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ClinicalTrialsEstimation/r-analysis/EffectsOfMarketConditions.qmd

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---
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) {
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$l_sdi_val)
#Setup fixed effects
x["status_NYR"] <- ifelse(df["current_status"]=="Not yet recruiting",1,0)
x["status_EBI"] <- ifelse(df["current_status"]=="Enrolling by invitation",1,0)
#x["status_Rec"] <- ifelse(df["current_status"]=="Recruiting",1,0) # Base case is Recruiting
x["status_ANR"] <- ifelse(df["current_status"]=="Active, not recruiting",1,0)
#interaction terms for competitors
x["ib*elapsed"] <- x["elapsed_duration"]*x["identical_brands"]
x["bnc*elapsed"] <- x["elapsed_duration"] * x["brand_name_counts"]
#interaction terms for status effects
x["sNYR*elapsed"] <- x["elapsed_duration"]*x["status_NYR"]
x["sEBI*elapsed"] <- x["elapsed_duration"]*x["status_EBI"]
x["sANR*elapsed"] <- x["elapsed_duration"]*x["status_ANR"]
y <- ifelse(df["final_status"]=="Terminated",1,0)
#get category list
return(list(x=x,y=y))
}
train <- data_formatter(df)
categories <- df$category_id
x <- train$x
y <- train$y
```
```{r}
# get data for counterfactuals
get_counterfactuals <- 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 (
select
nct_id
,lag(current_status, 1) over (partition by nct_id order by earliest_date_observed) as previous_status
,current_status
,earliest_date_observed as date_current
from formatted_data_mat fdm
), cte2 as (
select
nct_id
,previous_status
,current_status
,max(date_current) as date_current_max
from cte
where
previous_status != current_status
and
current_status = 'Active, not recruiting'
group by
nct_id
,previous_status
,current_status
,date_current
)
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 cte2
on cte2.nct_id = fdqpe.nct_id
and cte2.date_current_max = fdqpe.earliest_date_observed
order by fdqpe.nct_id, fdqpe.earliest_date_observed
;
"
)
df <- fetch(query, n = -1)
df <- na.omit(df)
return(df)
}
counterfact_raw <- get_counterfactuals(driver)
#extract data
counterfact_list <- data_formatter(counterfact_raw)
counterfact_list$y <- NULL #remove the chance of accidentally training on the wrong data
counterfact_categories <- counterfact_raw$category_id
#setup the two counterfactuals
counterfact_x <- counterfact_list$x #no change
counterfact_x_tilde <- counterfact_list$x #to be changed
#make changes, set it to Recruiting #TODO: change this so it matches previous state.
counterfact_x_tilde$status_ANR <- 0
counterfact_x_tilde["sANR*elapsed"] <- 0
```
## Explore data
```{r}
################################# DATA EXPLORATION ############################
driver <- dbDriver("PostgreSQL")
con <- dbConnect(
driver,
user='root',
password='root',
dbname='aact_db',
host='will-office'
)
#Plot histogram of count of snapshots
df3 <- dbGetQuery(
con,
"select nct_id,final_status,count(*) from formatted_data_with_planned_enrollment fdwpe
group by nct_id,final_status ;"
)
#df3 <- fetch(query3, n = -1)
ggplot(data=df3, aes(x=count, fill=final_status)) +
geom_histogram(binwidth=1) +
ggtitle("Histogram of snapshots per trial (matched trials)") +
xlab("Snapshots per trial")
#Plot duration for terminated vs completed
df4 <- dbGetQuery(
con,
"
select
nct_id,
start_date ,
primary_completion_date,
overall_status ,
primary_completion_date - start_date as duration
from ctgov.studies s
where nct_id in (select distinct nct_id from http.download_status ds)
;"
)
#df4 <- fetch(query4, n = -1)
ggplot(data=df4, aes(x=duration,fill=overall_status)) +
geom_histogram()+
ggtitle("Histogram of trial durations") +
xlab("duration")+
facet_wrap(~overall_status)
df5 <- dbGetQuery(
con,
"
with cte1 as (
select
nct_id,
start_date ,
primary_completion_date,
overall_status ,
primary_completion_date - start_date as duration
from ctgov.studies s
where nct_id in (select distinct nct_id from http.download_status ds)
), cte2 as (
select nct_id,count(*) as snapshot_count from formatted_data_with_planned_enrollment fdwpe
group by nct_id
)
select a.nct_id, a.overall_status, a.duration,b.snapshot_count
from cte1 as a
join cte2 as b
on a.nct_id=b.nct_id
;"
)
df5$overall_status <- as.factor(df5$overall_status)
#df5 <- fetch(query5, n = -1)
ggplot(data=df5, aes(x=duration,y=snapshot_count,color=overall_status)) +
geom_jitter() +
ggtitle("duration, status, and snapshot_count") +
xlab("duration") +
ylab("snapshot count")
dbDisconnect(con)
#get number of trials and snapshots in each category
group_trials_by_category <- as.data.frame(aggregate(category_id ~ nct_id, df, max))
group_trials_by_category <- as.data.frame(group_trials_by_category)
ggplot(data = group_trials_by_category, aes(x=category_id)) +
geom_histogram(binwidth=1,color="black",fill="seagreen") +
scale_x_continuous(breaks=scales::pretty_breaks(n=22))
summary(df5)
```
# Fit Model
```{r}
################################# FIT MODEL #########################################
#setup data (named list)
trials_data <- list(
D = ncol(x),#
N = nrow(x),
L = n_categories$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 = 189,
llx = as.vector(counterfact_categories),
counterfact_x_tilde = as.matrix(counterfact_x_tilde),
counterfact_x = as.matrix(counterfact_x)
)
fit <- stan(
file='Hierarchal_Logistic.stan',
data = trials_data,
chains = 4,
iter = 5000,
seed = 11021585
)
```
```{r}
#pull out prior predictions
generated <- gqs(
fit@stanmodel,
data=trials_data,
draws=as.matrix(fit),
seed=11021585
)
# to implement distribution of differences:
# create two datasets with interventions
# simulate both with the same seed and draws
# figure out how to difference the posterior (and maybe look at prior) probabilities
```
```{r}
hist(as.vector(extract(generated, pars="p_prior")$p_prior))
hist(as.vector(extract(generated, pars="mu_prior")$mu_prior), )
hist(as.vector(extract(generated, pars="sigma_prior")$sigma_prior))
```
```{r}
check_hmc_diagnostics(fit)
hist(as.vector(extract(generated, pars="p_predicted")$p_predicted))
```
# Diagnostics
```{r}
#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}
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}
#other diagnostics
logpost <- log_posterior(fit)
nuts_prmts <- nuts_params(fit)
posterior <- as.array(fit)
```
```{r}
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}
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}
mcmc_parcoord(posterior,regex_pars = "sigma", np=nuts_prmts, alpha=0.05)
```
```{r}
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}
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)
)
)
}}
```
# 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(
`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_ANR",
#interactions for brands
`12`="ib*elapsed",
`13`="bnc*elapsed",
# interactions for status
`14`="sNYR*elapsed",
`15`="sEBI*elapsed",
`16`="sANR*elapsed"
)
)
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)
p2 <- parameter_mcmc_areas("beta",beta_list,fit,2)
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)
#p8 <- parameter_mcmc_areas("beta",beta_list,fit,8)
p9 <- parameter_mcmc_areas("beta",beta_list,fit,9)
p10 <- parameter_mcmc_areas("beta",beta_list,fit,10)
p11 <- parameter_mcmc_areas("beta",beta_list,fit,11)
p12 <- parameter_mcmc_areas("beta",beta_list,fit,12)
p13 <- parameter_mcmc_areas("beta",beta_list,fit,13)
p14 <- parameter_mcmc_areas("beta",beta_list,fit,14)
p15 <- parameter_mcmc_areas("beta",beta_list,fit,15)
p16 <- parameter_mcmc_areas("beta",beta_list,fit,16)
```
Note these have 95% outer CI and 80% inner (shaded)
1) "Elapsed Duration",
2) "asinh(Generic Brands)",
3) "asinh(Competitors USPDC)",
4) "asinh(High SDI)",
5) "asinh(High-Medium SDI)",
6) "asinh(Medium SDI)",
7) "asinh(Low-Medium SDI)",
8) "asinh(Low SDI)",
9) "status_NYR",
10) "status_EBI",
11) "status_ANR",
12) "ib*elapsed",
13) "bnc*elapsed",
14) "sNYR*elapsed",
15) "sEBI*elapsed",
16) "sANR*elapsed"
of interest
- p1 + p2
- p3 + p2
- p2 + p12
- p3 + p13
- p9 + p14
- p10 + p15
- p11 + p16
```{r}
p1 + p2
```
```{r}
p2 + p3
```
```{r}
p2 + p12
```
```{r}
p3 + p13
```
```{r}
p9 + p14
```
```{r}
p10 + p15
```
```{r}
p11 + p16
```
# Posterior Prediction
```{r}
#TODO: Convert to ggplot, stabilize y axis
hist(as.vector(extract(generated, pars="p_predicted_default")$p_predicted_default))
hist(as.vector(extract(generated, pars="p_predicted_intervention")$p_predicted_intervention))
```
## Distribution of Predicted Differences
### Intervention: Marginal increase in time to finish enrollment
```{r}
pddf <- data.frame(extract(generated, pars="predicted_difference")$predicted_difference) |> pivot_longer(X1:X189)
pddf["entry_idx"] <- as.numeric(gsub("\\D","",pddf$name))
pddf["category"] <- sapply(pddf$entry_idx, function(i) counterfact_categories[i])
pddf["category_name"] <- sapply(pddf$category, function(i) beta_list$groups[i])
ggplot(pddf, aes(x=value,)) +
geom_histogram(bins=100) +
labs(
title = "Distribution of predicted differences"
,x = "Difference in probability due to intervention"
,y = "Predicted counts"
) +
xlim(-0.3,0.1) +
geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed")
ggplot(pddf, aes(x=value,)) +
geom_histogram(bins=100) +
facet_wrap(
~factor(
category_name,
levels=beta_list$groups
)
, labeller = label_wrap_gen(multi_line = TRUE)
, ncol=5) +
labs(
title = "Distribution of predicted differences",
subtitle = "By group"
,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))
```
Recall that we had really tight zero priors.
### Intervention: Adding a single competitor
```{r}
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_ANR"
#,"ib*elapsed"
#,"bnc*elapsed"
,"sNYR*elapsed"
,"sEBI*elapsed"
,"sANR*elapsed"
)
```
#### Generics
```{r}
#generics intervention
brand_intervention_ib <- x[c(inherited_cols,"brand_name_counts","bnc*elapsed")]
brand_intervention_ib["identical_brands"] <- asinh(sinh(x$identical_brands)+1) #add a single generic brand
brand_intervention_ib["ib*elapsed"] <- brand_intervention_ib$identical_brands * brand_intervention_ib$elapsed_duration
```
```{r}
counterfact_marketing_ib <- list(
D = ncol(x),#
N = nrow(x),
L = n_categories$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)
)
```
```{r}
generated_ib <- gqs(
fit@stanmodel,
data=counterfact_marketing_ib,
draws=as.matrix(fit),
seed=11021585
)
```
```{r}
#TODO: Convert to ggplot, stabilize y axis
hist(as.vector(extract(generated_ib, pars="p_predicted_default")$p_predicted_default), bins=100)
hist(as.vector(extract(generated_ib, pars="p_predicted_intervention")$p_predicted_intervention), bins=100)
hist(as.vector(extract(generated_ib, pars="predicted_difference")$predicted_difference), bins=100)
```
```{r}
pddf_ib <- data.frame(extract(generated_ib, pars="predicted_difference")$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$groups[i])
```
```{r}
ggplot(pddf_ib, aes(x=value,)) +
geom_histogram(bins=100) +
labs(
title = "Distribution of predicted differences"
,x = "Difference in probability due to intervention"
,y = "Predicted counts"
) +
#xlim(-0.3,0.1) +
geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed")
ggplot(pddf_ib, aes(x=value,)) +
geom_histogram(bins=100) +
facet_wrap(
~factor(
category_name,
levels=beta_list$groups
)
, labeller = label_wrap_gen(multi_line = TRUE)
, ncol=5) +
labs(
title = "Distribution of predicted differences",
subtitle = "By group"
,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))
```
#### USP DC
```{r}
#formulary intervention
brand_intervention_bnc <- x[c(inherited_cols,"identical_brands","ib*elapsed")]
brand_intervention_bnc["brand_name_counts"] <- asinh(sinh(x$brand_name_counts)+1) #add a single formulary competitor brand
brand_intervention_bnc["bnc*elapsed"] <- brand_intervention_bnc$brand_name_counts * brand_intervention_bnc$elapsed_duration
```
```{r}
counterfact_marketing_bnc <- list(
D = ncol(x),#
N = nrow(x),
L = n_categories$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)
)
```
```{r}
generated_bnc <- gqs(
fit@stanmodel,
data=counterfact_marketing_bnc,
draws=as.matrix(fit),
seed=11021585
)
```
```{r}
#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")$predicted_difference), bins=100)
```
```{r}
pddf_bnc <- data.frame(extract(generated_bnc, pars="predicted_difference")$predicted_difference) |>
pivot_longer(X1:X1343)
#Add 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$groups[i])
#add snapshot date
pddf_bnc["snapshot_date"] <- sapply(pddf_bnc$entry_idx, function(i) df$earliest_date_observed[i])#changed values
```
```{r}
ggplot(pddf_bnc, aes(x=value,)) +
geom_histogram(bins=100) +
labs(
title = "Distribution of predicted differences"
,x = "Difference in probability due to intervention"
,y = "Predicted counts"
) +
#xlim(-0.3,0.1) +
geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed")
ggplot(pddf_bnc, aes(x=value,)) +
geom_histogram(bins=100) +
facet_wrap(
~factor(
category_name,
levels=beta_list$groups
)
, labeller = label_wrap_gen(multi_line = TRUE)
, ncol=5) +
labs(
title = "Distribution of predicted differences",
subtitle = "By group"
,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))
```
TODO: add density plot of (x,y,z) (date,value,counts)
- with and without faceting
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