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ClinicalTrialsEstimation/r-analysis/EffectsOfEnrollmentDelay.qmd

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---
title: "The Effects of Recruitment status on 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)
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)
```
```{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))
}
get_counterfact_base <- function(driver) {
con <- dbConnect(
driver,
user='root',
password='root',
dbname='aact_db',
host='will-office'
)
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_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
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$data
################ Format Data ###########################
data_formatter <- function(df) {
categories <- df["category_id"]
x <- df["elapsed_duration"]
x["identical_brands"] <- asinh(df$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$category_id
x <- train$x
y <- train$y
x_cf_base <- counterfact_base$x
y_cf_base <- counterfact_base$y
cf_categories <- df_counterfact_base$category_id
```
# 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=0,color="grey",alpha=0.75)
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$parameters[parameter_id]
#create area plot with appropriate title
p <- mcmc_areas(filtdata,prob = 0.8, prob_outer = 0.95) +
ggtitle(parameter_name,"Parameter Distribution")
d <- pivot_longer(filtdata, everything()) |>
group_by(name) |>
summarize(
mean=mean(value)
,q025 = quantile(value,probs = 0.025)
,q975 = quantile(value,probs = 0.975)
,q05 = quantile(value,probs = 0.05)
,q95 = quantile(value,probs = 0.95)
)
return(list(plot=p,quantiles=d,name=parameter_name))
}
```
Plan: select all snapshots that are the first to have closed enrollment (Rec -> ANR)
```{r}
#delay intervention
intervention_enrollment <- x_cf_base[c(inherited_cols,"brand_name_counts", "identical_brands")]
intervention_enrollment["status_ANR"] <- 0
intervention_enrollment["status_Rec"] <- 1
```
```{r}
counterfact_delay <- list(
D = ncol(x),#
N = nrow(x),
L = n_categories$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_cf_base),
llx = as.vector(cf_categories),
counterfact_x_tilde = as.matrix(intervention_enrollment),
counterfact_x = as.matrix(x_cf_base)
)
```
```{r}
fit <- stan(
file='Hierarchal_Logistic.stan',
data = counterfact_delay,
chains = 4,
iter = 5000,
seed = 11021585
)
```
## Explore data
```{r}
#get number of trials and snapshots in each category
group_trials_by_category <- as.data.frame(aggregate(category_id ~ nct_id, df, max))
group_trials_by_category <- as.data.frame(group_trials_by_category)
category_count <- group_trials_by_category |> group_by(category_id) |> count()
```
## Fit Results
```{r}
################################# ANALYZE #####################################
print(fit)
```
# Counterfactuals
```{r}
generated_ib <- gqs(
fit@stanmodel,
data=counterfact_delay,
draws=as.matrix(fit),
seed=11021585
)
```
```{r}
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)
)
#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("./EffectsOfEnrollmentDelay/Images/DirectEffects/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("./EffectsOfEnrollmentDelay/Images/DirectEffects/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("./EffectsOfEnrollmentDelay/Images/DirectEffects/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("./EffectsOfEnrollmentDelay/Images/DirectEffects/prior_sigma.png")
```
```{r}
check_hmc_diagnostics(fit)
```
### Intervention: Delay close of enrollment
```{r}
counterfact_predicted_ib <- data.frame(
p_predicted_default = as.vector(extract(generated_ib, pars="p_predicted_default")$p_predicted_default)
,p_predicted_intervention = as.vector(extract(generated_ib, pars="p_predicted_intervention")$p_predicted_intervention)
,predicted_difference = as.vector(extract(generated_ib, pars="predicted_difference")$predicted_difference)
)
ggplot(counterfact_predicted_ib, aes(x=p_predicted_default)) +
geom_density() +
labs(
title="Predicted Distribution of 'p'"
,subtitle="Intervention: None"
,x="Probability Domain 'p'"
,y="Probability Density"
)
ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/default_p_generic_intervention_base.png")
ggplot(counterfact_predicted_ib, aes(x=p_predicted_intervention)) +
geom_density() +
labs(
title="Predicted Distribution of 'p'"
,subtitle="Intervention: Delay close of enrollment"
,x="Probability Domain 'p'"
,y="Probability Density"
)
ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/default_p_generic_intervention_interv.png")
ggplot(counterfact_predicted_ib, aes(x=predicted_difference)) +
geom_density() +
labs(
title="Predicted Distribution of differences 'p'"
,subtitle="Intervention: Delay close of enrollment"
,x="Difference in 'p' under treatment"
,y="Probability Density"
)
ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/default_p_generic_intervention_distdiff.png")
```
```{r}
pddf_ib <- data.frame(extract(generated_ib, pars="predicted_difference")$predicted_difference) |>
pivot_longer(X1:X169)
#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$groups[i])
ggplot(pddf_ib, aes(x=value,)) +
geom_density(bins=100) +
labs(
title = "Distribution of predicted differences"
,subtitle = "Intervention: Delay close of enrollment"
,x = "Difference in probability due to intervention"
,y = "Probability Density"
) +
geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed")
ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/p_generic_intervention_distdiff_styled.png")
ggplot(pddf_ib, aes(x=value,)) +
geom_density(bins=100) +
facet_wrap(
~factor(
category_name,
levels=beta_list$groups
)
, 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("./EffectsOfEnrollmentDelay/Images/DirectEffects/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$groups
)
, labeller = label_wrap_gen(multi_line = TRUE)
, ncol=5) +
labs(
title = "Histogram of predicted differences | By Group"
,subtitle = "Intervention: Delay close of enrollment"
,x = "Difference in probability due to intervention"
,y = "Predicted counts"
) +
#xlim(-0.25,0.1) +
geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed") +
theme(strip.text.x = element_text(size = 8))
ggsave("./EffectsOfEnrollmentDelay/Images/DirectEffects/p_generic_intervention_histdiff_by_group.png")
```
Get the probability of increase over probability of a decrease
```{r}
mean(counterfact_predicted_ib$predicted_difference)
```
Thus adding a Delay close of enrollment increases the probability of termination by 16.72% on average for
the snapshots investigated.
```{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)))
```
# Diagnostics
```{r}
#| eval: false
#trace plots
plot(fit, pars=c("mu"), plotfun="trace")
for (i in 1:4) {
print(
mcmc_rank_overlay(
fit,
pars=c(
paste0("mu[",4*i-3,"]"),
paste0("mu[",4*i-2,"]"),
paste0("mu[",4*i-1,"]"),
paste0("mu[",4*i,"]")
),
n_bins=100
)+ legend_move("top") +
scale_colour_ghibli_d("KikiMedium")
)
}
```
```{r}
#| eval: false
plot(fit, pars=c("sigma"), plotfun="trace")
for (i in 1:4) {
print(
mcmc_rank_overlay(
fit,
pars=c(
paste0("sigma[",4*i-3,"]"),
paste0("sigma[",4*i-2,"]"),
paste0("sigma[",4*i-1,"]"),
paste0("sigma[",4*i,"]")
),
n_bins=100
)+ legend_move("top") +
scale_colour_ghibli_d("KikiMedium")
)
}
```
```{r}
#| 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
for (i in 1:4) {
mus = sapply(3:0, function(j) paste0("mu[",4*i-j ,"]"))
print(
mcmc_pairs(
posterior,
np = nuts_prmts,
pars=c(
mus,
"lp__"
),
off_diag_args = list(size = 0.75)
)
)
}
```
```{r}
#| eval: false
mcmc_parcoord(posterior,regex_pars = "sigma", np=nuts_prmts, alpha=0.05)
```
```{r}
#| eval: false
for (i in 1:4) {
params = sapply(3:0, function(j) paste0("sigma[",4*i-j ,"]"))
print(
mcmc_pairs(
posterior,
np = nuts_prmts,
pars=c(
params,
"lp__"
),
off_diag_args = list(size = 0.75)
)
)
}
```
```{r}
#| eval: false
for (k in 1:22) {
for (i in 1:4) {
params = sapply(3:0, function(j) paste0("beta[",k,",",4*i-j ,"]"))
print(
mcmc_pairs(
posterior,
np = nuts_prmts,
pars=c(
params,
"lp__"
),
off_diag_args = list(size = 0.75)
)
)
}}
```
# TODO
- [ ] Double check data flow. (Write summary of this in human readable form)
- Is it the data we want from the database
- Training
- Counterfactual Evaluation
- choose a single snapshot per trial.
- Is the model in STAN well specified.
- [ ] work on LOO validation of model
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