---
title: "Simplified - The Effects of Recruitment status on completion of clinical trials"
author: "Will King"
format: html
editor: source
---

The purpose of this is to help me investigate some of my code. It was not used to perform the analysis.

# Setup



```{r}
library(knitr)
library(bayesplot)
available_mcmc(pattern = "_nuts_")
library(ggplot2)
library(patchwork)
library(tidyverse)
library(rstan)
library(tidyr)
library(ghibli)
library(xtable)
#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)


image_root <- "./output/EffectsOfEnrollmentDelay"
image_dist_diff_analysis <- paste0(image_root,"/dist_diff_analysis")
image_trial_details <-paste0(image_root,"/trials_details")
image_parameters_by_groups <-paste0(image_root,"/betas/parameters_by_group")
image_parameters_across_groups <-paste0(image_root,"/betas/parameter_across_groups")
```

run on `{r} now(tz='UTC')`



```{r}
################ Pull data from database ######################
library(RPostgreSQL)
#host <-'aact_db-restored-2025-01-07'
host <- '10.89.0.6'

driver <- dbDriver("PostgreSQL")

get_data <- function(driver) {

con <- dbConnect(
    driver,
    user='root',
    password='root',
    dbname='aact_db',
    host=host
    )
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_counts 
    ,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_brand_counts_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))
}


get_counterfact_base <- function(driver) {

con <- dbConnect(
    driver,
    user='root',
    password='root',
    dbname='aact_db',
    host=host
    )
on.exit(dbDisconnect(con))

query <- dbSendQuery(
    con,
    "
    with cte as (
    --get last recruiting state
    select fd.nct_id, max(fd.earliest_date_observed),min(fd2.earliest_date_observed) as tmstmp
    from formatted_data fd 
    	join formatted_data fd2 
    	on fd.nct_id=fd2.nct_id and fd.earliest_date_observed < fd2.earliest_date_observed 
    where fd.current_status = 'Recruiting'
    	and fd2.current_status = 'Active, not recruiting'
    group by fd.nct_id 
    )
    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_counts 
        ,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_brand_counts_through_uspdc ntbtu
            on fdqpe.nct_id = ntbtu.nct_id 
    	join cte 
    		on fdqpe.nct_id = cte.nct_id 
    			and fdqpe.earliest_date_observed = cte.tmstmp
    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

cf <- get_counterfact_base(driver)
df_counterfact_base <- cf$data
```

```{r}
#number of trials in the base dataset 
df$nct_id |> unique() %>% length
#Number of trials in the conterfactual dataset 
df_counterfact_base$nct_id %>% unique %>% length

```


```{r}
################ Format Data ###########################

data_formatter <- function(df) {
categories <- df["category_id"]

x <- df["elapsed_duration"]
x["identical_brands"] <- asinh(df$identical_brands)
x["brand_name_counts"] <- asinh(df$brand_name_count)
x["h_sdi_val"] <- asinh(df$h_sdi_val)
x["hm_sdi_val"] <- asinh(df$hm_sdi_val)
x["m_sdi_val"] <- asinh(df$m_sdi_val)
x["lm_sdi_val"] <- asinh(df$lm_sdi_val)
x["l_sdi_val"] <- asinh(df$l_sdi_val)


#Setup fixed effects
x["status_NYR"] <- ifelse(df["current_status"]=="Not yet recruiting",1,0)
x["status_EBI"] <- ifelse(df["current_status"]=="Enrolling by invitation",1,0)
x["status_Rec"] <- ifelse(df["current_status"]=="Recruiting",1,0) 
x["status_ANR"] <- ifelse(df["current_status"]=="Active, not recruiting",1,0)


y <- ifelse(df["final_status"]=="Terminated",1,0)

#get category list


return(list(x=x,y=y))
}

train <- data_formatter(df)
counterfact_base <- data_formatter(df_counterfact_base)

categories <- df$category_id

x <- as.matrix(train$x)
y <- as.vector(train$y)

x_cf_base <- counterfact_base$x 
y_cf_base <- counterfact_base$y
cf_categories <- df_counterfact_base$category_id
```

note how the base training has `{r} length(y)` snapshots while 
the counterfactual has `{r} length(y_cf_base)`.

There are `{r} categories |> unique() %>% length` categories 
respresented in the base dataset and 
`{r} cf_categories |> unique() %>% length` 
categories in the counterfactual dataset

```{r}
length(y) #Snapshots
length(y_cf_base) # trials

categories |> unique() %>% length #base categories (snapshots dataset)
cf_categories |> unique() %>% length #counterfactual categories
```


# Explore data


```{r}
#get number of trials and snapshots in each category
group_trials_by_category <- as.data.frame(aggregate(category_id ~ nct_id, df, min))
#SOMETHING GOING ON HERE ^^^^^
# It looks like it grabs the max category id, i.e. if there are multiple categories assigned, it defaults to the highest

#Trying something
group_trials_by_category2 <- as.data.frame(aggregate(category_id ~ nct_id, df, max))

for (i in 1:22) {
    print(group_trials_by_category$category_id[i] - group_trials_by_category2$category_id[i])
}
#ok, it looks like the max an min give the same categories. So I don't have to worry about multiple classifications
```


```{r}
category_count <- group_trials_by_category |> group_by(category_id) |> count()
category_count
sum(category_count$n)
```

Aggregating category counts from df, we get `{r} sum(category_count$n)`

```{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(paste0(image_trial_details,"/HistSnapshots.png"), create.dir = TRUE)

#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(paste0(image_trial_details,"/HistTrialDurations_Faceted.png"), create.dir = TRUE)

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(paste0(image_trial_details,"/SnapshotsVsDurationVsTermination.png"), create.dir = TRUE)

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="lightblue") +
    scale_x_continuous(breaks=scales::pretty_breaks(n=22)) + 
    labs(
        title="bar chart of trial categories"
        ,x="Category ID"
        ,y="Count"
    )
#ggsave(paste0(image_trial_details,"/CategoryCounts.png"), create.dir = TRUE)
    


summary(df5)

cor_dur_count <- cor(df5$duration,df5$snapshot_count)
count_snapshots <- sum(df5$snapshot_count)
```

the correlation value is `{r} cor_dur_count` between duration and snapshot count.
There are `{r} count_snapshots` snapshots in total, spread over 
`{r} 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)
```

### Tracking differences
OK, all results from df5 are from a different selection process than that of the standard result.
Turns out that there is a single trial in df5 that isn't in the counterfactual list.
```{r}
df5_trials <- df5$nct_id %>% unique
counterfact_trials <- group_trials_by_category$nct_id %>% unique

length(df5_trials) - length(counterfact_trials)
setdiff(df5_trials, counterfact_trials)
```


# Fit Model




```{r}
################################# FIT MODEL #########################################
inherited_cols <- c(
    "elapsed_duration"
    ,"identical_brands"
    ,"brand_name_counts"
    ,"h_sdi_val"
    ,"hm_sdi_val"
    ,"m_sdi_val"
    ,"lm_sdi_val"
    ,"l_sdi_val"
    ,"status_NYR"
    ,"status_EBI"
    ,"status_Rec"
    ,"status_ANR"
)
```



```{r}
beta_list <- list(
        groups = c(
        `1`="Infections & Parasites",
        `2`="Neoplasms",
        `3`="Blood & Immune system",
        `4`="Endocrine, Nutritional, and Metabolic",
        `5`="Mental & Behavioral",
        `6`="Nervous System",
        `7`="Eye and Adnexa",
        `8`="Ear and Mastoid",
        `9`="Circulatory",
        `10`="Respiratory",
        `11`="Digestive",
        `12`="Skin & Subcutaneaous tissue",
        `13`="Musculoskeletal",
        `14`="Genitourinary",
        `15`="Pregancy, Childbirth, & Puerperium",
        `16`="Perinatal Period",
        `17`="Congential",
        `18`="Symptoms, Signs etc.",
        `19`="Injury etc.",
        `20`="External Causes",
        `21`="Contact with Healthcare",
        `22`="Special Purposes"
    ),
    parameters = c(
        `1`="Elapsed Duration",
        # brands
        `2`="asinh(Generic Brands)",
        `3`="asinh(Competitors USPDC)",
        # population
        `4`="asinh(High SDI)",
        `5`="asinh(High-Medium SDI)",
        `6`="asinh(Medium SDI)",
        `7`="asinh(Low-Medium SDI)",
        `8`="asinh(Low SDI)",
        #Status
        `9`="status_NYR",
        `10`="status_EBI",
        `11`="status_Rec",
        `12`="status_ANR"
    )
)

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,
        filter=NULL
        ) {
    
    #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
    p <- mcmc_areas(filtdata,prob = 0.8, prob_outer = 0.95) +
        ggtitle(paste("Parameter distributions for ICD-10 class:",group_name)) +
        geom_vline(xintercept=seq(-2,2,0.5),color="grey",alpha=0.750)  
    
    d <- pivot_longer(filtdata, everything()) |> 
        group_by(name) |> 
        summarize(
            mean=mean(value)
            ,q025 = quantile(value,probs = 0.025)
            ,q975 = quantile(value,probs = 0.975)
            ,q05 = quantile(value,probs = 0.05)
            ,q95 = quantile(value,probs = 0.95)
            )
    return(list(plot=p,quantiles=d,name=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
    p <- mcmc_areas(filtdata,prob = 0.8, prob_outer = 0.95) +
        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) |> 
        summarize(
            mean=mean(value)
            ,q025 = quantile(value,probs = 0.025)
            ,q975 = quantile(value,probs = 0.975)
            ,q05 = quantile(value,probs = 0.05)
            ,q95 = quantile(value,probs = 0.95)
            )
    return(list(plot=p,quantiles=d,name=parameter_name))
}


```

Plan: select all snapshots that are the first to have closed enrollment (Rec -> ANR)
```{r}
#delay intervention
intervention_enrollment <- x_cf_base[c(inherited_cols)]
intervention_enrollment["status_ANR"] <- 0
intervention_enrollment["status_Rec"] <- 1
```

There are `{r} length(intervention_enrollment$identical_brands)` trials in the intervention enrollment dataset

```{r}

counterfact_delay <- list(
    D = ncol(x),
    N = nrow(x),
    L = n_categories$count,
    y = y,
    ll = as.vector(categories),
    x = x,
    mu_mean = 0,
    mu_stdev = 0.05,
    sigma_location = -2.1,
    sigma_scale = 0.2,
    Nx = nrow(x_cf_base),
    llx = as.vector(cf_categories),
    counterfact_x_tilde = as.matrix(intervention_enrollment),
    counterfact_x = as.matrix(x_cf_base),
    status_indexes = c(11,12) #subtract anr from recruiting to get movement from anr to recruiting

)
```


Ok, so I cut out the estimation because there is some problem of some sort. And I'm not really working on that right now.

TODO: Next step: get this working, then figure out how many trials I analyzed, and if they match the DF5 trials list
```{r}
#| label: Fitting
#| eval: false
#| include: true
fit <- stan(
    file='Hierarchal_Logistic.stan', 
    data = counterfact_delay,
    chains = 8,
    iter = 12000,
    warmup = 4000,
    seed = 11021585
    )
```


## Fit Results
```{r}
print(check_hmc_diagnostics(fit))
print(get_bfmi(fit))
```


```{r}
print(fit)
```








# Parameter Distributions


```{r}
#| eval: false
#| include: true
#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 >= 0]) {
    print(i)
    
    #Print parameter distributions
    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, create.dir = TRUE
        )
    gx <- c(gx,gi)

    #Get Quantiles and means for parameters
    table <- xtable(gi$quantiles,
      floating=FALSE
      ,latex.environments = NULL
      ,booktabs = TRUE
      ,zap=getOption("digits")
      )
    write_lines(
        table,
        paste0(image_parameters_by_groups,"/group_table_",i,"_",gi$name,".tex")
    )
}
```



```{r}
#| eval: false
#| include: true
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(image_parameters_across_groups,"/parameters_",i,"_",pi$name,".png")
        ,plot=pi$plot, create.dir = TRUE
        )
    px <- c(px,pi)

    #Get Quantiles and means for parameters
    table <- xtable(pi$quantiles,
      floating=FALSE
      ,latex.environments = NULL
      ,booktabs = TRUE
      ,zap=getOption("digits")
      )
    write_lines(
        table,
        paste0(image_parameters_across_groups,"/parameters_tables_",i,"_",pi$name,".tex")
    )
    
}
```

Note these have 95% outer CI and 80% inner (shaded)







# Priors
```{r}
#| eval: false
#| include: true
df_ib_p <- data.frame(
    p_prior=as.vector(extract(generated_ib, pars="p_prior")$p_prior)
    ,p_predicted = as.vector(extract(generated_ib, pars="p_predicted")$p_predicted)
)

df_ib_prior <- data.frame(
    mu_prior = as.vector(extract(generated_ib, pars="mu_prior")$mu_prior)
    ,sigma_prior = as.vector(extract(generated_ib, pars="sigma_prior")$sigma_prior)
)
```


```{r}
#| eval: false
#| include: true
#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(paste0(image_dist_diff_analysis,"/prior_p.png"), create.dir = TRUE)

#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(paste0(image_dist_diff_analysis,"/posterior_p.png"), create.dir = TRUE)

#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(paste0(image_dist_diff_analysis,"/prior_mu.png"), create.dir = TRUE)

#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(paste0(image_dist_diff_analysis,"/prior_sigma.png"), create.dir = TRUE)
```






# Counterfactual: Delay close of enrollment
```{r}
generated_ib <- gqs(
    fit@stanmodel,
    data=counterfact_delay,
    draws=as.matrix(fit),
    seed=11021585
    )
```

```{r}
counterfact_predicted_ib <- data.frame(
    p_predicted_default = as.vector(extract(generated_ib, pars="p_predicted_default")$p_predicted_default)
    ,p_predicted_intervention = as.vector(extract(generated_ib, pars="p_predicted_intervention")$p_predicted_intervention)
    ,predicted_difference = as.vector(extract(generated_ib, pars="predicted_difference")$predicted_difference)
)
```


```{r}
#| eval: false
#| include: true
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(paste0(image_dist_diff_analysis,"/p_no_intervention.png"), create.dir = TRUE)

ggplot(counterfact_predicted_ib, aes(x=p_predicted_intervention)) +
    geom_density() + 
    labs(
        title="Predicted Distribution of 'p'"
        ,subtitle="Intervention: Delay close of enrollment"
        ,x="Probability Domain 'p'"
        ,y="Probability Density"
    )
##ggsave(paste0(image_dist_diff_analysis,"/p_delay_intervention.png"), create.dir = TRUE)

ggplot(counterfact_predicted_ib, aes(x=predicted_difference)) +
    geom_density() + 
    labs(
        title="Predicted Distribution of differences 'p'"
        ,subtitle="Intervention: Delay close of enrollment"
        ,x="Difference in 'p' under delay"
        ,y="Probability Density"
    )
##ggsave(paste0(image_dist_diff_analysis,"/p_delay_intervention_distdiff_1.png"), create.dir = TRUE)
```


```{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: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$category_id[i])
pddf_ib["category_name"] <- sapply(
    pddf_ib$category, 
    function(i) category_names[i]
    )
```


```{r}
# Compare group counts
counterfact_delay$ll %>% unique() %>% sort()
counterfact_delay$llx %>% unique() %>% sort()
df$category_id %>% unique() %>% sort()
```


```{r}
ggplot(pddf_ib, aes(x=value,)) +
    geom_density(adjust=1/5) +
    labs(
        title = "Distribution of predicted differences"
        ,subtitle = "Intervention: Delay close of enrollment"
        ,x = "Difference in probability due to intervention"
        ,y = "Probability Density"
    ) + 
    geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") 
#ggsave(paste0(image_dist_diff_analysis,"/p_delay_intervention_distdiff_styled.png"), create.dir = TRUE)
```


```{r}
ggplot(pddf_ib, aes(x=value,)) +
    geom_density(adjust=1/5) +
    facet_wrap(
        ~factor(
            category_name, 
            levels=category_names
            )
        , labeller = label_wrap_gen(multi_line = TRUE)
        , ncol=4) +
    labs(
        title = "Distribution of predicted differences | By Group"
        ,subtitle = "Intervention: Delay close of enrollment"
        ,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(paste0(image_dist_diff_analysis,"/p_delay_intervention_distdiff_by_group.png"), create.dir = TRUE)
```


```{r}
ggplot(pddf_ib, aes(x=value,)) +
    geom_histogram(bins=300) +
    facet_wrap(
        ~factor(
            category_name, 
            levels=category_names
            )
        , labeller = label_wrap_gen(multi_line = TRUE)
        , 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"
    ) + 
    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"), create.dir = TRUE)
```


```{r}
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") 

density_hight <- max(density(pddf_ib$value)$y)

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$value),
 stdev = sd(pddf_ib$value),
 q3 = quantile(pddf_ib$value, 0.75),
 p90 = quantile(pddf_ib$value, 0.90),
 p95 = quantile(pddf_ib$value, 0.95),
 max_height = max(ggplot_build(p3)$data[[1]]$count),
 y_offset = -max(ggplot_build(p3)$data[[1]]$count) * 0.05,
 y_offset_density = -density_hight * 0.15
)

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(paste0(image_dist_diff_analysis,"/p_delay_intervention_histdiff_boxplot.png"), create.dir = TRUE)
```


```{r}
p4 <- ggplot(pddf_ib, aes(x = value)) +
  geom_density() +
    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") +
 geom_segment(data = data.frame(
   x = c(stats$q1, stats$q3, stats$med),
   xend = c(stats$q1, stats$q3, stats$med),
   y = rep(stats$y_offset_density, 3), 
   yend = rep(stats$y_offset_density * 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$y_offset_density, stats$y_offset_density * 2),
   yend = c(stats$y_offset_density, stats$y_offset_density * 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$y_offset_density * 1.5, 2),
   yend = rep(stats$y_offset_density * 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$y_offset_density * 1.3,
   yend = stats$y_offset_density * 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$y_offset_density * 1.5, 2),
   yend = rep(stats$y_offset_density * 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$y_offset_density * 1.3,
   yend = stats$y_offset_density * 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_density * 1.5
 ), aes(x = x, y = y))
#ggsave(paste0(image_dist_diff_analysis,"/p_delay_intervention_distdiff_boxplot.png"), create.dir = TRUE)

p4
```

```{r}
#| eval: false
#| include: true
 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(paste0(image_dist_diff_analysis,"/p_delay_intervention_cumulative_distdiff.png"), create.dir = TRUE)
```


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/2`,`{r} width/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 * 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$mean (stats$stdev)`.

```{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
)
# Convert to LaTeX
table <- xtable(quant_df,
    digits = rep(3, ncol(quant_df) + 1),
    floating = FALSE,
    latex.environments = NULL,
    booktabs = TRUE
)

# Write to file
write_lines(
    print(table, include.rownames = FALSE),
    paste0(image_root,"/distdiff_5percentile_table.tex")
)

kable(quant_df)

proportion_increase <- mean(pddf_ib$value >= 0)
```

about `{r} proportion_increase * 100` percent probability increase in the probability of terminations

```{r}
n = length(counterfact_predicted_ib$p_predicted_intervention)
k = 100
simulated_terminations_intervention <- mean(rbinom(n,k,as.vector(counterfact_predicted_ib$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)
```

The simulation above shows that this results in a percentage-point change in terminations of about 
`{r} 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
)



print(distdif_by_group_latex)
distdiff_by_group
```



## fixed effects distributions

```{r}
#| eval: false
#| include: true
#Get dataframe with only the rows of interest
filtdata <- as.data.frame(extract(fit, pars="status_diff"))
#rename columns
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"), create.dir = TRUE)
    
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),
        `5%` = quantile(value, probs = 0.05),
        `25%` = quantile(value, probs = 0.25),
        `50% median` = quantile(value, probs = 0.5),
        `75%` = quantile(value, probs = 0.75),
        `95%` = quantile(value, probs = 0.95),
        `97.5%` = quantile(value, probs = 0.975)
    )

# 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(fixed_effects_table)[1] <- "ICD-10 Category"

# Convert to LaTeX
table <- xtable(fixed_effects_table,
    digits = rep(2, ncol(fixed_effects_table) + 1),
    floating = FALSE,
    align = "lccccccccccc",
    latex.environments = "tabularx",
    booktabs = TRUE
)



# Write to file
write_lines(
    print(table, include.rownames = FALSE),
    paste0(image_parameters_across_groups,"/fixed_effects_anr_vs_rec_by_group.tex")
)



```






  
