updated analysis
Will King
3 years ago
parent
a7e1f71e12
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---
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title: "The Effects of market conditions on recruitment and completion of clinical trials"
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author: "Will King"
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format: html
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editor: source
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---
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# Setup
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```{r}
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library(bayesplot)
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available_mcmc(pattern = "_nuts_")
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library(ggplot2)
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library(patchwork)
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library(tidyverse)
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library(rstan)
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library(tidyr)
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library(ghibli)
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#Resources: https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started
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#save unchanged models instead of recompiling
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rstan_options(auto_write = TRUE)
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#allow for multithreaded sampling
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options(mc.cores = parallel::detectCores())
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#test installation, shouldn't get any errors
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#example(stan_model, package = "rstan", run.dontrun = TRUE)
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```
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```{r}
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################ Pull data from database ######################
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library(RPostgreSQL)
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driver <- dbDriver("PostgreSQL")
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get_data <- function(driver) {
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con <- dbConnect(
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driver,
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user='root',
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password='root',
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dbname='aact_db',
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host='will-office'
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)
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on.exit(dbDisconnect(con))
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query <- dbSendQuery(
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con,
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# "select * from formatted_data_with_planned_enrollment;"
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"
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select
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fdqpe.nct_id
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--,fdqpe.start_date
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--,fdqpe.current_enrollment
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--,fdqpe.enrollment_category
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,fdqpe.current_status
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,fdqpe.earliest_date_observed
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,fdqpe.elapsed_duration
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,fdqpe.n_brands as identical_brands
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,ntbtu.brand_name_count
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,fdqpe.category_id
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,fdqpe.final_status
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,fdqpe.h_sdi_val
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--,fdqpe.h_sdi_u95
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--,fdqpe.h_sdi_l95
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,fdqpe.hm_sdi_val
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--,fdqpe.hm_sdi_u95
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--,fdqpe.hm_sdi_l95
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,fdqpe.m_sdi_val
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--,fdqpe.m_sdi_u95
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--,fdqpe.m_sdi_l95
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,fdqpe.lm_sdi_val
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--,fdqpe.lm_sdi_u95
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--,fdqpe.lm_sdi_l95
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,fdqpe.l_sdi_val
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--,fdqpe.l_sdi_u95
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--,fdqpe.l_sdi_l95
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from formatted_data_with_planned_enrollment fdqpe
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join \"Formularies\".nct_to_brands_through_uspdc ntbtu
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on fdqpe.nct_id = ntbtu.nct_id
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order by fdqpe.nct_id, fdqpe.earliest_date_observed
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;
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"
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)
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df <- fetch(query, n = -1)
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df <- na.omit(df)
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query2 <-dbSendQuery(con,"select count(*) from \"DiseaseBurden\".icd10_categories ic where \"level\"=1;")
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n_categories <- fetch(query2, n = -1)
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return(list(data=df,ncat=n_categories))
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}
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d <- get_data(driver)
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df <- d$data
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n_categories <- d$ncat
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################ Format Data ###########################
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data_formatter <- function(df) {
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categories <- df["category_id"]
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x <- df["elapsed_duration"]
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x["identical_brands"] <- asinh(df$identical_brands)
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x["brand_name_counts"] <- asinh(df$brand_name_count)
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x["h_sdi_val"] <- asinh(df$h_sdi_val)
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x["hm_sdi_val"] <- asinh(df$hm_sdi_val)
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x["m_sdi_val"] <- asinh(df$m_sdi_val)
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x["lm_sdi_val"] <- asinh(df$lm_sdi_val)
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x["l_sdi_val"] <- asinh(df$l_sdi_val)
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#interaction terms for competitors
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x["ib*elapsed"] <- x["elapsed_duration"]*x["identical_brands"]
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x["bnc*elapsed"] <- x["elapsed_duration"] * x["brand_name_counts"]
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y <- ifelse(df["final_status"]=="Terminated",1,0)
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return(list(x=x,y=y))
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}
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train <- data_formatter(df)
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categories <- df$category_id
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x <- train$x
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y <- train$y
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```
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### Intervention: Adding a single competitor
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```{r}
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inherited_cols <- c(
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"elapsed_duration"
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,"identical_brands"
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,"brand_name_counts"
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,"h_sdi_val"
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,"hm_sdi_val"
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,"m_sdi_val"
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,"lm_sdi_val"
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,"l_sdi_val"
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,"status_NYR"
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,"status_EBI"
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,"status_ANR"
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,"ib*elapsed"
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,"bnc*elapsed"
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)
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```
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#### Generics
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```{r}
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#generics intervention
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brand_intervention_ib <- x[inherited_cols]
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brand_intervention_ib["identical_brands"] <- asinh(sinh(x$identical_brands)+1) #add a single generic brand
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brand_intervention_ib["ib*elapsed"] <- brand_intervention_ib$identical_brands * brand_intervention_ib$elapsed_duration
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```
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```{r}
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counterfact_marketing_ib <- list(
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D = ncol(x),#
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N = nrow(x),
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L = n_categories$count,
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y = as.vector(y),
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ll = as.vector(categories),
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x = as.matrix(x),
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mu_mean = 0,
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mu_stdev = 0.05,
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sigma_shape = 4,
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sigma_rate = 20,
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Nx = nrow(x),
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llx = as.vector(categories),
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counterfact_x_tilde = as.matrix(brand_intervention_ib),
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counterfact_x = as.matrix(x)
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)
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```
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```{r}
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generated_bi <- gqs(
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fit@stanmodel,
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data=counterfact_marketing_ib,
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draws=as.matrix(fit),
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seed=11021585
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)
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```
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### USP DC
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```{r}
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#formulary intervention
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brand_intervention_bnc <- x[c(inherited_cols,"identical_brands","ib*elapsed")]
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brand_intervention_bnc["brand_name_counts"] <- asinh(sinh(x$brand_name_counts)+1) #add a single formulary competitor brand
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brand_intervention_bnc["bnc*elapsed"] <- brand_intervention_bnc$brand_name_counts * brand_intervention_bnc$elapsed_duration
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```
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```{r}
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counterfact_marketing_bnc <- list(
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D = ncol(x),#
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N = nrow(x),
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L = n_categories$count,
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y = as.vector(y),
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ll = as.vector(categories),
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x = as.matrix(x),
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mu_mean = 0,
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mu_stdev = 0.05,
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sigma_shape = 4,
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sigma_rate = 20,
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Nx = nrow(x),
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llx = as.vector(categories),
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counterfact_x_tilde = as.matrix(brand_intervention_bnc),
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counterfact_x = as.matrix(x)
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)
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```
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```{r}
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generated_bnc <- gqs(
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fit@stanmodel,
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data=counterfact_marketing_bnc,
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draws=as.matrix(fit),
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seed=11021585
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)
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```
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# Fit Model
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```{r}
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################################# FIT MODEL #########################################
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fit <- stan(
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file='Hierarchal_Logistic.stan',
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data = counterfact_marketing_ib,
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chains = 4,
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iter = 5000,
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seed = 11021585
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)
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```
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```{r}
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hist(as.vector(extract(generated, pars="p_prior")$p_prior))
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hist(as.vector(extract(generated, pars="mu_prior")$mu_prior), )
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hist(as.vector(extract(generated, pars="sigma_prior")$sigma_prior))
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```
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```{r}
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check_hmc_diagnostics(fit)
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hist(as.vector(extract(generated, pars="p_predicted")$p_predicted))
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```
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# Diagnostics
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```{r}
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#trace plots
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plot(fit, pars=c("mu"), plotfun="trace")
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for (i in 1:4) {
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print(
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mcmc_rank_overlay(
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fit,
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pars=c(
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paste0("mu[",4*i-3,"]"),
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paste0("mu[",4*i-2,"]"),
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paste0("mu[",4*i-1,"]"),
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paste0("mu[",4*i,"]")
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),
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n_bins=100
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)+ legend_move("top") +
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scale_colour_ghibli_d("KikiMedium")
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)
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}
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```
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```{r}
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plot(fit, pars=c("sigma"), plotfun="trace")
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for (i in 1:4) {
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print(
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mcmc_rank_overlay(
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fit,
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pars=c(
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paste0("sigma[",4*i-3,"]"),
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paste0("sigma[",4*i-2,"]"),
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paste0("sigma[",4*i-1,"]"),
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paste0("sigma[",4*i,"]")
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),
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n_bins=100
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)+ legend_move("top") +
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scale_colour_ghibli_d("KikiMedium")
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)
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}
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```
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```{r}
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#other diagnostics
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logpost <- log_posterior(fit)
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nuts_prmts <- nuts_params(fit)
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posterior <- as.array(fit)
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```
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```{r}
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color_scheme_set("darkgray")
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div_style <- parcoord_style_np(div_color = "green", div_size = 0.05, div_alpha = 0.4)
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mcmc_parcoord(posterior, regex_pars = "mu", np=nuts_prmts, np_style = div_style, alpha = 0.05)
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```
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```{r}
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for (i in 1:4) {
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mus = sapply(3:0, function(j) paste0("mu[",4*i-j ,"]"))
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print(
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mcmc_pairs(
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posterior,
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np = nuts_prmts,
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pars=c(
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mus,
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"lp__"
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),
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off_diag_args = list(size = 0.75)
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)
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)
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}
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```
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```{r}
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mcmc_parcoord(posterior,regex_pars = "sigma", np=nuts_prmts, alpha=0.05)
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```
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```{r}
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for (i in 1:4) {
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params = sapply(3:0, function(j) paste0("sigma[",4*i-j ,"]"))
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print(
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mcmc_pairs(
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posterior,
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np = nuts_prmts,
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pars=c(
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params,
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"lp__"
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),
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off_diag_args = list(size = 0.75)
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)
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)
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}
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```
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```{r}
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for (k in 1:22) {
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for (i in 1:4) {
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params = sapply(3:0, function(j) paste0("beta[",k,",",4*i-j ,"]"))
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print(
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mcmc_pairs(
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posterior,
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np = nuts_prmts,
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pars=c(
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params,
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"lp__"
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),
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off_diag_args = list(size = 0.75)
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)
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)
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}}
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```
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# Results
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```{r}
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################################# ANALYZE #####################################
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print(fit)
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```
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## Result Plots
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Note the regular large difference in variance.
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I would guess those are the beta[1:22,2] values.
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I wonder if a lot of the variance is due to the 2 values that are sitting out.
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```{r}
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beta_list <- list(
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groups = c(
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`1`="Infections & Parasites",
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`2`="Neoplasms",
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`3`="Blood & Immune system",
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`4`="Endocrine, Nutritional, and Metabolic",
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`5`="Mental & Behavioral",
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`6`="Nervous System",
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`7`="Eye and Adnexa",
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`8`="Ear and Mastoid",
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`9`="Circulatory",
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`10`="Respiratory",
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`11`="Digestive",
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`12`="Skin & Subcutaneaous tissue",
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`13`="Musculoskeletal",
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`14`="Genitourinary",
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`15`="Pregancy, Childbirth, & Puerperium",
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`16`="Perinatal Period",
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`17`="Congential",
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`18`="Symptoms, Signs etc.",
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`19`="Injury etc.",
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`20`="External Causes",
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`21`="Contact with Healthcare",
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`22`="Special Purposes"
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),
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parameters = c(
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`1`="Elapsed Duration",
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# brands
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`2`="asinh(Generic Brands)",
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`3`="asinh(Competitors USPDC)",
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# population
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`4`="asinh(High SDI)",
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`5`="asinh(High-Medium SDI)",
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`6`="asinh(Medium SDI)",
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`7`="asinh(Low-Medium SDI)",
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`8`="asinh(Low SDI)",
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#Status
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`9`="status_NYR",
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`10`="status_EBI",
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`11`="status_ANR",
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#interactions for brands
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`12`="ib*elapsed",
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`13`="bnc*elapsed",
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# interactions for status
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`14`="sNYR*elapsed",
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`15`="sEBI*elapsed",
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`16`="sANR*elapsed"
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)
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)
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get_parameters <- function(stem,class_list) {
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#get categories and lengths
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named <- names(class_list)
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lengths <- sapply(named, (function (x) length(class_list[[x]])))
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#describe the grid needed
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iter_list <- sapply(named, (function (x) 1:lengths[x]))
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#generate the list of parameters
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pardf <- generate_parameter_df(stem, iter_list)
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#add columns with appropriate human-readable names
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for (name in named) {
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pardf[paste(name,"_hr",sep="")] <- as.factor(
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sapply(pardf[name], (function (i) class_list[[name]][i]))
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)
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}
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return(pardf)
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}
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generate_parameter_df <- function(stem, iter_list) {
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grid <- expand.grid(iter_list)
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grid["param_name"] <- grid %>% unite(x,colnames(grid),sep=",")
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grid["param_name"] <- paste(stem,"[",grid$param_name,"]",sep="")
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return(grid)
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}
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group_mcmc_areas <- function(
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stem,# = "beta"
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class_list,# = beta_list
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stanfit,# = fit
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group_id,# = 2
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rename=TRUE
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) {
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#get all parameter names
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params <- get_parameters(stem,class_list)
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#filter down to parameters of interest
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params <- filter(params,groups == group_id)
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#Get dataframe with only the rows of interest
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filtdata <- as.data.frame(stanfit)[params$param_name]
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#rename columns
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if (rename) dimnames(filtdata)[[2]] <- params$parameters_hr
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#get group name for title
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group_name <- class_list$groups[group_id]
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#create area plot with appropriate title
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mcmc_areas(filtdata,prob = 0.8, prob_outer = 0.95) +
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ggtitle(paste("Parameter distributions for ICD-10 class:",group_name))
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}
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parameter_mcmc_areas <- function(
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stem,# = "beta"
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class_list,# = beta_list
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stanfit,# = fit
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parameter_id,# = 2
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rename=TRUE
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) {
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#get all parameter names
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params <- get_parameters(stem,class_list)
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#filter down to parameters of interest
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||||
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: Adding a single competitor
|
||||
|
||||
|
||||
#### Generics
|
||||
|
||||
|
||||
```{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}
|
||||
#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) as_date(df$earliest_date_observed[i]))
|
||||
```
|
||||
|
||||
|
||||
```{r}
|
||||
|
||||
ggplot(pddf_bnc, aes(x=value,)) +
|
||||
geom_histogram(bins=100) +
|
||||
labs(
|
||||
title = "Distribution of predicted differences"
|
||||
,x = "Difference in probability due to intervention"
|
||||
,y = "Predicted counts"
|
||||
) +
|
||||
#xlim(-0.3,0.1) +
|
||||
geom_vline(aes(xintercept = 0), color = "skyblue", linetype="dashed")
|
||||
|
||||
ggplot(pddf_bnc, aes(x=value,)) +
|
||||
geom_histogram(bins=100) +
|
||||
facet_wrap(
|
||||
~factor(
|
||||
category_name,
|
||||
levels=beta_list$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))
|
||||
|
||||
```
|
||||
|
||||
Loading…
Reference in New Issue