = 0.404566643306311, `95%` = 0.757665883111782, `95%` = 4.4435402069247, `95%` = -0.0535686895330286, `95%` = 0.144052434219696, `95%` = 0.356322084893283, `95%` = 0.295168504418982, `95%` = 0.235723096029231, `95%` = 0.569378098960284, `95%` = 0.353283663339729, `95%` = 0.576987914910334, `95%` = 0.268009451996814)

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

@@ -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(-0.174849840107956, 0.344654754591335, -0.71289290741607, -0.185341893373765, -0.0612704269574426, 0.0475554801866666, 0.0209325549818404, 0.00823090691189444, -0.140469476402341, -0.000991729207921393, 0.0426395752164606, 0.0531332616653892)
-c(`2.5%` = -0.853225149285778, `2.5%` = -0.81212639023518, `2.5%` = -2.47632399275718, `2.5%` = -0.700340720981917, `2.5%` = -0.459624700583444, `2.5%` = -0.299576900991634, `2.5%` = -0.329465385054038, `2.5%` = -0.35530723101666, `2.5%` = -0.818237407714228, `2.5%` = -0.457007790658148, `2.5%` = -0.626446320243322, `2.5%` = -0.42852558763013)
-c(`97.5%` = 0.313015825195173, `97.5%` = 1.64628843970952, `97.5%` = 0.803673159441113, `97.5%` = 0.264097087549475, `97.5%` = 0.301714093464364, `97.5%` = 0.457951093623432, `97.5%` = 0.41718558607108, `97.5%` = 0.42368030495614, `97.5%` = 0.415680090003177, `97.5%` = 0.473772689053127, `97.5%` = 0.774791684477986, `97.5%` = 0.622393397809352)
-c(`5%` = -0.69853877504723, `5%` = -0.619677989159712, `5%` = -2.16966249843568, `5%` = -0.602161468147168, `5%` = -0.370652637547503, `5%` = -0.233289235150446, `5%` = -0.267307968491817, `5%` = -0.289828310680018, `5%` = -0.66997803833358, `5%` = -0.350248519811252, `5%` = -0.483515522092064, `5%` = -0.330171694279594)
-c(`95%` = 0.228050943041459, `95%` = 1.41171899585957, `95%` = 0.585314360782336, `95%` = 0.187637916801343, `95%` = 0.231239446941901, `95%` = 0.360332344911533, `95%` = 0.329385758977443, `95%` = 0.33895575065099, `95%` = 0.315101103321463, `95%` = 0.355492422245274, `95%` = 0.614652923649533, `95%` = 0.486396526448576)

r-analysis/latex_output/DirectEffects/group_status_ANR.tex

@@ -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(-0.0379222039996637, -0.00951521433050643, -0.0357669209565548, -0.0289858802339238, -0.0384045782213899, -0.0354852935888951, -0.195806801668196, -0.0319728051993748, -0.064502474892061, -0.0359000714018003, -0.209385718567549, -0.0293405266823335, -0.166256613851307, -0.204154479367649, -0.343021435135372, 0.0429675640807533, -0.031540048708067, -0.0346985641986963, -0.0303375281259717, -0.13391213882268, -0.0315874427691754, -0.0292508480128129)
-c(`2.5%` = -0.670177498740691, `2.5%` = -0.606574716528012, `2.5%` = -0.660124076867341, `2.5%` = -0.658500223161749, `2.5%` = -0.673266724039317, `2.5%` = -0.693646849412836, `2.5%` = -0.917059388708302, `2.5%` = -0.662394912651839, `2.5%` = -0.67991481986874, `2.5%` = -0.670316698080103, `2.5%` = -0.83183723815622, `2.5%` = -0.663476379211969, `2.5%` = -0.854735257874132, `2.5%` = -0.918576287776926, `2.5%` = -0.963532341357661, `2.5%` = -0.495917243186253, `2.5%` = -0.64751983879636, `2.5%` = -0.673951139603439, 
-`2.5%` = -0.665804518054749, `2.5%` = -0.791664538636436, `2.5%` = -0.643761100670645, `2.5%` = -0.680737220370752)
-c(`97.5%` = 0.579428469021833, `97.5%` = 0.640138775494956, `97.5%` = 0.596180548733038, `97.5%` = 0.619030332938483, `97.5%` = 0.597678927212641, `97.5%` = 0.6121441149317, `97.5%` = 0.323295322658126, `97.5%` = 0.590730699845894, `97.5%` = 0.503098249043251, `97.5%` = 0.607349406190736, `97.5%` = 0.228895375416542, `97.5%` = 0.624129832779025, `97.5%` = 0.342536899260382, `97.5%` = 0.307896298234294, `97.5%` = 0.0903633166835597, `97.5%` = 0.686527591660775, `97.5%` = 0.616181981700487, `97.5%` = 0.599647736473311, 
-`97.5%` = 0.609123918623858, `97.5%` = 0.415337198000103, `97.5%` = 0.597111489383847, `97.5%` = 0.632428409170173)
-c(`5%` = -0.530855657109975, `5%` = -0.487829039222839, `5%` = -0.530023192811465, `5%` = -0.516610981668542, `5%` = -0.524022832574353, `5%` = -0.537218989651104, `5%` = -0.75728605285178, `5%` = -0.517067859106373, `5%` = -0.547235882003818, `5%` = -0.536626649173697, `5%` = -0.691946442016388, `5%` = -0.52266339334223, `5%` = -0.70021531860572, `5%` = -0.769560225018392, `5%` = -0.844315621869912, `5%` = -0.393051567766974, `5%` = -0.519010088416499, `5%` = -0.529941857869653, `5%` = -0.52218968602785, 
-`5%` = -0.648665425674694, `5%` = -0.520022794035758, `5%` = -0.521893002755623)
-c(`95%` = 0.449851048402911, `95%` = 0.496036928523184, `95%` = 0.452249441742121, `95%` = 0.47353271148754, `95%` = 0.454890426424996, `95%` = 0.474673790264394, `95%` = 0.22238953047732, `95%` = 0.44333608430914, `95%` = 0.387054472484903, `95%` = 0.475167927437824, `95%` = 0.156987753663085, `95%` = 0.473536190521047, `95%` = 0.254381363435638, `95%` = 0.217911529700368, `95%` = 0.0348859178585696, `95%` = 0.552104217589191, `95%` = 0.462090261872998, `95%` = 0.456310261272674, `95%` = 0.474214855342067, 
-`95%` = 0.307884811241422, `95%` = 0.465192052817642, `95%` = 0.472198045319103)

r-analysis/latex_output/DirectEffects/parameters_10_status_EBI.tex

@@ -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(-0.00232413253272223, -0.00152293417694882, -0.00353563813667985, -0.00310660613711883, -0.00533663159113219, -0.00305599644590309, -0.0110760315296536, -0.00588960985826809, -0.00370684105270185, -0.00470552774062355, -0.026301838156295, -0.00292165177766262, -0.0129978772390341, -0.00183935600163672, -0.00267878134821512, -0.00220242035797092, -0.000914878424206371, -0.00258882299014672, 0.000504336438586614, -0.000991729207921393, -0.000800806458059062, -0.00386686775250893)
-c(`2.5%` = -0.479474973075092, `2.5%` = -0.472669740624549, `2.5%` = -0.464039194626794, `2.5%` = -0.473392603483428, `2.5%` = -0.478359030066064, `2.5%` = -0.455950775505214, `2.5%` = -0.485658627931781, `2.5%` = -0.485882001168744, `2.5%` = -0.462623364969651, `2.5%` = -0.464633556660333, `2.5%` = -0.52584405348817, `2.5%` = -0.46165946058897, `2.5%` = -0.494462890468011, `2.5%` = -0.457818242374006, `2.5%` = -0.477380824369578, `2.5%` = -0.467981470659442, `2.5%` = -0.462006993832474, `2.5%` = -0.475538447435748, 
-`2.5%` = -0.459119942403029, `2.5%` = -0.457007790658148, `2.5%` = -0.457457906185736, `2.5%` = -0.463105985034812)
-c(`97.5%` = 0.472157783917002, `97.5%` = 0.465334754332652, `97.5%` = 0.444423983236463, `97.5%` = 0.450919265047758, `97.5%` = 0.452833279685459, `97.5%` = 0.451291140159942, `97.5%` = 0.447738518135558, `97.5%` = 0.47182876476933, `97.5%` = 0.450642071977278, `97.5%` = 0.460733730501955, `97.5%` = 0.404201638523238, `97.5%` = 0.463075602426935, `97.5%` = 0.435596577978869, `97.5%` = 0.452102307502102, `97.5%` = 0.485090234449767, `97.5%` = 0.46197268399125, `97.5%` = 0.471768785447442, `97.5%` = 0.469214434976901, 
-`97.5%` = 0.458559371773605, `97.5%` = 0.473772689053127, `97.5%` = 0.468011769036541, `97.5%` = 0.460260492516532)
-c(`5%` = -0.362566524131353, `5%` = -0.365391855607623, `5%` = -0.356688829996404, `5%` = -0.360094627174451, `5%` = -0.354956928534609, `5%` = -0.35377118365327, `5%` = -0.37653437100647, `5%` = -0.370648036848808, `5%` = -0.364772370803702, `5%` = -0.363091670495567, `5%` = -0.40133526307109, `5%` = -0.359038021511569, `5%` = -0.37607446715096, `5%` = -0.356984822002284, `5%` = -0.371058543649785, `5%` = -0.352812984615348, `5%` = -0.357581652703343, `5%` = -0.357584782489892, `5%` = -0.358080523145794, 
-`5%` = -0.350248519811252, `5%` = -0.359275569693152, `5%` = -0.355239427293753)
-c(`95%` = 0.349080687253274, `95%` = 0.362582528037606, `95%` = 0.344628062144889, `95%` = 0.343429488771613, `95%` = 0.34504055978718, `95%` = 0.347420792879581, `95%` = 0.328780688143198, `95%` = 0.352000827804009, `95%` = 0.35762381742188, `95%` = 0.353870410467441, `95%` = 0.316946023033097, `95%` = 0.352512087776552, `95%` = 0.333041306872968, `95%` = 0.343376182190238, `95%` = 0.368527660640565, `95%` = 0.353283663339729, `95%` = 0.354267255995946, `95%` = 0.347884235465927, `95%` = 0.352698507186818, 
-`95%` = 0.355492422245274, `95%` = 0.354508762801702, `95%` = 0.347938178694997)

r-analysis/latex_output/DirectEffects/parameters_11_status_Rec.tex

@@ -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(-0.00224061119544955, -0.0498803962359136, 0.0029866500758097, 0.0092020834745846, -0.00557138538681874, 0.00139701841247001, 0.208644014756123, 0.00486415637216138, -0.0297390835449691, 0.00664076692247885, 0.020489615259928, 0.00270098452440913, 0.0828587018493729, 0.115533405660669, -0.140862225354917, -0.0954924005742835, 0.00218728416741836, 0.00490518108019053, -0.000597898274967985, 0.0531332616653892, 0.00140665840745891, 0.00267108484868849)
-c(`2.5%` = -0.573130925585206, `2.5%` = -0.631300654586586, `2.5%` = -0.549248794254278, `2.5%` = -0.526524124449695, `2.5%` = -0.547236884821695, `2.5%` = -0.525794262806427, `2.5%` = -0.233024875361211, `2.5%` = -0.531351924132048, `2.5%` = -0.577014239447581, `2.5%` = -0.526231597802405, `2.5%` = -0.429251090289663, `2.5%` = -0.539844609215633, `2.5%` = -0.376439176914794, `2.5%` = -0.329263294357893, `2.5%` = -0.614365567191018, `2.5%` = -0.702986558104766, `2.5%` = -0.548813703529005, `2.5%` = -0.549035834183446, 
-`2.5%` = -0.55240500338214, `2.5%` = -0.42852558763013, `2.5%` = -0.532991842266845, `2.5%` = -0.561981734264935)
-c(`97.5%` = 0.535870607314021, `97.5%` = 0.439183977577692, `97.5%` = 0.531820860628844, `97.5%` = 0.557228636508852, `97.5%` = 0.521616619266678, `97.5%` = 0.521530978763819, `97.5%` = 0.947611094270466, `97.5%` = 0.551470726780118, `97.5%` = 0.439020973201874, `97.5%` = 0.543800507198643, `97.5%` = 0.487394572761673, `97.5%` = 0.537619293317655, `97.5%` = 0.696764948267981, `97.5%` = 0.740236931779179, `97.5%` = 0.205381626274404, `97.5%` = 0.352194753343582, `97.5%` = 0.553383244915363, `97.5%` = 0.559402688668633, 
-`97.5%` = 0.550312950172821, `97.5%` = 0.622393397809352, `97.5%` = 0.536869755062453, `97.5%` = 0.557804559325436)
-c(`5%` = -0.428266097170789, `5%` = -0.491562554012717, `5%` = -0.411292594357374, `5%` = -0.403177595011685, `5%` = -0.425566130863293, `5%` = -0.404058425652893, `5%` = -0.159905839522464, `5%` = -0.416161496643271, `5%` = -0.449023424748452, `5%` = -0.399764467982059, `5%` = -0.340507185356601, `5%` = -0.420233862640142, `5%` = -0.283655680952849, `5%` = -0.244951545599123, `5%` = -0.521580687834855, `5%` = -0.55490968224003, `5%` = -0.422336361660143, `5%` = -0.422015963935169, `5%` = -0.420900175948769, 
-`5%` = -0.330171694279594, `5%` = -0.41323722677094, `5%` = -0.42744212405137)
-c(`95%` = 0.404392809444781, `95%` = 0.333871293266063, `95%` = 0.417529613693018, `95%` = 0.430463617558602, `95%` = 0.402307136842525, `95%` = 0.410955507727826, `95%` = 0.766303524326403, `95%` = 0.430330128267061, `95%` = 0.341991702173203, `95%` = 0.419913772461363, `95%` = 0.386482609592056, `95%` = 0.421100502019285, `95%` = 0.544517445336076, `95%` = 0.586186029847553, `95%` = 0.150938308973965, `95%` = 0.268009451996814, `95%` = 0.425628186353177, `95%` = 0.433826523724275, `95%` = 0.417184121248096, 
-`95%` = 0.486396526448576, `95%` = 0.410430471444198, `95%` = 0.43788787573263)

r-analysis/latex_output/DirectEffects/parameters_12_status_ANR.tex

@@ -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(-0.033444959026225, -0.00793522059592051, -0.0336009736151623, -0.0316339144412533, -0.0405372878690479, -0.0414663539452286, -0.20912536018799, -0.0266756188322575, -0.062701663439268, -0.0331874740057613, -0.219385658942507, -0.0325137781481118, -0.175613009906426, -0.211716341147135, -0.358974070196284, 0.0439801430116134, -0.0306047651482687, -0.032498476375438, -0.0371883825844407, -0.140469476402341, -0.0333940650001783, -0.0354885056744574)
-c(`2.5%` = -0.666923716865872, `2.5%` = -0.64102067023692, `2.5%` = -0.677795179892062, `2.5%` = -0.670757215379559, `2.5%` = -0.700807445098139, `2.5%` = -0.693814265711575, `2.5%` = -0.945439609291323, `2.5%` = -0.649489899228185, `2.5%` = -0.680839523831996, `2.5%` = -0.69007268009126, `2.5%` = -0.81900840565343, `2.5%` = -0.679848629284621, `2.5%` = -0.892050792681191, `2.5%` = -0.936591248518692, `2.5%` = -0.959715545078129, `2.5%` = -0.52071567059202, `2.5%` = -0.684856132016299, `2.5%` = -0.682381096206198, 
-`2.5%` = -0.682121426892847, `2.5%` = -0.818237407714228, `2.5%` = -0.644091572578507, `2.5%` = -0.695950746544654)
-c(`97.5%` = 0.62590334980134, `97.5%` = 0.653919501490395, `97.5%` = 0.641015835184623, `97.5%` = 0.629010798078368, `97.5%` = 0.626632265835835, `97.5%` = 0.605986429897587, `97.5%` = 0.315669435320966, `97.5%` = 0.629421610550102, `97.5%` = 0.522372341682048, `97.5%` = 0.611617883076929, `97.5%` = 0.239595636184448, `97.5%` = 0.614136402646953, `97.5%` = 0.370881206753106, `97.5%` = 0.314398740452913, `97.5%` = 0.0761919118896935, `97.5%` = 0.714311594742576, `97.5%` = 0.62707456073199, `97.5%` = 0.660256568215515, 
-`97.5%` = 0.614029672287225, `97.5%` = 0.415680090003177, `97.5%` = 0.590770974069619, `97.5%` = 0.638335616472465)
-c(`5%` = -0.534614253816882, `5%` = -0.492652438031023, `5%` = -0.534510689945915, `5%` = -0.537204763734244, `5%` = -0.561400134198236, `5%` = -0.554901107934352, `5%` = -0.770582950456033, `5%` = -0.526625109409884, `5%` = -0.557990156682396, `5%` = -0.545095270800367, `5%` = -0.702502858669705, `5%` = -0.532227460109175, `5%` = -0.726956896133474, `5%` = -0.776805336768321, `5%` = -0.860866809127253, `5%` = -0.416255094218329, `5%` = -0.540848631429852, `5%` = -0.545406019188074, `5%` = -0.556323223126082, 
-`5%` = -0.66997803833358, `5%` = -0.528524246946612, `5%` = -0.557925242336448)
-c(`95%` = 0.477502832552862, `95%` = 0.513718992636229, `95%` = 0.489098182794968, `95%` = 0.480711435248938, `95%` = 0.475022207136582, `95%` = 0.474127651026557, `95%` = 0.224931630208054, `95%` = 0.491419189883086, `95%` = 0.412334948233884, `95%` = 0.482084498841415, `95%` = 0.169351071503387, `95%` = 0.478552110978265, `95%` = 0.268210851959822, `95%` = 0.22352188185812, `95%` = 0.0202098111117333, `95%` = 0.569378098960284, `95%` = 0.485945600247666, `95%` = 0.497377449520452, `95%` = 0.472090491001111, 
-`95%` = 0.315101103321463, `95%` = 0.458319237867322, `95%` = 0.490829889891898)

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

@@ -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(-0.0295164133163417, -0.0430143836673774, -0.0339902806330816, -0.0260159056108803, -0.0333554671965411, -0.028359266287689, -0.0353319092798262, -0.0187357101597514, -0.1350166586327, -0.0260977976963767, -0.0993758150556964, -0.0236033005860729, -0.101687967791986, 0.100124327907652, -0.412146324077768, -0.0605258874521727, -0.024078230002875, -0.0269598652137639, -0.0274504939892296, -0.174849840107956, -0.0222497943593906, -0.0274902413057739)
-c(`2.5%` = -0.646070710340414, `2.5%` = -0.683290321643176, `2.5%` = -0.675081729811003, `2.5%` = -0.648421169748859, `2.5%` = -0.650918866487675, `2.5%` = -0.668055634029354, `2.5%` = -0.617973586189941, `2.5%` = -0.640444657164909, `2.5%` = -0.841164238146927, `2.5%` = -0.670733739121956, `2.5%` = -0.649453534111085, `2.5%` = -0.635643676510193, `2.5%` = -0.75353018780598, `2.5%` = -0.406446255373983, `2.5%` = -0.990046725218716, `2.5%` = -0.668423764362022, `2.5%` = -0.65459523058199, `2.5%` = -0.623518595242253, 
-`2.5%` = -0.639580335809163, `2.5%` = -0.853225149285778, `2.5%` = -0.651372688023807, `2.5%` = -0.665950740608084)
-c(`97.5%` = 0.622118536783365, `97.5%` = 0.567754232733557, `97.5%` = 0.594665290830079, `97.5%` = 0.595712650833978, `97.5%` = 0.586408105812293, `97.5%` = 0.61702506930373, `97.5%` = 0.557141349785682, `97.5%` = 0.64037824695388, `97.5%` = 0.422370584061102, `97.5%` = 0.619806919714738, `97.5%` = 0.368644030362086, `97.5%` = 0.596438871658726, `97.5%` = 0.456963518658231, `97.5%` = 0.797719164264997, `97.5%` = 0.0176791224477116, `97.5%` = 0.524706569957862, `97.5%` = 0.622703525731803, `97.5%` = 0.570789155361791, 
-`97.5%` = 0.59747717452121, `97.5%` = 0.313015825195173, `97.5%` = 0.627269012162122, `97.5%` = 0.615198351898292)
-c(`5%` = -0.510251564339674, `5%` = -0.52441458740886, `5%` = -0.532441957391025, `5%` = -0.518410421043954, `5%` = -0.521557208692412, `5%` = -0.521871400612245, `5%` = -0.508832891153791, `5%` = -0.518203720240489, `5%` = -0.674921145513311, `5%` = -0.531996138266231, `5%` = -0.52192353020523, `5%` = -0.500496765978635, `5%` = -0.598975479040644, `5%` = -0.316960032380135, `5%` = -0.880856751562785, `5%` = -0.540693659717227, `5%` = -0.519375237016899, `5%` = -0.509904935942235, `5%` = -0.513849598148613, 
-`5%` = -0.69853877504723, `5%` = -0.511181165016275, `5%` = -0.520466766457042)
-c(`95%` = 0.471281041217163, `95%` = 0.423703175708398, `95%` = 0.466378617261805, `95%` = 0.454221478113165, `95%` = 0.458661660656351, `95%` = 0.460036483429395, `95%` = 0.445024272718964, `95%` = 0.492401099446282, `95%` = 0.315064862057188, `95%` = 0.475963967540287, `95%` = 0.282609130587132, `95%` = 0.45986621483045, `95%` = 0.334682947415185, `95%` = 0.636944255371381, `95%` = -0.0294487230376872, `95%` = 0.404566643306311, `95%` = 0.478649635433352, `95%` = 0.441853123918437, `95%` = 0.463352362829126, 
-`95%` = 0.228050943041459, `95%` = 0.482992956431603, `95%` = 0.466730867021221)

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

@@ -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(-0.12331418463673, -0.643524712834449, -0.155293145618607, -0.00107765414463224, -0.14822283281126, 0.00881755884088804, -0.426662961246809, -0.0178268855590179, -0.180236471981648, -0.290840496837507, -0.420548263203837, -0.00380033220537924, -1.41311346802269, 1.52446064588643, -1.2444367751168, 2.932306880193, 0.00169846910876418, -0.00893420267820256, -0.235385994622511, -0.71289290741607, -0.00820736853904142, -0.12592765159372)
-c(`2.5%` = -2.02564979096055, `2.5%` = -2.39439013494348, `2.5%` = -1.99208722130077, `2.5%` = -1.83809201587386, `2.5%` = -2.00373524711419, `2.5%` = -1.87235054261414, `2.5%` = -1.58611083836008, `2.5%` = -1.81806137603714, `2.5%` = -1.24185484456687, `2.5%` = -2.12331233705335, `2.5%` = -1.22916631970868, `2.5%` = -1.87838853425636, `2.5%` = -3.401492690184, `2.5%` = 0.527275445018903, `2.5%` = -1.78375367793506, `2.5%` = 1.32770940293854, `2.5%` = -1.89529361773991, `2.5%` = -1.9106750132439, 
-`2.5%` = -2.02803126368439, `2.5%` = -2.47632399275718, `2.5%` = -1.86122689687105, `2.5%` = -2.00542308533653)
-c(`97.5%` = 1.67969744861974, `97.5%` = 0.935428242477581, `97.5%` = 1.6348058321464, `97.5%` = 1.81710270358056, `97.5%` = 1.63450278223622, `97.5%` = 1.88045146010562, `97.5%` = 0.676762640081955, `97.5%` = 1.78906314682073, `97.5%` = 0.850790497217344, `97.5%` = 1.47057766493624, `97.5%` = 0.39318191253476, `97.5%` = 1.88452418451776, `97.5%` = 0.199302958070319, `97.5%` = 2.69049448523487, `97.5%` = -0.71625139500421, `97.5%` = 4.8161142366925, `97.5%` = 1.90068098208742, `97.5%` = 1.88773849773307, 
-`97.5%` = 1.47639661532028, `97.5%` = 0.803673159441113, `97.5%` = 1.86049650391514, `97.5%` = 1.71917016589104)
-c(`5%` = -1.66549179341294, `5%` = -2.05805403972291, `5%` = -1.66084826903427, `5%` = -1.50609037865979, `5%` = -1.62666028114303, `5%` = -1.53803827380169, `5%` = -1.374497411387, `5%` = -1.50464058833537, `5%` = -1.07057709615347, `5%` = -1.79986867754153, `5%` = -1.10318164735297, `5%` = -1.56027911708145, `5%` = -3.03473408528801, `5%` = 0.677198726234082, `5%` = -1.69975002339859, `5%` = 1.56455100307583, `5%` = -1.54500435851042, `5%` = -1.54650472228178, `5%` = -1.71550508080056, `5%` = -2.16966249843568, 
-`5%` = -1.51253234614188, `5%` = -1.68286480200484)
-c(`95%` = 1.38012867025627, `95%` = 0.661680376811838, `95%` = 1.31572988103649, `95%` = 1.49439855925108, `95%` = 1.31428822112951, `95%` = 1.54140325707139, `95%` = 0.491906288928273, `95%` = 1.47557433066007, `95%` = 0.6825129130599, `95%` = 1.15276028747074, `95%` = 0.262600483963485, `95%` = 1.54587386125945, `95%` = -0.0370570732959479, `95%` = 2.46984046267875, `95%` = -0.79680171298815, `95%` = 4.4435402069247, `95%` = 1.53642174870284, `95%` = 1.54093350572683, `95%` = 1.19508577316775, 
-`95%` = 0.585314360782336, `95%` = 1.5156111408874, `95%` = 1.37175745000343)

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

@@ -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(-0.101703799949395, -0.564637906246216, -0.0956910914293675, -0.00337692444405644, -0.0816048428798888, -6.35740684684093e-05, -0.667659588573525, -0.00405812351552042, 1.42004536167462, -0.171009890912524, 0.683653688372939, 0.00344017290368172, 0.260625956246919, -1.41625310169202, 0.470951452129627, 0.208110428258286, 0.00250209506601534, -0.00562874748733102, -0.105857797157013, 0.344654754591335, 0.0027807037509892, -0.0552183959035103)
-c(`2.5%` = -1.51590135737519, `2.5%` = -1.842080521195, `2.5%` = -1.52784116663569, `2.5%` = -1.39142200782843, `2.5%` = -1.46567361056997, `2.5%` = -1.42253864934177, `2.5%` = -1.84852850564476, `2.5%` = -1.35828695365276, `2.5%` = 0.166228321710892, `2.5%` = -1.56024033377577, `2.5%` = -0.0715480521081878, `2.5%` = -1.39463661876756, `2.5%` = -1.00766752361942, `2.5%` = -2.60148475745005, `2.5%` = 0.0825563723532098, `2.5%` = -0.483919695463572, `2.5%` = -1.40349956302864, `2.5%` = -1.38403675822353, 
-`2.5%` = -1.53199462919355, `2.5%` = -0.81212639023518, `2.5%` = -1.34556223161126, `2.5%` = -1.46894825626173)
-c(`97.5%` = 1.23043134810689, `97.5%` = 0.516158819481392, `97.5%` = 1.27757713500863, `97.5%` = 1.34564557876177, `97.5%` = 1.25686631205631, `97.5%` = 1.4133810381728, `97.5%` = 0.333296222901717, `97.5%` = 1.3394057445905, `97.5%` = 3.03047315901632, `97.5%` = 1.09705630948954, `97.5%` = 1.47768415582758, `97.5%` = 1.42305354019587, `97.5%` = 1.69446395582928, `97.5%` = -0.457781574665431, `97.5%` = 0.869227382956849, `97.5%` = 0.860804399758541, `97.5%` = 1.39777584880611, `97.5%` = 1.32536002712745, 
-`97.5%` = 1.22054546483929, `97.5%` = 1.64628843970952, `97.5%` = 1.40043610000601, `97.5%` = 1.32705054827579)
-c(`5%` = -1.23554421440395, `5%` = -1.60865719854905, `5%` = -1.26990351126472, `5%` = -1.10623630944065, `5%` = -1.20092272626125, `5%` = -1.14277805533658, `5%` = -1.61987740240911, `5%` = -1.10114220997093, `5%` = 0.325020098610294, `5%` = -1.28688609021933, `5%` = 0.0437719361245331, `5%` = -1.13051681084733, `5%` = -0.774388773799267, `5%` = -2.39673285825223, `5%` = 0.140776593909285, `5%` = -0.358913993804725, `5%` = -1.12240343354847, `5%` = -1.13264764154031, `5%` = -1.25936194261107, `5%` = -0.619677989159712, 
-`5%` = -1.12306907889816, `5%` = -1.19574516476068)
-c(`95%` = 0.9962993915319, `95%` = 0.344271026201751, `95%` = 1.02585852524287, `95%` = 1.11395208896461, `95%` = 1.0103835099505, `95%` = 1.11961350598891, `95%` = 0.192677426246452, `95%` = 1.10063510284539, `95%` = 2.71868187996996, `95%` = 0.899896640181501, `95%` = 1.35108734959306, `95%` = 1.14893128770879, `95%` = 1.39113469911214, `95%` = -0.579922080756115, `95%` = 0.805026483916245, `95%` = 0.757665883111782, `95%` = 1.12853607517645, `95%` = 1.09611509546288, `95%` = 0.961250543645123, 
-`95%` = 1.41171899585957, `95%` = 1.10399402315835, `95%` = 1.04723154370974)

r-analysis/latex_output/DirectEffects/parameters_9_status_NYR.tex

@@ -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(-0.000505821125346685, 0.045620786014229, -0.00525743090751, -0.00499600218282098, -0.000585600111380386, -0.00276881425069573, -0.13143407853664, -0.0036185203171912, 0.051697644596875, -0.0107891870271464, 0.32478426535094, -0.00145937361475847, 0.0136500560555857, -0.0738493576307788, -0.476611882301335, 0.0222560987441026, -0.00414548910371886, -0.0104742177218119, -0.00809846939246779, 0.0426395752164606, -0.0115019802718463, -0.00996274304864821)
-c(`2.5%` = -0.701450360691855, `2.5%` = -0.63003373124748, `2.5%` = -0.715622145882045, `2.5%` = -0.727044467579978, `2.5%` = -0.747738822953945, `2.5%` = -0.735270274057596, `2.5%` = -0.907802053311157, `2.5%` = -0.733860049506474, `2.5%` = -0.622045682166618, `2.5%` = -0.742120219363362, `2.5%` = -0.243402705068146, `2.5%` = -0.735852738037434, `2.5%` = -0.666840078342412, `2.5%` = -0.798071774552942, `2.5%` = -1.42904260420137, `2.5%` = -0.64696023851359, `2.5%` = -0.746859618917552, `2.5%` = -0.739101938071361, 
-`2.5%` = -0.730727303837492, `2.5%` = -0.626446320243322, `2.5%` = -0.750902816931559, `2.5%` = -0.743675008324717)
-c(`97.5%` = 0.732116835481222, `97.5%` = 0.789641535896855, `97.5%` = 0.73726328358774, `97.5%` = 0.702602048400459, `97.5%` = 0.722592110325922, `97.5%` = 0.745772208274965, `97.5%` = 0.469638387064318, `97.5%` = 0.740059158395697, `97.5%` = 0.802350604222116, `97.5%` = 0.729903013043148, `97.5%` = 1.23628887068288, `97.5%` = 0.735322757301528, `97.5%` = 0.721254548490607, `97.5%` = 0.547821159342026, `97.5%` = 0.0901610144027345, `97.5%` = 0.724174566356542, `97.5%` = 0.75034324181327, `97.5%` = 0.709982748482133, 
-`97.5%` = 0.719605886783329, `97.5%` = 0.774791684477986, `97.5%` = 0.735072184820506, `97.5%` = 0.723815080592094)
-c(`5%` = -0.558975272641589, `5%` = -0.481208746850505, `5%` = -0.558122801520996, `5%` = -0.566607488065583, `5%` = -0.571443956067767, `5%` = -0.560863708568709, `5%` = -0.733995288939712, `5%` = -0.573157666695002, `5%` = -0.481699496200734, `5%` = -0.584184368190383, `5%` = -0.164285973529124, `5%` = -0.561811722478321, `5%` = -0.511640963000957, `5%` = -0.649098498864267, `5%` = -1.24192236504004, `5%` = -0.514000899450722, `5%` = -0.573055024907531, `5%` = -0.589897724557502, `5%` = -0.576009342907356, 
-`5%` = -0.483515522092064, `5%` = -0.59377439249432, `5%` = -0.589781985563434)
-c(`95%` = 0.558241074532826, `95%` = 0.6343107609265, `95%` = 0.564064518165769, `95%` = 0.554270165096566, `95%` = 0.568906077580551, `95%` = 0.564421378888805, `95%` = 0.361698139473533, `95%` = 0.556521770858677, `95%` = 0.636739588170121, `95%` = 0.554481744418514, `95%` = 1.05804976340078, `95%` = 0.564219400738208, `95%` = 0.570750639989039, `95%` = 0.424983622057134, `95%` = 0.0339253948301201, `95%` = 0.576987914910334, `95%` = 0.588745135323456, `95%` = 0.565535664200477, `95%` = 0.572292114386171, 
-`95%` = 0.614652923649533, `95%` = 0.564155780832837, `95%` = 0.568721991063375)

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

@@ -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(-0.0338934915059191, -0.0424617202550975, -0.0299850538975256, -0.0228882519097089, -0.0337281404548882, -0.0258485686453499, -0.0402405594871021, -0.0253886763112486, -0.136200903561077, -0.0212623461765324, -0.102097548934776, -0.0229996389940294, -0.106049590275806, 0.103092606051222, -0.431577879697863, -0.0589099972525265, -0.0270810088184279, -0.0260112735850109, -0.0325654806803472, -0.182061588936881, -0.0261135283573324, -0.0227456753432774)
-c(`2.5%` = -0.690100665844187, `2.5%` = -0.671202369164881, `2.5%` = -0.666088312766176, `2.5%` = -0.666578142141343, `2.5%` = -0.678544709698103, `2.5%` = -0.669196847024182, `2.5%` = -0.655923003454131, `2.5%` = -0.655413313800275, `2.5%` = -0.845732099866594, `2.5%` = -0.653192374517221, `2.5%` = -0.64122257390992, `2.5%` = -0.659159394086157, `2.5%` = -0.787764109870441, `2.5%` = -0.425148647750428, `2.5%` = -1.00072959307916, `2.5%` = -0.69270592355035, `2.5%` = -0.685883418294076, `2.5%` = -0.679342853679393, 
-`2.5%` = -0.681272654592773, `2.5%` = -0.837786199816108, `2.5%` = -0.665616824031369, `2.5%` = -0.6589068176532)
-c(`97.5%` = 0.607853806397433, `97.5%` = 0.575243002929989, `97.5%` = 0.623466621187216, `97.5%` = 0.641896362473495, `97.5%` = 0.611672327879679, `97.5%` = 0.621633709372348, `97.5%` = 0.556891230159617, `97.5%` = 0.626653736033388, `97.5%` = 0.426452209102124, `97.5%` = 0.604814400002199, `97.5%` = 0.370375731808055, `97.5%` = 0.656507843091209, `97.5%` = 0.463728458888126, `97.5%` = 0.803945161990884, `97.5%` = 0.00182348352747238, `97.5%` = 0.534212402242803, `97.5%` = 0.609945240239135, `97.5%` = 0.637145158270836, 
-`97.5%` = 0.595960590167125, `97.5%` = 0.325103240011011, `97.5%` = 0.639977001719915, `97.5%` = 0.645740444211412)
-c(`5%` = -0.543756319725243, `5%` = -0.53599965672411, `5%` = -0.53861642350383, `5%` = -0.536297908680718, `5%` = -0.535626704345617, `5%` = -0.534014891414532, `5%` = -0.515645442393892, `5%` = -0.521060513099755, `5%` = -0.698732119314075, `5%` = -0.513828973026094, `5%` = -0.536163536290694, `5%` = -0.520608887905471, `5%` = -0.637108697299896, `5%` = -0.333645087620771, `5%` = -0.901047800865227, `5%` = -0.558977042638121, `5%` = -0.528351288300235, `5%` = -0.531154576765547, `5%` = -0.535401625627599, 
-`5%` = -0.704974120731683, `5%` = -0.530102478693174, `5%` = -0.519892009691237)
-c(`95%` = 0.476219882900217, `95%` = 0.43869836398252, `95%` = 0.487869109380049, `95%` = 0.495857205487324, `95%` = 0.477771545962831, `95%` = 0.479824548055006, `95%` = 0.431579111164995, `95%` = 0.489104423628694, `95%` = 0.318624951651975, `95%` = 0.475052338519103, `95%` = 0.285016338923033, `95%` = 0.498321179113614, `95%` = 0.356019293245049, `95%` = 0.645249978382246, `95%` = -0.0484915873069474, `95%` = 0.422234919504667, `95%` = 0.474969816313099, `95%` = 0.495469548426488, `95%` = 0.459482254828791, 
-`95%` = 0.237533499815169, `95%` = 0.491700443982918, `95%` = 0.504132013362862)

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

@@ -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(-0.101589582550983, -0.55509837146517, -0.0876297898120394, -0.00424167706294526, -0.0856815547958816, -0.000891733412756743, -0.656064432872784, -0.00724344466650777, 1.39799100967958, -0.162272605765493, 0.674878170950668, 0.00679859198995776, 0.256393632879033, -1.39692218698997, 0.469486930453339, 0.201756832022176, -0.000743046330130356, -0.00239703676867723, -0.101293376110023, 0.334667664170298, 0.0036268316699096, -0.0553940979488865)
-c(`2.5%` = -1.50442203323384, `2.5%` = -1.84877532048521, `2.5%` = -1.4700191365573, `2.5%` = -1.37500277258178, `2.5%` = -1.4848837602478, `2.5%` = -1.32756197431566, `2.5%` = -1.85894539233901, `2.5%` = -1.33085969325607, `2.5%` = 0.16999404397094, `2.5%` = -1.53594319207073, `2.5%` = -0.0683721763680483, `2.5%` = -1.33523195430546, `2.5%` = -0.987723094632562, `2.5%` = -2.58976230693659, `2.5%` = 0.0834766521350615, `2.5%` = -0.475822349223494, `2.5%` = -1.33731236431054, `2.5%` = -1.35448275771273, 
-`2.5%` = -1.42832958185923, `2.5%` = -0.797988116140275, `2.5%` = -1.3168218907587, `2.5%` = -1.41268885278703)
-c(`97.5%` = 1.22021244556502, `97.5%` = 0.475219087766019, `97.5%` = 1.2205452457074, `97.5%` = 1.38725107457264, `97.5%` = 1.24763911953619, `97.5%` = 1.36828112117342, `97.5%` = 0.304797046306457, `97.5%` = 1.2809727123191, `97.5%` = 2.97850864225959, `97.5%` = 1.1200916681713, `97.5%` = 1.48172994699915, `97.5%` = 1.35752400157911, `97.5%` = 1.59974497777273, `97.5%` = -0.420429798817082, `97.5%` = 0.874242905367939, `97.5%` = 0.869912231337424, `97.5%` = 1.32550284173821, `97.5%` = 1.35888949832736, 
-`97.5%` = 1.1936539738932, `97.5%` = 1.609867105785, `97.5%` = 1.34949094435626, `97.5%` = 1.22944948085911)
-c(`5%` = -1.22562889252442, `5%` = -1.59673376165319, `5%` = -1.21885811476843, `5%` = -1.11813404961243, `5%` = -1.20918844446486, `5%` = -1.07055731145333, `5%` = -1.6236745264581, `5%` = -1.07506861425794, `5%` = 0.318380069304849, `5%` = -1.29675430841515, `5%` = 0.041344205634408, `5%` = -1.10591252208745, `5%` = -0.740508923444193, `5%` = -2.36760582705321, `5%` = 0.147263440981433, `5%` = -0.359590775683247, `5%` = -1.09935756841891, `5%` = -1.10179682888056, `5%` = -1.19332341912876, `5%` = -0.607636638304879, 
-`5%` = -1.08485266813522, `5%` = -1.13445756592737)
-c(`95%` = 0.972806121648346, `95%` = 0.338841318556515, `95%` = 0.988158619006595, `95%` = 1.09780676333781, `95%` = 1.00794578824477, `95%` = 1.1020724405486, `95%` = 0.184033329014398, `95%` = 1.04560505760623, `95%` = 2.69222620971344, `95%` = 0.884777865331596, `95%` = 1.35166837167041, `95%` = 1.11527199108615, `95%` = 1.36231797243719, `95%` = -0.54782380504844, `95%` = 0.80963853468214, `95%` = 0.767827618485196, `95%` = 1.08512970421361, `95%` = 1.08918975608724, `95%` = 0.953418962916104, 
-`95%` = 1.37967304828955, `95%` = 1.09273387336662, `95%` = 1.00375347161432)