Added all the updates for fixed run

I ran the analysis and got the following updated results.

r-analysis/EffectsOfEnrollmentDelay.rmarkdown

@@ -12,6 +12,8 @@ editor: source
 
 
 
+
+
 ```{r}
 library(knitr)
 library(bayesplot)
@@ -41,10 +43,19 @@ image_parameters_by_groups <-paste0(image_root,"/betas/parameters_by_group")
 image_parameters_across_groups <-paste0(image_root,"/betas/parameter_across_groups")
 ```
 
+
+
+run on `r .QuartoInlineRender(now(tz='UTC'))`
+
+
+
+
+
 ```{r}
 ################ Pull data from database ######################
 library(RPostgreSQL)
-host <- 'aact_db-restored-2025-01-07'
+#host <-'aact_db-restored-2025-01-07'
+host <- '10.89.0.6'
 
 driver <- dbDriver("PostgreSQL")
 
@@ -441,6 +452,7 @@ counterfact_delay <- list(
 ```
 
 ```{r}
+#| label: Fitting
 fit <- stan(
     file='Hierarchal_Logistic.stan', 
     data = counterfact_delay,
@@ -509,7 +521,7 @@ 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(paste0(image_trial_details,"/HistSnapshots.png"))
+ggsave(paste0(image_trial_details,"/HistSnapshots.png"), create.dir = TRUE)
 
 #Plot duration for terminated vs completed
 df4 <- dbGetQuery(
@@ -532,7 +544,7 @@ ggplot(data=df4, aes(x=duration,fill=overall_status)) +
     ggtitle("Histogram of trial durations") +
     xlab("duration")+
     facet_wrap(~overall_status)
-ggsave(paste0(image_trial_details,"/HistTrialDurations_Faceted.png"))
+ggsave(paste0(image_trial_details,"/HistTrialDurations_Faceted.png"), create.dir = TRUE)
 
 df5 <- dbGetQuery(
     con,
@@ -563,7 +575,7 @@ ggplot(data=df5, aes(x=duration,y=snapshot_count,color=overall_status)) +
     ggtitle("Comparison of duration, status, and snapshot_count") +
     xlab("duration") +
     ylab("snapshot count") 
-ggsave(paste0(image_trial_details,"/SnapshotsVsDurationVsTermination.png"))
+ggsave(paste0(image_trial_details,"/SnapshotsVsDurationVsTermination.png"), create.dir = TRUE)
 
 dbDisconnect(con)
 
@@ -579,7 +591,7 @@ ggplot(data = group_trials_by_category, aes(x=category_id)) +
         ,x="Category ID"
         ,y="Count"
     )
-ggsave(paste0(image_trial_details,"/CategoryCounts.png"))
+ggsave(paste0(image_trial_details,"/CategoryCounts.png"), create.dir = TRUE)
     
 
 
@@ -591,8 +603,20 @@ count_snapshots <- sum(df5$snapshot_count)
 
 
 
-the correlation value is `r cor_dur_count` between duration and snapshot count.
-There are `r count_snapshots` snapshots in total.
+the correlation value is `r .QuartoInlineRender(cor_dur_count)` between duration and snapshot count.
+There are `r .QuartoInlineRender(count_snapshots)` snapshots in total, spread over 
+`r .QuartoInlineRender(length(df5$nct_id))` trials.
+
+The number of categories by trial are
+TODO: add in category names, 
+
+
+```{r}
+group_trials_by_category |> count(category_id)
+```
+
+
+
 
 
 
@@ -615,7 +639,7 @@ for (i in category_count$category_id[category_count$n >= 0]) {
     gi <- group_mcmc_areas("beta",beta_list,fit,i) #add way to filter groups
     ggsave(
         paste0(image_parameters_by_groups,"/group_",i,"_",gi$name,".png")
-        ,plot=gi$plot
+        ,plot=gi$plot, create.dir = TRUE
         )
     gx <- c(gx,gi)
 
@@ -643,7 +667,7 @@ for (i in c(1,2,3,9,10,11,12)) {
     pi <- parameter_mcmc_areas("beta",beta_list,fit,i) #add way to filter groups
     ggsave(
         paste0(image_parameters_across_groups,"/parameters_",i,"_",pi$name,".png")
-        ,plot=pi$plot
+        ,plot=pi$plot, create.dir = TRUE
         )
     px <- c(px,pi)
 
@@ -710,7 +734,7 @@ ggplot(df_ib_p, aes(x=p_prior)) +
         ,x="Probability Domain 'p'"
         ,y="Probability Density"
     )
-ggsave(paste0(image_dist_diff_analysis,"/prior_p.png"))
+ggsave(paste0(image_dist_diff_analysis,"/prior_p.png"), create.dir = TRUE)
 
 #p_posterior
 ggplot(df_ib_p, aes(x=p_predicted)) +
@@ -721,7 +745,7 @@ ggplot(df_ib_p, aes(x=p_predicted)) +
         ,x="Probability Domain 'p'"
         ,y="Probability Density"
     )
-ggsave(paste0(image_dist_diff_analysis,"/posterior_p.png"))
+ggsave(paste0(image_dist_diff_analysis,"/posterior_p.png"), create.dir = TRUE)
 
 #mu_prior
 ggplot(df_ib_prior) +
@@ -732,7 +756,7 @@ ggplot(df_ib_prior) +
         ,x="Mu"
         ,y="Probability"
     )
-ggsave(paste0(image_dist_diff_analysis,"/prior_mu.png"))
+ggsave(paste0(image_dist_diff_analysis,"/prior_mu.png"), create.dir = TRUE)
 
 #sigma_posterior
 ggplot(df_ib_prior) +
@@ -743,7 +767,7 @@ ggplot(df_ib_prior) +
         ,x="Sigma"
         ,y="Probability"
     )
-ggsave(paste0(image_dist_diff_analysis,"/prior_sigma.png"))
+ggsave(paste0(image_dist_diff_analysis,"/prior_sigma.png"), create.dir = TRUE)
 ```
 
 
@@ -753,7 +777,7 @@ ggsave(paste0(image_dist_diff_analysis,"/prior_sigma.png"))
 
 
 
-### Intervention: Delay close of enrollment
+# Intervention: Delay close of enrollment
 
 
 
@@ -773,7 +797,7 @@ ggplot(counterfact_predicted_ib, aes(x=p_predicted_default)) +
         ,x="Probability Domain 'p'"
         ,y="Probability Density"
     )
-ggsave(paste0(image_dist_diff_analysis,"p_no_intervention.png"))
+ggsave(paste0(image_dist_diff_analysis,"/p_no_intervention.png"), create.dir = TRUE)
 
 ggplot(counterfact_predicted_ib, aes(x=p_predicted_intervention)) +
     geom_density() + 
@@ -783,7 +807,7 @@ ggplot(counterfact_predicted_ib, aes(x=p_predicted_intervention)) +
         ,x="Probability Domain 'p'"
         ,y="Probability Density"
     )
-ggsave(paste0(image_dist_diff_analysis,"/p_delay_intervention.png"))
+ggsave(paste0(image_dist_diff_analysis,"/p_delay_intervention.png"), create.dir = TRUE)
 
 ggplot(counterfact_predicted_ib, aes(x=predicted_difference)) +
     geom_density() + 
@@ -793,7 +817,7 @@ ggplot(counterfact_predicted_ib, aes(x=predicted_difference)) +
         ,x="Difference in 'p' under treatment"
         ,y="Probability Density"
     )
-ggsave(paste0(image_dist_diff_analysis,"/p_delay_intervention_distdiff_1.png"))
+ggsave(paste0(image_dist_diff_analysis,"/p_delay_intervention_distdiff_1.png"), create.dir = TRUE)
 ```
 
 ```{r}
@@ -815,7 +839,7 @@ pddf_ib <- data.frame(extract(generated_ib, pars="predicted_difference")$predict
     pivot_longer(X1:X168) #CHANGE_NOTE: moved from X169 to X168
 
 pddf_ib["entry_idx"] <- as.numeric(gsub("\\D","",pddf_ib$name))
-pddf_ib["category"] <-  sapply(pddf_ib$entry_idx, function(i) df$category_id[i])
+pddf_ib["category"] <-  sapply(pddf_ib$entry_idx, function(i) counterfact_delay$llx[i])
 pddf_ib["category_name"] <- sapply(
     pddf_ib$category, 
     function(i) category_names[i]
@@ -832,7 +856,7 @@ ggplot(pddf_ib, aes(x=value,)) +
         ,y = "Probability Density"
     ) + 
     geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") 
-ggsave(paste0(image_dist_diff_analysis,"/p_delay_intervention_distdiff_styled.png"))
+ggsave(paste0(image_dist_diff_analysis,"/p_delay_intervention_distdiff_styled.png"), create.dir = TRUE)
 ```
 
 ```{r}
@@ -853,7 +877,7 @@ ggplot(pddf_ib, aes(x=value,)) +
     ) + 
     geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") +
     theme(strip.text.x = element_text(size = 8))
-ggsave(paste0(image_dist_diff_analysis,"/p_delay_intervention_distdiff_by_group.png"))
+ggsave(paste0(image_dist_diff_analysis,"/p_delay_intervention_distdiff_by_group.png"), create.dir = TRUE)
 ```
 
 ```{r}
@@ -874,7 +898,7 @@ ggplot(pddf_ib, aes(x=value,)) +
     ) + 
     geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") +
     theme(strip.text.x = element_text(size = 8))
-ggsave(paste0(image_dist_diff_analysis,"p_delay_intervention_histdiff_by_group.png"))
+ggsave(paste0(image_dist_diff_analysis,"/p_delay_intervention_histdiff_by_group.png"), create.dir = TRUE)
 ```
 
 ```{r}
@@ -897,6 +921,7 @@ stats <- list(
  q1 = quantile(pddf_ib$value, 0.25),
  med = median(pddf_ib$value),
  mean = mean(pddf_ib$value),
+ stdev = sd(pddf_ib$value),
  q3 = quantile(pddf_ib$value, 0.75),
  p90 = quantile(pddf_ib$value, 0.90),
  p95 = quantile(pddf_ib$value, 0.95),
@@ -952,7 +977,7 @@ p3 +
    x = stats$mean,
    y = stats$y_offset * 1.5
  ), aes(x = x, y = y))
-ggsave(paste0(image_dist_diff_analysis,"/p_delay_intervention_histdiff_boxplot.png"))
+ggsave(paste0(image_dist_diff_analysis,"/p_delay_intervention_histdiff_boxplot.png"), create.dir = TRUE)
 ```
 
 ```{r}
@@ -1011,7 +1036,7 @@ p4 <- ggplot(pddf_ib, aes(x = value)) +
    x = stats$mean,
    y = stats$y_offset_density * 1.5
  ), aes(x = x, y = y))
-ggsave(paste0(image_dist_diff_analysis,"/p_delay_intervention_distdiff_boxplot.png"))
+ggsave(paste0(image_dist_diff_analysis,"/p_delay_intervention_distdiff_boxplot.png"), create.dir = TRUE)
 
 p4
 ```
@@ -1026,7 +1051,7 @@ p4
         y = "Cumulative Proportion"
     ) 
 
-ggsave(paste0(image_dist_diff_analysis,"/p_delay_intervention_cumulative_distdiff.png"))
+ggsave(paste0(image_dist_diff_analysis,"/p_delay_intervention_cumulative_distdiff.png"), create.dir = TRUE)
 ```
 
 
@@ -1047,18 +1072,18 @@ mass_below_zero <- mean( pddf_ib$value <= 0)
 ```
 
 
-Looking at the spike around zero, we find that `r spike_band_centered_zero*100`% 
+Looking at the spike around zero, we find that `r .QuartoInlineRender(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 
+[`r .QuartoInlineRender(-width/2)`,`r .QuartoInlineRender(width/2)`].
+Additionally, there was `r .QuartoInlineRender(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
+and `r .QuartoInlineRender(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,
+`r .QuartoInlineRender(mass_below_zero * 100)`% 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$mean`.
+the mean probability of termination increases by `r .QuartoInlineRender(stats$mean (stats$stdev))`.
 
 
 
@@ -1077,7 +1102,7 @@ quant_df <- data.frame(
 )
 # Convert to LaTeX
 table <- xtable(quant_df,
-    digits = rep(3, ncol(d) + 1),
+    digits = rep(3, ncol(quant_df) + 1),
     floating = FALSE,
     latex.environments = NULL,
     booktabs = TRUE
@@ -1096,7 +1121,7 @@ proportion_increase <- mean(pddf_ib$value >= 0)
 
 
 
-about `r proportion_increase * 100` percent probability increase in the probability of terminations
+about `r .QuartoInlineRender(proportion_increase * 100)` percent probability increase in the probability of terminations
 
 
 
@@ -1106,34 +1131,74 @@ 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
+simulated_percentages <- (simulated_terminations_intervention - simulated_terminations_base)
 ```
 
 
 
 The simulation above shows that this results in a percentage-point change in terminations of about 
-`r simulated_percentages * 100`.
+`r .QuartoInlineRender(simulated_percentages * 100)`.
+
+
+
+
+```{r}
+distdiff_by_group <- pddf_ib %>% 
+    group_by(category_name, category) %>%
+    summarize(
+        mean = mean(value, na.rm=TRUE) *100
+        ,"P(p>0)" = mean(value>0) *100
+        ,"5%"    = quantile( value, 0.05, na.rm = TRUE)*100
+        ,"25%"   = quantile( value, 0.25, na.rm = TRUE)*100
+        ,"median" = quantile( value, 0.5, na.rm = TRUE)*100
+        ,"75%"   = quantile( value, 0.75, na.rm = TRUE)*100
+        ,"95%"   = quantile( value, 0.95, na.rm = TRUE)*100
+    ) %>% arrange(category) %>% select(!category)
+    
+distdif_by_group_latex<- xtable(distdiff_by_group,
+    digits = rep(2, ncol(distdiff_by_group) +1),
+    floating = FALSE,
+    align = "llccccccc",
+    latex.environments = "tabularx",
+    booktabs = TRUE
+)
+
+# Write to file
+write_lines(
+    print(distdif_by_group_latex, include.rownames = FALSE),
+    paste0(image_root,"/distdiff_anr_vs_rec_by_group.tex")
+)
+
+print(distdif_by_group_latex)
+distdiff_by_group
+```
+
+
+
+
 
 ## fixed effects distributions
 
 
 
 ```{r}
+#| label: Fixed Effects Distributions
 #Get dataframe with only the rows of interest
 filtdata <- as.data.frame(extract(fit, pars="status_diff"))
 #rename columns
-dimnames(filtdata)[[2]] <- beta_list$groups
+dimnames(filtdata)[[2]] <- sprintf("%02d %s", seq_along(beta_list$groups), beta_list$groups)
 #create area plot with appropriate title
 mcmc_areas(filtdata,prob = 0.8, prob_outer = 0.95) +
     ggtitle("Differences in Fixed Effects | By ICD-10 Category", 
             subtitle = "Moving from 'Active, not recruiting' to 'Recruiting'"
         ) +
     geom_vline(xintercept=seq(-0.25,0.5,0.25),color="grey",alpha=0.750)  
-ggsave(paste0(image_parameters_across_groups,"/fixed_effects_anr_vs_rec_by_group.png"))
+ggsave(paste0(image_parameters_across_groups,"/fixed_effects_anr_vs_rec_by_group.png"), create.dir = TRUE)
     
-d <- pivot_longer(filtdata, everything()) |> 
+fixed_effects_table <- pivot_longer(filtdata, everything()) |> 
     group_by(name) |> 
     summarize(
+        `sample size` = 0,
         mean = mean(value),
         `P(≥0)` = mean(value >= 0),
         `2.5%` = quantile(value, probs = 0.025),
@@ -1145,18 +1210,26 @@ d <- pivot_longer(filtdata, everything()) |>
         `97.5%` = quantile(value, probs = 0.975)
     )
 
+# Convert the indexing to be consecutive by using a temporary vector
+sample_sizes <- rep(0, 22)  # Create vector of zeros
+sample_sizes[category_count$category_id] <- category_count$n
+
+# Now assign the whole vector at once
+fixed_effects_table["sample size"] <- sample_sizes
 # Rename the name column
-names(d)[1] <- "ICD-10 Category"
+names(fixed_effects_table)[1] <- "ICD-10 Category"
 
 # Convert to LaTeX
-table <- xtable(d,
-    digits = rep(3, ncol(d) + 1),
+table <- xtable(fixed_effects_table,
+    digits = rep(2, ncol(fixed_effects_table) + 1),
     floating = FALSE,
-    latex.environments = NULL,
+    align = "lccccccccccc",
+    latex.environments = "tabularx",
     booktabs = TRUE
 )
 
 
+
 # Write to file
 write_lines(
     print(table, include.rownames = FALSE),
@@ -1175,12 +1248,13 @@ write_lines(
 
 
 ```{r}
-#| eval: true
+#| label: diagnostics 1
+#| eval: false
 #trace plots
 image_diagnostics <- paste0(image_root,"/diagnostics")
 
 plot(fit, pars=c("mu"), plotfun="trace")
-ggsave(paste0(image_diagnostics,"/trace_plot_mu.png"))
+ggsave(paste0(image_diagnostics,"/trace_plot_mu.png"), create.dir = TRUE)
 
 
 for (i in 1:3) {
@@ -1199,14 +1273,15 @@ for (i in 1:3) {
     )
     mu_range <- paste0(4*i-3,"-",4*i)
     filename <- paste0(image_diagnostics,"/trace_rank_plot_mu_",mu_range,".png")
-    ggsave(filename)
+    ggsave(filename, create.dir = TRUE)
 }
 ```
 
 ```{r}
-#| eval: true
+#| label: diagnostics 2
+#| eval: false
 plot(fit, pars=c("sigma"), plotfun="trace")
-ggsave(paste0(image_diagnostics,"/traceplot_sigma.png"))
+ggsave(paste0(image_diagnostics,"/traceplot_sigma.png"), create.dir = TRUE)
 
 for (i in 1:3) {
     print(
@@ -1224,12 +1299,13 @@ for (i in 1:3) {
     )
     sigma_range <- paste0(4*i-3,"-",4*i)
     filename <- paste0(image_diagnostics,"/trace_rank_plot_sigma_",sigma_range,".png")
-    ggsave(filename)
+    ggsave(filename, create.dir = TRUE)
 }
 ```
 
 ```{r}
-#| eval: true
+#| label: diagnostics 3
+#| eval: false
 #other diagnostics
 logpost <- log_posterior(fit)
 nuts_prmts <- nuts_params(fit)
@@ -1238,15 +1314,17 @@ posterior <- as.array(fit)
 ```
 
 ```{r}
-#| eval: true
+#| label: diagnostics 4
+#| 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)
-ggsave(paste0(image_diagnostics,"/parcoord_mu.png"))
+ggsave(paste0(image_diagnostics,"/parcoord_mu.png"), create.dir = TRUE)
 ```
 
 ```{r}
-#| eval: true
+#| label: diagnostics 5
+#| eval: false
 for (i in 1:3) {
     mus = sapply(3:0, function(j) paste0("mu[",4*i-j ,"]"))
     print(
@@ -1262,7 +1340,7 @@ for (i in 1:3) {
     )
     mu_range <- paste0(4*i-3,"-",4*i)
     filename <- paste0(image_diagnostics,"/correlation_plot_mu_",mu_range,".png")
-    ggsave(filename)
+    ggsave(filename, create.dir = TRUE)
 }
 
 
@@ -1270,13 +1348,15 @@ for (i in 1:3) {
 ```
 
 ```{r}
-#| eval: true
+#| label: diagnostics 6
+#| eval: false
 mcmc_parcoord(posterior,regex_pars = "sigma", np=nuts_prmts, alpha=0.05)
-ggsave(paste0(image_diagnostics,"/parcoord_sigma.png"))
+ggsave(paste0(image_diagnostics,"/parcoord_sigma.png"), create.dir = TRUE)
 ```
 
 ```{r}
-#| eval: true
+#| label: diagnostics 7
+#| eval: false
 
 for (i in 1:3) {
     params = sapply(3:0, function(j) paste0("sigma[",4*i-j ,"]"))
@@ -1293,12 +1373,13 @@ for (i in 1:3) {
     )
     sigma_range <- paste0(4*i-3,"-",4*i)
     filename <- paste0(image_diagnostics,"/correlation_plot_sigma_",sigma_range,".png")
-    ggsave(filename)
+    ggsave(filename, create.dir = TRUE)
 }
 ```
 
 ```{r}
-#| eval: true
+#| eval: false
+#| label: diagnostics 8
 for (k in 1:22) {
 for (i in 1:3) {
     params = sapply(3:0, function(j) paste0("beta[",k,",",4*i-j ,"]"))
@@ -1316,7 +1397,7 @@ for (i in 1:3) {
     
     beta_range <- paste0("k_",k,"_i_",4*i-3,"-",4*i)
     filename <- paste0(image_diagnostics,"/correlation_plot_beta_",beta_range,".png")
-    ggsave(filename)
+    ggsave(filename, create.dir = TRUE)
 }}
 ```
 

r-analysis/EoED_Diagnostics.qmd

@@ -0,0 +1,75 @@
+---
+title: "TrialCountExtraction"
+author: "Will"
+format: html
+editor: source
+---
+
+
+```{r}
+#| eval: false
+#| include: true
+
+#Full set
+categories %>% unique() %>% sort() %>% length()
+
+#Evaluation set
+cf_categories %>% unique() %>% sort() %>% length()
+
+```
+
+
+```{r}
+# Pulled from df
+group_trials_by_category %>% group_by(category_id) %>% count()
+```
+
+# Actual data from Evaluation and counterfactual
+```{r}
+# Original Evaluation
+# - Pulled from `categories` above when defined
+counterfact_delay$ll %>% unique() %>% sort() %>% length()
+
+
+# Counterfactual
+# - Pulled from `cf_categories` above when defined
+counterfact_delay$llx %>% unique() %>% sort() %>% length()
+```
+Those came from
+```{r}
+df$category_id %>% unique() %>% sort() %>% length()
+df_counterfact_base$category_id %>% unique() %>% sort() %>% length()
+```
+
+The difference between those is that the counterfactual imposes the constraint
+that there must be a snapshot where it moves from "ANR" to "Rec", implying that
+it can't just terminate.
+
+# Where do the other values drop
+
+When we find the counterfactual, the table looses some of the categories etc.
+Here is the extracted data
+
+```{r}
+data.frame(extract(generated_ib, pars="predicted_difference")$predicted_difference)
+```
+
+```{r}
+pddf_ib <- data.frame(extract(generated_ib, pars="predicted_difference")$predicted_difference) |>
+    pivot_longer(X1:X168) #CHANGE_NOTE: moved from X169 to X168
+
+
+pddf_ib["entry_idx"] <- as.numeric(gsub("\\D","",pddf_ib$name))
+pddf_ib["category"] <-  sapply(pddf_ib$entry_idx, function(i) counterfact_delay$llx[i])
+pddf_ib["category_name"] <- sapply(
+    pddf_ib$category, 
+    function(i) category_names[i]
+    )
+```
+and yet it seems that we predict the difference for all 168 trials
+
+It looks like there is an error where I apply category IDs. Because I'm pulling them from 
+```{r}
+ground_truth <- df$category_id[1:168]
+```
+

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

@@ -1,34 +0,0 @@
-% latex table generated in R 4.4.2 by xtable 1.8-4 package
-% Mon Feb  3 23:56:27 2025
-\begin{table}[ht]
-\centering
-\begin{tabular}{ccccccccccc}
-  \hline
-ICD-10 Category & sample size & mean & P(≥0) & 2.5\% & 5\% & 25\% & 50\% median & 75\% & 95\% & 97.5\% \\ 
-  \hline
-01 Infections \& Parasites & 20.000 & 0.135 & 0.772 & -0.223 & -0.163 & 0.013 & 0.134 & 0.257 & 0.438 & 0.498 \\ 
-  02 Neoplasms & 49.000 & 0.211 & 0.902 & -0.107 & -0.056 & 0.099 & 0.208 & 0.320 & 0.489 & 0.546 \\ 
-  03 Blood \& Immune system & 1.000 & 0.061 & 0.624 & -0.333 & -0.267 & -0.071 & 0.061 & 0.194 & 0.388 & 0.454 \\ 
-  04 Endocrine, Nutritional, and Metabolic & 10.000 & 0.177 & 0.820 & -0.201 & -0.137 & 0.045 & 0.173 & 0.304 & 0.505 & 0.573 \\ 
-  05 Mental \& Behavioral & 8.000 & 0.142 & 0.767 & -0.239 & -0.175 & 0.010 & 0.140 & 0.270 & 0.469 & 0.537 \\ 
-  06 Nervous System & 8.000 & 0.009 & 0.524 & -0.383 & -0.315 & -0.120 & 0.011 & 0.140 & 0.327 & 0.389 \\ 
-  07 Eye and Adnexa & 11.000 & 0.061 & 0.624 & -0.333 & -0.265 & -0.069 & 0.062 & 0.192 & 0.386 & 0.452 \\ 
-  08 Ear and Mastoid & 0.000 & 0.063 & 0.625 & -0.332 & -0.263 & -0.069 & 0.064 & 0.196 & 0.393 & 0.457 \\ 
-  09 Circulatory & 3.000 & 0.037 & 0.577 & -0.362 & -0.293 & -0.094 & 0.038 & 0.170 & 0.363 & 0.426 \\ 
-  10 Respiratory & 7.000 & 0.061 & 0.622 & -0.335 & -0.269 & -0.072 & 0.061 & 0.195 & 0.391 & 0.458 \\ 
-  11 Digestive & 12.000 & 0.060 & 0.621 & -0.339 & -0.270 & -0.071 & 0.059 & 0.192 & 0.388 & 0.454 \\ 
-  12 Skin \& Subcutaneaous tissue & 9.000 & 0.104 & 0.704 & -0.283 & -0.220 & -0.027 & 0.103 & 0.234 & 0.429 & 0.494 \\ 
-  13 Musculoskeletal & 17.000 & 0.159 & 0.794 & -0.218 & -0.156 & 0.027 & 0.156 & 0.286 & 0.482 & 0.550 \\ 
-  14 Genitourinary & 2.000 & 0.063 & 0.627 & -0.337 & -0.270 & -0.070 & 0.065 & 0.195 & 0.389 & 0.456 \\ 
-  15 Pregancy, Childbirth, \& Puerperium & 0.000 & 0.061 & 0.622 & -0.336 & -0.270 & -0.071 & 0.061 & 0.194 & 0.392 & 0.458 \\ 
-  16 Perinatal Period & 0.000 & 0.062 & 0.626 & -0.338 & -0.270 & -0.068 & 0.062 & 0.194 & 0.390 & 0.457 \\ 
-  17 Congential & 3.000 & 0.063 & 0.628 & -0.332 & -0.264 & -0.068 & 0.063 & 0.195 & 0.391 & 0.456 \\ 
-  18 Symptoms, Signs etc. & 1.000 & 0.062 & 0.625 & -0.332 & -0.265 & -0.069 & 0.062 & 0.194 & 0.390 & 0.456 \\ 
-  19 Injury etc. & 0.000 & 0.063 & 0.625 & -0.337 & -0.268 & -0.069 & 0.062 & 0.196 & 0.392 & 0.459 \\ 
-  20 External Causes & 0.000 & 0.063 & 0.625 & -0.330 & -0.266 & -0.070 & 0.064 & 0.194 & 0.392 & 0.460 \\ 
-  21 Contact with Healthcare & 0.000 & 0.063 & 0.627 & -0.335 & -0.267 & -0.070 & 0.064 & 0.194 & 0.390 & 0.454 \\ 
-  22 Special Purposes & 0.000 & 0.062 & 0.625 & -0.335 & -0.268 & -0.070 & 0.062 & 0.194 & 0.391 & 0.456 \\ 
-   \hline
-\end{tabular}
-\end{table}
-

r-analysis/output/EffectsOfEnrollmentDelay/distdiff_5percentile_table.tex

@@ -1,5 +1,5 @@
-% latex table generated in R 4.4.2 by xtable 1.8-4 package
-% Sun Feb  2 01:37:17 2025
+% latex table generated in R 4.5.1 by xtable 1.8-4 package
+% Mon Mar 23 21:59:57 2026
 \begin{table}[ht]
 \centering
 \begin{tabular}{r}

r-analysis/output/EffectsOfEnrollmentDelay/distdiff_anr_vs_rec_by_group.tex

@@ -0,0 +1,26 @@
+% latex table generated in R 4.5.1 by xtable 1.8-4 package
+% Mon Mar 23 23:42:59 2026
+\begin{table}[ht]
+\centering
+\begin{tabular}{lcccccccc}
+  \hline
+category\_name & N & mean & P(p$>$0) & 5\% & 25\% & median & 75\% & 95\% \\ 
+  \hline
+ICD-10 \#1: Infections \& Parasites (n=20) & XX & 1.60 & 77.23 & -1.79 & 0.11 & 1.19 & 2.77 & 6.30 \\ 
+  ICD-10 \#2: Neoplasms (n=49) & XX & 3.63 & 90.17 & -0.90 & 1.52 & 3.37 & 5.52 & 9.06 \\ 
+  ICD-10 \#3: Blood \& Immune system (n=1) & XX & 0.15 & 62.35 & -0.80 & -0.02 & 0.01 & 0.16 & 1.63 \\ 
+  ICD-10 \#4: Endocrine, Nutritional, and Metabolic (n=10) & XX & 2.23 & 82.05 & -1.75 & 0.52 & 2.03 & 3.77 & 6.89 \\ 
+  ICD-10 \#5: Mental \& Behavioral (n=8) & XX & 3.42 & 76.72 & -4.21 & 0.24 & 3.36 & 6.47 & 11.28 \\ 
+  ICD-10 \#6: Nervous System (n=8) & XX & 0.05 & 52.37 & -2.98 & -0.83 & 0.07 & 0.95 & 3.01 \\ 
+  ICD-10 \#7: Eye and Adnexa (n=11) & XX & 0.03 & 62.40 & -0.13 & -0.01 & 0.00 & 0.04 & 0.25 \\ 
+  ICD-10 \#9: Circulatory (n=3) & XX & 0.69 & 57.69 & -5.03 & -1.33 & 0.50 & 2.66 & 6.88 \\ 
+  ICD-10 \#10: Respiratory (n=7) & XX & 0.04 & 62.21 & -0.17 & -0.00 & 0.00 & 0.03 & 0.37 \\ 
+  ICD-10 \#11: Digestive (n=12) & XX & 0.02 & 62.13 & -0.10 & -0.00 & 0.00 & 0.02 & 0.18 \\ 
+  ICD-10 \#12: Skin \& Subcutaneaous tissue (n=9) & XX & 0.55 & 70.42 & -1.06 & -0.11 & 0.41 & 1.10 & 2.56 \\ 
+  ICD-10 \#13: Musculoskeletal (n=17) & XX & 1.16 & 79.42 & -1.13 & 0.18 & 1.02 & 2.02 & 3.93 \\ 
+  ICD-10 \#14: Genitourinary (n=2) & XX & 0.13 & 62.72 & -0.54 & -0.01 & 0.00 & 0.10 & 1.27 \\ 
+  ICD-10 \#17: Congential (n=3) & XX & 0.06 & 62.82 & -0.27 & -0.00 & 0.00 & 0.05 & 0.63 \\ 
+   \hline
+\end{tabular}
+\end{table}
+

r-analysis/r-analysis.Rproj

@@ -1,4 +1,5 @@
 Version: 1.0
+ProjectId: af114abe-061f-43bf-8ae1-a953900da3ff
 
 RestoreWorkspace: Yes
 SaveWorkspace: Ask