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Copy path6.1.slade_comparing_naive_models.R
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6.1.slade_comparing_naive_models.R
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####################
## Description:
## - In this file we make a collected of plots comparing assessment
## measurements of several models from 5.1.
####################
# Used in slade to ensure the library being used is my personal library
.libPaths(.libPaths()[c(2,1,3)])
## increase memery usage to 50gb of RAM
options(java.parameters = "-Xmx50g")
library(tidyverse)
library(bartMachine)
## path to output folder
output_path <- "Samples"
## make directory for outputs
dir.create(output_path)
output_path <- "Samples/SGLT2-GLP1"
## make directory for outputs
dir.create(output_path)
## make directory for outputs
dir.create(paste0(output_path, "/Assessment"))
## make directory for outputs
dir.create("Plots")
###############################################################################
###############################################################################
######################### Read Data / Model In ################################
###############################################################################
###############################################################################
# name: final.dev
load(paste0(output_path, "/datasets/cprd_19_sglt2glp1_devcohort.Rda"))
# name: final.val
load(paste0(output_path, "/datasets/cprd_19_sglt2glp1_valcohort.Rda"))
###############################################################################
###############################################################################
################################ FUNCTIONS ####################################
###############################################################################
###############################################################################
source("0.1.slade_functions.R")
#############################
### Complete model of only routine data, no propensity score (n: 8564(4542))
#############################
bart_comp_routine_no_prop <- readRDS(paste0(output_path, "/Model_fit/bart_comp_routine_no_prop.rds"))
# Dev
data_complete_routine_dev <- final.dev %>%
select(
patid,
pateddrug,
posthba1c_final,
colnames(bart_comp_routine_no_prop$X)
) %>%
drop_na()
## Get posteriors
if (class(try(
posteriors_comp_routine_no_prop_dev <- readRDS(paste0(output_path, "/Assessment/posteriors_comp_routine_no_prop_dev.rds"))
, silent = TRUE)) == "try-error") {
posteriors_comp_routine_no_prop_dev <- bartMachine::bart_machine_get_posterior(bart_comp_routine_no_prop, data_complete_routine_dev %>%
select(
colnames(bart_comp_routine_no_prop$X)
))
saveRDS(posteriors_comp_routine_no_prop_dev, paste0(output_path, "/Assessment/posteriors_comp_routine_no_prop_dev.rds"))
}
### residuals calculation
if (class(try(
comp_routine_no_prop_cred_pred_dev <- readRDS(paste0(output_path, "/Assessment/comp_routine_no_prop_cred_pred_dev.rds"))
, silent = TRUE)) == "try-error") {
comp_routine_no_prop_cred_pred_dev <- calc_resid(data_complete_routine_dev, posteriors_comp_routine_no_prop_dev)
saveRDS(comp_routine_no_prop_cred_pred_dev, paste0(output_path, "/Assessment/comp_routine_no_prop_cred_pred_dev.rds"))
}
# calculate effects
if (class(try(
comp_routine_no_prop_effects_summary_dev <- readRDS(paste0(output_path, "/Final_model/Assessment/comp_routine_no_prop_effects_summary_dev.rds"))
, silent = TRUE)) == "try-error") {
comp_routine_no_prop_effects_summary_dev <- calc_effect_summary(bart_comp_routine_no_prop, data_complete_routine_dev)
saveRDS(comp_routine_no_prop_effects_summary_dev, paste0(output_path, "/Final_model/Assessment/comp_routine_no_prop_effects_summary_dev.rds"))
}
## plot effects validation
predicted_observed_dev <- data_complete_routine_dev %>%
cbind(hba1c_diff = comp_routine_no_prop_effects_summary_dev$mean) %>%
mutate(bestdrug = ifelse(hba1c_diff < 0, "SGLT2", "GLP1"),
hba1c_diff.q = ntile(hba1c_diff, 10))
plot_comp_routine_no_prop_effects_validation_dev <- plot_full_effects_validation(predicted_observed_dev, dataset = "Dev")
## plot histogram of effect
plot_effect_1 <- hist_plot(comp_routine_no_prop_effects_summary_dev, "", -15, 20)
# Val
data_complete_routine_val <- final.val %>%
select(
patid,
pateddrug,
posthba1c_final,
colnames(bart_comp_routine_no_prop$X)
) %>%
drop_na()
## Get posteriors
if (class(try(
posteriors_comp_routine_no_prop_val <- readRDS(paste0(output_path, "/Assessment/posteriors_comp_routine_no_prop_val.rds"))
, silent = TRUE)) == "try-error") {
posteriors_comp_routine_no_prop_val <- bartMachine::bart_machine_get_posterior(bart_comp_routine_no_prop, data_complete_routine_val %>%
select(
colnames(bart_comp_routine_no_prop$X)
))
saveRDS(posteriors_comp_routine_no_prop_val, paste0(output_path, "/Assessment/posteriors_comp_routine_no_prop_val.rds"))
}
### residuals calculation
if (class(try(
comp_routine_no_prop_cred_pred_val <- readRDS(paste0(output_path, "/Assessment/comp_routine_no_prop_cred_pred_val.rds"))
, silent = TRUE)) == "try-error") {
comp_routine_no_prop_cred_pred_val <- calc_resid(data_complete_routine_val, posteriors_comp_routine_no_prop_val)
saveRDS(comp_routine_no_prop_cred_pred_val, paste0(output_path, "/Assessment/comp_routine_no_prop_cred_pred_val.rds"))
}
# calculate effects
if (class(try(
comp_routine_no_prop_effects_summary_val <- readRDS(paste0(output_path, "/Final_model/Assessment/comp_routine_no_prop_effects_summary_val.rds"))
, silent = TRUE)) == "try-error") {
comp_routine_no_prop_effects_summary_val <- calc_effect_summary(bart_comp_routine_no_prop, data_complete_routine_val)
saveRDS(comp_routine_no_prop_effects_summary_val, paste0(output_path, "/Final_model/Assessment/comp_routine_no_prop_effects_summary_val.rds"))
}
## plot effects validation
predicted_observed_val <- data_complete_routine_val %>%
cbind(hba1c_diff = comp_routine_no_prop_effects_summary_val$mean) %>%
mutate(bestdrug = ifelse(hba1c_diff < 0, "SGLT2", "GLP1"),
hba1c_diff.q = ntile(hba1c_diff, 10))
plot_comp_routine_no_prop_effects_validation_val <- plot_full_effects_validation(predicted_observed_val, dataset = "Val")
## plot histogram of effect
plot_effect_2 <- hist_plot(comp_routine_no_prop_effects_summary_val, "", -15, 20)
plot_comp_routine_no_prop_effects <- cowplot::plot_grid(
#title
cowplot::ggdraw() +
cowplot::draw_label(
"Model fitting: Complete data (Routine/No propensity score)")
,
cowplot::plot_grid(
plot_effect_1,
plot_effect_2
, ncol = 2, nrow = 1, labels = c("A", "B")
), ncol = 1, nrow = 2, rel_heights = c(0.1,1)
)
# assessment of R2, RSS, RMSE
if (class(try(
assessment_comp_routine_no_prop <- readRDS(paste0(output_path, "/Assessment/assessment_comp_routine_no_prop.rds"))
, silent = TRUE)) == "try-error") {
assessment_values_dev <- calc_assessment(data_complete_routine_dev, posteriors_comp_routine_no_prop_dev)
assessment_values_val <- calc_assessment(data_complete_routine_val, posteriors_comp_routine_no_prop_val)
assessment_comp_routine_no_prop <- rbind(
cbind(t(assessment_values_dev[["r2"]]), Dataset = "Development", statistic = "R2 (bigger is better)"),
cbind(t(assessment_values_val[["r2"]]), Dataset = "Validation", statistic = "R2 (bigger is better)"),
cbind(t(assessment_values_dev[["RSS"]]), Dataset = "Development", statistic = "RSS (smaller is better)"),
cbind(t(assessment_values_val[["RSS"]]), Dataset = "Validation", statistic = "RSS (smaller is better)"),
cbind(t(assessment_values_dev[["RMSE"]]), Dataset = "Development", statistic = "RMSE (smaller is better)"),
cbind(t(assessment_values_val[["RMSE"]]), Dataset = "Validation", statistic = "RMSE (smaller is better)")
)
saveRDS(assessment_comp_routine_no_prop, paste0(output_path, "/Assessment/assessment_comp_routine_no_prop.rds"))
}
plot_comp_routine_no_prop <- resid_plot(comp_routine_no_prop_cred_pred_dev,
comp_routine_no_prop_cred_pred_val,
"Model fitting: Complete data (Routine/No propensity score)")
#############################
### Complete model of only routine data, propensity score (n: 8564(4542))
#############################
bart_comp_routine_prop <- readRDS(paste0(output_path, "/Model_fit/bart_comp_routine_prop.rds"))
bart_comp_routine_prop_model <- readRDS(paste0(output_path, "/Model_fit/bart_comp_routine_prop_model.rds"))
# Dev
data_complete_routine_prop_dev <- final.dev %>%
select(
patid,
pateddrug,
posthba1c_final,
colnames(bart_comp_routine_prop_model$X)[which(colnames(bart_comp_routine_prop_model$X) != "prop_score")]
) %>%
drop_na() %>%
cbind(prop_score = bart_comp_routine_prop$p_hat_train)
## Get posteriors
if (class(try(
posteriors_complete_routine_prop_dev <- readRDS(paste0(output_path, "/Assessment/posteriors_complete_routine_prop_dev.rds"))
, silent = TRUE)) == "try-error") {
posteriors_complete_routine_prop_dev <- bartMachine::bart_machine_get_posterior(bart_comp_routine_prop_model, data_complete_routine_prop_dev %>%
select(
colnames(bart_comp_routine_prop_model$X)
))
saveRDS(posteriors_complete_routine_prop_dev, paste0(output_path, "/Assessment/posteriors_complete_routine_prop_dev.rds"))
}
### residuals calculation
if (class(try(
comp_routine_prop_cred_pred_dev <- readRDS(paste0(output_path, "/Assessment/comp_routine_prop_cred_pred_dev.rds"))
, silent = TRUE)) == "try-error") {
comp_routine_prop_cred_pred_dev <- calc_resid(data_complete_routine_prop_dev, posteriors_complete_routine_prop_dev)
saveRDS(comp_routine_prop_cred_pred_dev, paste0(output_path, "/Assessment/comp_routine_prop_cred_pred_dev.rds"))
}
# calculate effects
if (class(try(
comp_routine_prop_effects_summary_dev <- readRDS(paste0(output_path, "/Final_model/Assessment/comp_routine_prop_effects_summary_dev.rds"))
, silent = TRUE)) == "try-error") {
comp_routine_prop_effects_summary_dev <- calc_effect_summary(bart_comp_routine_prop_model, data_complete_routine_prop_dev)
saveRDS(comp_routine_prop_effects_summary_dev, paste0(output_path, "/Final_model/Assessment/comp_routine_prop_effects_summary_dev.rds"))
}
## plot effects validation
predicted_observed_dev <- data_complete_routine_prop_dev %>%
cbind(hba1c_diff = comp_routine_prop_effects_summary_dev$mean) %>%
mutate(bestdrug = ifelse(hba1c_diff < 0, "SGLT2", "GLP1"),
hba1c_diff.q = ntile(hba1c_diff, 10))
plot_comp_routine_prop_effects_validation_dev <- plot_full_effects_validation(predicted_observed_dev, dataset = "Dev")
## plot histogram of effect
plot_effect_1 <- hist_plot(comp_routine_prop_effects_summary_dev, "", -15, 20)
# Val
data_complete_routine_prop_val <- final.val %>%
select(
patid,
pateddrug,
posthba1c_final,
colnames(bart_comp_routine_prop_model$X)[which(colnames(bart_comp_routine_prop_model$X) != "prop_score")]
) %>%
drop_na()
## calculate propensity score
if (class(try(
prop_score_complete_routine_prop <- readRDS(paste0(output_path,"/Assessment/prop_score_complete_routine_prop.rds"))
, silent = TRUE)) == "try-error") {
prop_score_complete_routine_prop <- predict(bart_comp_routine_prop, data_complete_routine_prop_val %>%
select(
colnames(bart_comp_routine_prop$X)
))
saveRDS(prop_score_complete_routine_prop, paste0(output_path, "/Assessment/prop_score_complete_routine_prop.rds"))
}
# Add
data_complete_routine_prop_val <- data_complete_routine_prop_val %>%
cbind(prop_score = prop_score_complete_routine_prop)
## Get posteriors
if (class(try(
posteriors_complete_routine_prop_val <- readRDS(paste0(output_path, "/Assessment/posteriors_complete_routine_prop_val.rds"))
, silent = TRUE)) == "try-error") {
posteriors_complete_routine_prop_val <- bartMachine::bart_machine_get_posterior(bart_comp_routine_prop_model, data_complete_routine_prop_val %>%
select(
colnames(bart_comp_routine_prop_model$X)
))
saveRDS(posteriors_complete_routine_prop_val, paste0(output_path, "/Assessment/posteriors_complete_routine_prop_val.rds"))
}
### residuals calculation
if (class(try(
comp_routine_prop_cred_pred_val <- readRDS(paste0(output_path, "/Assessment/comp_routine_prop_cred_pred_val.rds"))
, silent = TRUE)) == "try-error") {
comp_routine_prop_cred_pred_val <- calc_resid(data_complete_routine_prop_val, posteriors_complete_routine_prop_val)
saveRDS(comp_routine_prop_cred_pred_val, paste0(output_path, "/Assessment/comp_routine_prop_cred_pred_val.rds"))
}
# calculate effects
if (class(try(
comp_routine_prop_effects_summary_val <- readRDS(paste0(output_path, "/Final_model/Assessment/comp_routine_prop_effects_summary_val.rds"))
, silent = TRUE)) == "try-error") {
comp_routine_prop_effects_summary_val <- calc_effect_summary(bart_comp_routine_prop_model, data_complete_routine_prop_val)
saveRDS(comp_routine_prop_effects_summary_val, paste0(output_path, "/Final_model/Assessment/comp_routine_prop_effects_summary_val.rds"))
}
## plot effects validation
predicted_observed_val <- data_complete_routine_prop_val %>%
cbind(hba1c_diff = comp_routine_prop_effects_summary_val$mean) %>%
mutate(bestdrug = ifelse(hba1c_diff < 0, "SGLT2", "GLP1"),
hba1c_diff.q = ntile(hba1c_diff, 10))
plot_comp_routine_prop_effects_validation_val <- plot_full_effects_validation(predicted_observed_val, dataset = "Val")
## plot histogram of effect
plot_effect_2 <- hist_plot(comp_routine_prop_effects_summary_val, "", -15, 20)
plot_comp_routine_prop_effects <- cowplot::plot_grid(
#title
cowplot::ggdraw() +
cowplot::draw_label(
"Model fitting: Complete data (Routine/Propensity score)")
,
cowplot::plot_grid(
plot_effect_1,
plot_effect_2
, ncol = 2, nrow = 1, labels = c("A", "B")
), ncol = 1, nrow = 2, rel_heights = c(0.1,1)
)
# assessment of R2, RSS, RMSE
if (class(try(
assessment_comp_routine_prop <- readRDS(paste0(output_path, "/Assessment/assessment_comp_routine_prop.rds"))
, silent = TRUE)) == "try-error") {
assessment_values_dev <- calc_assessment(data_complete_routine_prop_dev, posteriors_complete_routine_prop_dev)
assessment_values_val <- calc_assessment(data_complete_routine_prop_val, posteriors_complete_routine_prop_val)
assessment_comp_routine_prop <- rbind(
cbind(t(assessment_values_dev[["r2"]]), Dataset = "Development", statistic = "R2 (bigger is better)"),
cbind(t(assessment_values_val[["r2"]]), Dataset = "Validation", statistic = "R2 (bigger is better)"),
cbind(t(assessment_values_dev[["RSS"]]), Dataset = "Development", statistic = "RSS (smaller is better)"),
cbind(t(assessment_values_val[["RSS"]]), Dataset = "Validation", statistic = "RSS (smaller is better)"),
cbind(t(assessment_values_dev[["RMSE"]]), Dataset = "Development", statistic = "RMSE (smaller is better)"),
cbind(t(assessment_values_val[["RMSE"]]), Dataset = "Validation", statistic = "RMSE (smaller is better)")
)
saveRDS(assessment_comp_routine_prop, paste0(output_path, "/Assessment/assessment_comp_routine_prop.rds"))
}
plot_comp_routine_prop <- resid_plot(comp_routine_prop_cred_pred_dev,
comp_routine_prop_cred_pred_val,
"Model fitting: Complete data (Routine/Propensity score)")
#############################
### Incomplete model of only routine data, no propensity score (n: 9866(5978))
#############################
bart_incomp_routine_no_prop <- readRDS(paste0(output_path, "/Model_fit/bart_incomp_routine_no_prop.rds"))
# Dev
data_incomplete_routine_dev <- final.dev %>%
select(
patid,
pateddrug,
posthba1c_final,
colnames(bart_incomp_routine_no_prop$X)
)
## Get posteriors
if (class(try(
posteriors_incomp_routine_no_prop_dev <- readRDS(paste0(output_path, "/Assessment/posteriors_incomp_routine_no_prop_dev.rds"))
, silent = TRUE)) == "try-error") {
posteriors_incomp_routine_no_prop_dev <- bartMachine::bart_machine_get_posterior(bart_incomp_routine_no_prop, data_incomplete_routine_dev %>%
select(
colnames(bart_incomp_routine_no_prop$X)
))
saveRDS(posteriors_incomp_routine_no_prop_dev, paste0(output_path, "/Assessment/posteriors_incomp_routine_no_prop_dev.rds"))
}
### residuals calculation
if (class(try(
incomp_routine_no_prop_cred_pred_dev <- readRDS(paste0(output_path, "/Assessment/incomp_routine_no_prop_cred_pred_dev.rds"))
, silent = TRUE)) == "try-error") {
incomp_routine_no_prop_cred_pred_dev <- calc_resid(data_incomplete_routine_dev, posteriors_incomp_routine_no_prop_dev)
saveRDS(incomp_routine_no_prop_cred_pred_dev, paste0(output_path, "/Assessment/incomp_routine_no_prop_cred_pred_dev.rds"))
}
# calculate effects
if (class(try(
incomp_routine_no_prop_effects_summary_dev <- readRDS(paste0(output_path, "/Final_model/Assessment/incomp_routine_no_prop_effects_summary_dev.rds"))
, silent = TRUE)) == "try-error") {
incomp_routine_no_prop_effects_summary_dev <- calc_effect_summary(bart_incomp_routine_no_prop, data_incomplete_routine_dev)
saveRDS(incomp_routine_no_prop_effects_summary_dev, paste0(output_path, "/Final_model/Assessment/incomp_routine_no_prop_effects_summary_dev.rds"))
}
## plot effects validation
predicted_observed_dev <- data_incomplete_routine_dev %>%
cbind(hba1c_diff = incomp_routine_no_prop_effects_summary_dev$mean) %>%
mutate(bestdrug = ifelse(hba1c_diff < 0, "SGLT2", "GLP1"),
hba1c_diff.q = ntile(hba1c_diff, 10))
plot_incomp_routine_no_prop_effects_validation_dev <- plot_full_effects_validation(predicted_observed_dev, dataset = "Dev")
## plot histogram of effect
plot_effect_1 <- hist_plot(incomp_routine_no_prop_effects_summary_dev, "", -15, 20)
# Val
data_incomplete_routine_val <- final.val %>%
select(
patid,
pateddrug,
posthba1c_final,
colnames(bart_incomp_routine_no_prop$X)
)
## Get posteriors
if (class(try(
posteriors_incomp_routine_no_prop_val <- readRDS(paste0(output_path, "/Assessment/posteriors_incomp_routine_no_prop_val.rds"))
, silent = TRUE)) == "try-error") {
posteriors_incomp_routine_no_prop_val <- bartMachine::bart_machine_get_posterior(bart_incomp_routine_no_prop, data_incomplete_routine_val %>%
select(
colnames(bart_incomp_routine_no_prop$X)
))
saveRDS(posteriors_incomp_routine_no_prop_val, paste0(output_path, "/Assessment/posteriors_incomp_routine_no_prop_val.rds"))
}
### residuals calculation
if (class(try(
incomp_routine_no_prop_cred_pred_val <- readRDS(paste0(output_path, "/Assessment/incomp_routine_no_prop_cred_pred_val.rds"))
, silent = TRUE)) == "try-error") {
incomp_routine_no_prop_cred_pred_val <- calc_resid(data_incomplete_routine_val, posteriors_incomp_routine_no_prop_val)
saveRDS(incomp_routine_no_prop_cred_pred_val, paste0(output_path, "/Assessment/incomp_routine_no_prop_cred_pred_val.rds"))
}
# calculate effects
if (class(try(
incomp_routine_no_prop_effects_summary_val <- readRDS(paste0(output_path, "/Final_model/Assessment/incomp_routine_no_prop_effects_summary_val.rds"))
, silent = TRUE)) == "try-error") {
incomp_routine_no_prop_effects_summary_val <- calc_effect_summary(bart_incomp_routine_no_prop, data_incomplete_routine_val)
saveRDS(incomp_routine_no_prop_effects_summary_val, paste0(output_path, "/Final_model/Assessment/incomp_routine_no_prop_effects_summary_val.rds"))
}
## plot effects validation
predicted_observed_val <- data_incomplete_routine_val %>%
cbind(hba1c_diff = incomp_routine_no_prop_effects_summary_val$mean) %>%
mutate(bestdrug = ifelse(hba1c_diff < 0, "SGLT2", "GLP1"),
hba1c_diff.q = ntile(hba1c_diff, 10))
plot_incomp_routine_no_prop_effects_validation_val <- plot_full_effects_validation(predicted_observed_val, dataset = "Val")
## plot histogram of effect
plot_effect_2 <- hist_plot(incomp_routine_no_prop_effects_summary_val, "", -15, 20)
plot_incomp_routine_no_prop_effects <- cowplot::plot_grid(
#title
cowplot::ggdraw() +
cowplot::draw_label(
"Model fitting: Incomplete data (Routine/No propensity score)")
,
cowplot::plot_grid(
plot_effect_1,
plot_effect_2
, ncol = 2, nrow = 1, labels = c("A", "B")
), ncol = 1, nrow = 2, rel_heights = c(0.1,1)
)
# assessment of R2, RSS, RMSE
if (class(try(
assessment_incomp_routine_no_prop <- readRDS(paste0(output_path, "/Assessment/assessment_incomp_routine_no_prop.rds"))
, silent = TRUE)) == "try-error") {
assessment_values_dev <- calc_assessment(data_incomplete_routine_dev, posteriors_incomp_routine_no_prop_dev)
assessment_values_val <- calc_assessment(data_incomplete_routine_val, posteriors_incomp_routine_no_prop_val)
assessment_incomp_routine_no_prop <- rbind(
cbind(t(assessment_values_dev[["r2"]]), Dataset = "Development", statistic = "R2 (bigger is better)"),
cbind(t(assessment_values_val[["r2"]]), Dataset = "Validation", statistic = "R2 (bigger is better)"),
cbind(t(assessment_values_dev[["RSS"]]), Dataset = "Development", statistic = "RSS (smaller is better)"),
cbind(t(assessment_values_val[["RSS"]]), Dataset = "Validation", statistic = "RSS (smaller is better)"),
cbind(t(assessment_values_dev[["RMSE"]]), Dataset = "Development", statistic = "RMSE (smaller is better)"),
cbind(t(assessment_values_val[["RMSE"]]), Dataset = "Validation", statistic = "RMSE (smaller is better)")
)
saveRDS(assessment_incomp_routine_no_prop, paste0(output_path, "/Assessment/assessment_incomp_routine_no_prop.rds"))
}
plot_incomp_routine_no_prop <- resid_plot(incomp_routine_no_prop_cred_pred_dev,
incomp_routine_no_prop_cred_pred_val,
"Model fitting: Incomplete data (Routine/No propensity score)")
#############################
### Incomplete model of all data, no propensity score (n: 9866(5978))
#############################
bart_incomp_no_prop <- readRDS(paste0(output_path, "/Model_fit/bart_incomp_no_prop.rds"))
# Dev
data_incomplete_dev <- final.dev
## Get posteriors
if (class(try(
posteriors_incomp_no_prop_dev <- readRDS(paste0(output_path, "/Assessment/posteriors_incomp_no_prop_dev.rds"))
, silent = TRUE)) == "try-error") {
posteriors_incomp_no_prop_dev <- bartMachine::bart_machine_get_posterior(bart_incomp_no_prop, data_incomplete_dev %>%
select(
colnames(bart_incomp_no_prop$X)
))
saveRDS(posteriors_incomp_no_prop_dev, paste0(output_path, "/Assessment/posteriors_incomp_no_prop_dev.rds"))
}
### residuals calculation
if (class(try(
incomp_no_prop_cred_pred_dev <- readRDS(paste0(output_path, "/Assessment/incomp_no_prop_cred_pred_dev.rds"))
, silent = TRUE)) == "try-error") {
incomp_no_prop_cred_pred_dev <- calc_resid(data_incomplete_dev, posteriors_incomp_no_prop_dev)
saveRDS(incomp_no_prop_cred_pred_dev, paste0(output_path, "/Assessment/incomp_no_prop_cred_pred_dev.rds"))
}
# calculate effects
if (class(try(
incomp_no_prop_effects_summary_dev <- readRDS(paste0(output_path, "/Final_model/Assessment/incomp_no_prop_effects_summary_dev.rds"))
, silent = TRUE)) == "try-error") {
incomp_no_prop_effects_summary_dev <- calc_effect_summary(bart_incomp_no_prop, data_incomplete_dev)
saveRDS(incomp_no_prop_effects_summary_dev, paste0(output_path, "/Final_model/Assessment/incomp_no_prop_effects_summary_dev.rds"))
}
## plot effects validation
predicted_observed_dev <- data_incomplete_dev %>%
cbind(hba1c_diff = incomp_no_prop_effects_summary_dev$mean) %>%
mutate(bestdrug = ifelse(hba1c_diff < 0, "SGLT2", "GLP1"),
hba1c_diff.q = ntile(hba1c_diff, 10))
plot_incomp_no_prop_effects_validation_dev <- plot_full_effects_validation(predicted_observed_dev, dataset = "Dev")
## plot histogram of effect
plot_effect_1 <- hist_plot(incomp_no_prop_effects_summary_dev, "", -15, 20)
# Val
data_incomplete_val <- final.val
## Get posteriors
if (class(try(
posteriors_incomp_no_prop_val <- readRDS(paste0(output_path, "/Assessment/posteriors_incomp_no_prop_val.rds"))
, silent = TRUE)) == "try-error") {
posteriors_incomp_no_prop_val <- bartMachine::bart_machine_get_posterior(bart_incomp_no_prop, data_incomplete_val %>%
select(
colnames(bart_incomp_no_prop$X)
))
saveRDS(posteriors_incomp_no_prop_val, paste0(output_path, "/Assessment/posteriors_incomp_no_prop_val.rds"))
}
### residuals calculation
if (class(try(
incomp_no_prop_cred_pred_val <- readRDS(paste0(output_path, "/Assessment/incomp_no_prop_cred_pred_val.rds"))
, silent = TRUE)) == "try-error") {
incomp_no_prop_cred_pred_val <- calc_resid(data_incomplete_val, posteriors_incomp_no_prop_val)
saveRDS(incomp_no_prop_cred_pred_val, paste0(output_path, "/Assessment/incomp_no_prop_cred_pred_val.rds"))
}
# calculate effects
if (class(try(
incomp_no_prop_effects_summary_val <- readRDS(paste0(output_path, "/Final_model/Assessment/incomp_no_prop_effects_summary_val.rds"))
, silent = TRUE)) == "try-error") {
incomp_no_prop_effects_summary_val <- calc_effect_summary(bart_incomp_no_prop, data_incomplete_val)
saveRDS(incomp_no_prop_effects_summary_val, paste0(output_path, "/Final_model/Assessment/incomp_no_prop_effects_summary_val.rds"))
}
## plot effects validation
predicted_observed_val <- data_incomplete_val %>%
cbind(hba1c_diff = incomp_no_prop_effects_summary_val$mean) %>%
mutate(bestdrug = ifelse(hba1c_diff < 0, "SGLT2", "GLP1"),
hba1c_diff.q = ntile(hba1c_diff, 10))
plot_incomp_no_prop_effects_validation_val <- plot_full_effects_validation(predicted_observed_val, dataset = "Val")
## plot histogram of effect
plot_effect_2 <- hist_plot(incomp_no_prop_effects_summary_val, "", -15, 20)
plot_incomp_no_prop_effects <- cowplot::plot_grid(
#title
cowplot::ggdraw() +
cowplot::draw_label(
"Model fitting: Incomplete data (No propensity score)")
,
cowplot::plot_grid(
plot_effect_1,
plot_effect_2
, ncol = 2, nrow = 1, labels = c("A", "B")
), ncol = 1, nrow = 2, rel_heights = c(0.1,1)
)
# assessment of R2, RSS, RMSE
if (class(try(
assessment_incomp_no_prop <- readRDS(paste0(output_path, "/Assessment/assessment_incomp_no_prop.rds"))
, silent = TRUE)) == "try-error") {
assessment_values_dev <- calc_assessment(data_incomplete_dev, posteriors_incomp_no_prop_dev)
assessment_values_val <- calc_assessment(data_incomplete_val, posteriors_incomp_no_prop_val)
assessment_incomp_no_prop <- rbind(
cbind(t(assessment_values_dev[["r2"]]), Dataset = "Development", statistic = "R2 (bigger is better)"),
cbind(t(assessment_values_val[["r2"]]), Dataset = "Validation", statistic = "R2 (bigger is better)"),
cbind(t(assessment_values_dev[["RSS"]]), Dataset = "Development", statistic = "RSS (smaller is better)"),
cbind(t(assessment_values_val[["RSS"]]), Dataset = "Validation", statistic = "RSS (smaller is better)"),
cbind(t(assessment_values_dev[["RMSE"]]), Dataset = "Development", statistic = "RMSE (smaller is better)"),
cbind(t(assessment_values_val[["RMSE"]]), Dataset = "Validation", statistic = "RMSE (smaller is better)")
)
saveRDS(assessment_incomp_no_prop, paste0(output_path, "/Assessment/assessment_incomp_no_prop.rds"))
}
plot_incomp_no_prop <- resid_plot(incomp_no_prop_cred_pred_dev,
incomp_no_prop_cred_pred_val,
"Model fitting: Incomplete data (No propensity score)")
#############################
### VARIABLE SELECTION: Incomplete model of all data, no propensity score (n: 9866(5978))
#############################
bart_incomp_no_prop_var_select <- readRDS(paste0(output_path, "/Model_fit/bart_incomp_no_prop_var_select.rds"))
# Dev
data_incomplete_dev_var_select <- final.dev %>%
select(
patid,
pateddrug,
posthba1c_final,
colnames(bart_incomp_no_prop_var_select$X)
)
## Get posteriors
if (class(try(
posteriors_incomp_no_prop_var_select_dev <- readRDS(paste0(output_path, "/Assessment/posteriors_incomp_no_prop_var_select_dev.rds"))
, silent = TRUE)) == "try-error") {
posteriors_incomp_no_prop_var_select_dev <- bartMachine::bart_machine_get_posterior(bart_incomp_no_prop_var_select, data_incomplete_dev_var_select %>%
select(
colnames(bart_incomp_no_prop_var_select$X)
))
saveRDS(posteriors_incomp_no_prop_var_select_dev, paste0(output_path, "/Assessment/posteriors_incomp_no_prop_var_select_dev.rds"))
}
### residuals calculation
if (class(try(
incomp_no_prop_var_select_cred_pred_dev <- readRDS(paste0(output_path, "/Assessment/incomp_no_prop_var_select_cred_pred_dev.rds"))
, silent = TRUE)) == "try-error") {
incomp_no_prop_var_select_cred_pred_dev <- calc_resid(data_incomplete_dev_var_select, posteriors_incomp_no_prop_var_select_dev)
saveRDS(incomp_no_prop_var_select_cred_pred_dev, paste0(output_path, "/Assessment/incomp_no_prop_var_select_cred_pred_dev.rds"))
}
# calculate effects
if (class(try(
incomp_no_prop_var_select_effects_summary_dev <- readRDS(paste0(output_path, "/Final_model/Assessment/incomp_no_prop_var_select_effects_summary_dev.rds"))
, silent = TRUE)) == "try-error") {
incomp_no_prop_var_select_effects_summary_dev <- calc_effect_summary(bart_incomp_no_prop_var_select, data_incomplete_dev_var_select)
saveRDS(incomp_no_prop_var_select_effects_summary_dev, paste0(output_path, "/Final_model/Assessment/incomp_no_prop_var_select_effects_summary_dev.rds"))
}
## plot effects validation
predicted_observed_dev <- data_incomplete_dev_var_select %>%
cbind(hba1c_diff = incomp_no_prop_var_select_effects_summary_dev$mean) %>%
mutate(bestdrug = ifelse(hba1c_diff < 0, "SGLT2", "GLP1"),
hba1c_diff.q = ntile(hba1c_diff, 10))
plot_incomp_no_prop_var_select_effects_validation_dev <- plot_full_effects_validation(predicted_observed_dev, dataset = "Dev")
## plot histogram of effect
plot_effect_1 <- hist_plot(incomp_no_prop_var_select_effects_summary_dev, "", -15, 20)
# Val
data_incomplete_val_var_select <- final.val %>%
select(
patid,
pateddrug,
posthba1c_final,
colnames(bart_incomp_no_prop_var_select$X)
)
## Get posteriors
if (class(try(
posteriors_incomp_no_prop_var_select_val <- readRDS(paste0(output_path, "/Assessment/posteriors_incomp_no_prop_var_select_val.rds"))
, silent = TRUE)) == "try-error") {
posteriors_incomp_no_prop_var_select_val <- bartMachine::bart_machine_get_posterior(bart_incomp_no_prop_var_select, data_incomplete_val_var_select %>%
select(
colnames(bart_incomp_no_prop_var_select$X)
))
saveRDS(posteriors_incomp_no_prop_var_select_val, paste0(output_path, "/Assessment/posteriors_incomp_no_prop_var_select_val.rds"))
}
### residuals calculation
if (class(try(
incomp_no_prop_var_select_cred_pred_val <- readRDS(paste0(output_path, "/Assessment/incomp_no_prop_var_select_cred_pred_val.rds"))
, silent = TRUE)) == "try-error") {
incomp_no_prop_var_select_cred_pred_val <- calc_resid(data_incomplete_val_var_select, posteriors_incomp_no_prop_var_select_val)
saveRDS(incomp_no_prop_var_select_cred_pred_val, paste0(output_path, "/Assessment/incomp_no_prop_var_select_cred_pred_val.rds"))
}
# calculate effects
if (class(try(
incomp_no_prop_var_select_effects_summary_val <- readRDS(paste0(output_path, "/Final_model/Assessment/incomp_no_prop_var_select_effects_summary_val.rds"))
, silent = TRUE)) == "try-error") {
incomp_no_prop_var_select_effects_summary_val <- calc_effect_summary(bart_incomp_no_prop_var_select, data_incomplete_val_var_select)
saveRDS(incomp_no_prop_var_select_effects_summary_val, paste0(output_path, "/Final_model/Assessment/incomp_no_prop_var_select_effects_summary_val.rds"))
}
## plot effects validation
predicted_observed_val <- data_incomplete_val_var_select %>%
cbind(hba1c_diff = incomp_no_prop_var_select_effects_summary_val$mean) %>%
mutate(bestdrug = ifelse(hba1c_diff < 0, "SGLT2", "GLP1"),
hba1c_diff.q = ntile(hba1c_diff, 10))
plot_incomp_no_prop_var_select_effects_validation_val <- plot_full_effects_validation(predicted_observed_val, dataset = "Val")
## plot histogram of effect
plot_effect_2 <- hist_plot(incomp_no_prop_var_select_effects_summary_val, "", -15, 20)
plot_incomp_no_prop_var_select_effects <- cowplot::plot_grid(
#title
cowplot::ggdraw() +
cowplot::draw_label(
"Model fitting: Variable Selection 1, Incomplete data (No propensity score)")
,
cowplot::plot_grid(