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02c_models_cox_vs_aft_poc.R
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02c_models_cox_vs_aft_poc.R
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######################################
# This script:
# - imports processed data
# - fits a number of stratified coxph and AFT models to several subsets of the data
######################################
# Preliminaries ----
## Import libraries
library('tidyverse')
library('lubridate')
library('survival')
library('gtsummary')
library('gt')
## Create output directory
dir.create(here::here("output", "models", "testing", "cox_vs_aft"), showWarnings = FALSE, recursive=TRUE)
## Import processed data
data_tte <- read_rds(here::here("output", "data", "data_modelling.rds"))
## Converts logical to integer so that model coefficients print nicely in gtsummary methods
data_cox <- data_tte %>%
mutate(
across(
where(is.logical),
~.x*1L
)
)
# MODELS ----
## Run models on different subsets of data (i.e. 10/50/100 practices)
sample_size = c(10,50,100,250)
timings_all = data.frame(Method = c("Coxph",
"Stratified coxph",
"AFT",
"Stratified AFT",
"AFT with RE"))
for (i in 1:length(sample_size)) {
# Subset data
data_sub <- data_cox %>%
filter(practice_id_latest_active_registration %in% unique(data_cox$practice_id_latest_active_registration)[1:sample_size[i]])
# Fit models and save model output
## Cox model - adjusted; baseline demographics, comorbs, geographical, flu, shielding
mod.coxph.adj <- coxph(Surv(follow_up_time, covid_vax) ~
ageband + sex + ethnicity + morbid_obesity +
chronic_heart_disease + diabetes + chronic_kidney_disease_diagnostic + chronic_kidney_disease_all_stages +
chronic_kidney_disease_all_stages_3_5 + sev_mental_ill + learning_disability + chronic_neuro_dis_inc_sig_learn_dis +
asplenia + chronic_liver_disease + chronis_respiratory_disease + immunosuppression_diagnosis +
immunosuppression_medication + imd + rural_urban + prior_covid + flu_vaccine + shielded + shielded_since_feb_15 +
rural_urban + region,
data = data_sub)
write_rds(mod.coxph.adj, here::here("output", "models", "testing", "cox_vs_aft",
paste("mod_coxph_adj_", sample_size[i],".rds", sep = "")), compress="gz")
## Stratified Cox model - adjusted; baseline demographics, comorbs, geographical, flu, shielding & practice as strata
mod.stratcoxph.adj <- coxph(Surv(follow_up_time, covid_vax) ~
ageband + sex + ethnicity + morbid_obesity +
chronic_heart_disease + diabetes + chronic_kidney_disease_diagnostic + chronic_kidney_disease_all_stages +
chronic_kidney_disease_all_stages_3_5 + sev_mental_ill + learning_disability + chronic_neuro_dis_inc_sig_learn_dis +
asplenia + chronic_liver_disease + chronis_respiratory_disease + immunosuppression_diagnosis +
immunosuppression_medication + imd + rural_urban + prior_covid + flu_vaccine + shielded + shielded_since_feb_15 +
rural_urban + region + strata(practice_id_latest_active_registration),
data = data_sub)
write_rds(mod.stratcoxph.adj, here::here("output", "models", "testing", "cox_vs_aft",
paste("mod_stratcoxph_adj_", sample_size[i],".rds", sep = "")), compress="gz")
# AFT model - adjusted; baseline demographics, comorbs, geographical, flu, shielding
mod.aft.adj <- survreg(Surv(follow_up_time, covid_vax) ~
ageband + sex + ethnicity + morbid_obesity +
chronic_heart_disease + diabetes + chronic_kidney_disease_diagnostic + chronic_kidney_disease_all_stages +
chronic_kidney_disease_all_stages_3_5 + sev_mental_ill + learning_disability + chronic_neuro_dis_inc_sig_learn_dis +
asplenia + chronic_liver_disease + chronis_respiratory_disease + immunosuppression_diagnosis +
immunosuppression_medication + imd + rural_urban + prior_covid + flu_vaccine + shielded + shielded_since_feb_15 +
rural_urban + region,
dist = "lognormal",
data = data_sub)
write_rds(mod.aft.adj, here::here("output", "models", "testing", "cox_vs_aft",
paste("mod_aft_adj_", sample_size[i],".rds", sep = "")), compress="gz")
# Stratified AFT model - adjusted; baseline demographics, comorbs, geographical, flu, shielding & practice as strata
mod.strat.aft.adj <- survreg(Surv(follow_up_time, covid_vax) ~
ageband + sex + ethnicity + morbid_obesity +
chronic_heart_disease + diabetes + chronic_kidney_disease_diagnostic + chronic_kidney_disease_all_stages +
chronic_kidney_disease_all_stages_3_5 + sev_mental_ill + learning_disability + chronic_neuro_dis_inc_sig_learn_dis +
asplenia + chronic_liver_disease + chronis_respiratory_disease + immunosuppression_diagnosis +
immunosuppression_medication + imd + rural_urban + prior_covid + flu_vaccine + shielded + shielded_since_feb_15 +
rural_urban + region + strata(practice_id_latest_active_registration),
data = data_sub)
write_rds(mod.strat.aft.adj, here::here("output", "models", "testing", "cox_vs_aft",
paste("mod_strat_aft_adj_", sample_size[i],".rds", sep = "")), compress="gz")
# AFT model - adjusted; baseline demographics, comorbs, geographical, flu, shielding & practice as random effect
mod.aft.re.adj <- survreg(Surv(follow_up_time, covid_vax) ~
ageband + sex + ethnicity + morbid_obesity +
chronic_heart_disease + diabetes + chronic_kidney_disease_diagnostic + chronic_kidney_disease_all_stages +
chronic_kidney_disease_all_stages_3_5 + sev_mental_ill + learning_disability + chronic_neuro_dis_inc_sig_learn_dis +
asplenia + chronic_liver_disease + chronis_respiratory_disease + immunosuppression_diagnosis +
immunosuppression_medication + imd + rural_urban + prior_covid + flu_vaccine + shielded + shielded_since_feb_15 +
rural_urban + region + frailty(practice_id_latest_active_registration),
data = data_sub)
write_rds(mod.aft.re.adj, here::here("output", "models", "testing", "cox_vs_aft",
paste("mod_aft_re_adj_", sample_size[i],".rds", sep = "")), compress="gz")
# Tables
## Cox model - adjusted
table_results_coxph.adj <- data.frame(summary(mod.coxph.adj)$coefficients) %>%
rownames_to_column(var = "Variable") %>%
mutate(LCI = round(exp.coef. - 1.96*se.coef., digits = 2),
UCI = round(exp.coef. + 1.96*se.coef., digits = 2),
`CoxPH HR (95% CI)` = paste(round(exp.coef., digits = 2),
" (", LCI, " - ", UCI, ")", sep = "")) %>%
select(Variable, `CoxPH HR (95% CI)`)
## Stratified Cox model
table_results_mod.stratcoxph.adj <- data.frame(summary(mod.stratcoxph.adj)$coefficients) %>%
rownames_to_column(var = "Variable") %>%
mutate(LCI = round(exp.coef. - 1.96*se.coef., digits = 2),
UCI = round(exp.coef. + 1.96*se.coef., digits = 2),
`Strat CoxPH HR (95% CI)` = paste(round(exp.coef., digits = 2),
" (", LCI, " - ", UCI, ")", sep = "")) %>%
select(Variable, `Strat CoxPH HR (95% CI)`)
# AFT model
table_results_mod.aft.adj <- data.frame(summary(mod.aft.adj)$table) %>%
rownames_to_column(var = "Variable") %>%
filter(!(Variable %in% c("(Intercept)", "Log(scale)"))) %>%
mutate(LCI = round(Value - 1.96*Std..Error, digits = 2),
UCI = round(Value + 1.96*Std..Error, digits = 2),
`AFT Time ratio (95% CI)` = paste(round(Value, digits = 2),
" (", LCI, " - ", UCI, ")", sep = "")) %>%
select(Variable, `AFT Time ratio (95% CI)`)
# Stratified AFT model - adjusted; baseline demographics, comorbs, geographical, flu, shielding & practice as strata
table_results_mod.strat.aft.adj <- data.frame(summary(mod.strat.aft.adj)$table) %>%
rownames_to_column(var = "Variable") %>%
mutate(LCI = round(Value - 1.96*Std..Error, digits = 2),
UCI = round(Value + 1.96*Std..Error, digits = 2),
`Strat AFT Time ratio (95% CI)` = paste(round(Value, digits = 2),
" (", LCI, " - ", UCI, ")", sep = "")) %>%
select(Variable, `Strat AFT Time ratio (95% CI)`)
table_results_mod.strat.aft.adj <- table_results_mod.strat.aft.adj[2:62,]
# AFT model with random effect
table_results_mod.aft.re.adj <- data.frame(summary(mod.aft.re.adj)$table) %>%
rownames_to_column(var = "Variable") %>%
mutate(LCI = round(Value - 1.96*Std..Error, digits = 2),
UCI = round(Value + 1.96*Std..Error, digits = 2),
`AFT with RE Time ratio (95% CI)` = paste(round(Value, digits = 2),
" (", LCI, " - ", UCI, ")", sep = "")) %>%
select(Variable, `AFT with RE Time ratio (95% CI)`)
table_results_mod.aft.re.adj <- table_results_mod.aft.re.adj[2:62,]
## Combine tables
table_results <- left_join(table_results_coxph.adj, table_results_mod.stratcoxph.adj, by = c("Variable")) %>%
left_join(table_results_mod.aft.adj, by = c("Variable")) %>%
left_join(table_results_mod.strat.aft.adj, by = c("Variable")) %>%
left_join(table_results_mod.aft.re.adj, by = c("Variable"))
write_csv(table_results, here::here("output", "models", "testing", "cox_vs_aft",
paste("table_results_", sample_size[i],".csv", sep = "")))
# Timings
## Summary table
timings <- data.frame(Method = c("Coxph",
"Stratified coxph",
"AFT",
"Stratified AFT",
"AFT with RE"),
Time = NA)
## Cox model
fit1 <- system.time(coxph(Surv(follow_up_time, covid_vax) ~
ageband + sex + ethnicity + morbid_obesity +
chronic_heart_disease + diabetes + chronic_kidney_disease_diagnostic + chronic_kidney_disease_all_stages +
chronic_kidney_disease_all_stages_3_5 + sev_mental_ill + learning_disability + chronic_neuro_dis_inc_sig_learn_dis +
asplenia + chronic_liver_disease + chronis_respiratory_disease + immunosuppression_diagnosis +
immunosuppression_medication + imd + rural_urban + prior_covid + flu_vaccine + shielded + shielded_since_feb_15 +
rural_urban + region,
data = data_sub))
timings[1,2] <- fit1[3]
## Stratified Cox model
fit2 <- system.time(coxph(Surv(follow_up_time, covid_vax) ~
ageband + sex + ethnicity + morbid_obesity +
chronic_heart_disease + diabetes + chronic_kidney_disease_diagnostic + chronic_kidney_disease_all_stages +
chronic_kidney_disease_all_stages_3_5 + sev_mental_ill + learning_disability + chronic_neuro_dis_inc_sig_learn_dis +
asplenia + chronic_liver_disease + chronis_respiratory_disease + immunosuppression_diagnosis +
immunosuppression_medication + imd + rural_urban + prior_covid + flu_vaccine + shielded + shielded_since_feb_15 +
rural_urban + region + strata(practice_id_latest_active_registration),
data = data_sub))
timings[2,2] <- fit2[3]
# AFT model a
fit3 <- system.time(survreg(Surv(follow_up_time, covid_vax) ~
ageband + sex + ethnicity + morbid_obesity +
chronic_heart_disease + diabetes + chronic_kidney_disease_diagnostic + chronic_kidney_disease_all_stages +
chronic_kidney_disease_all_stages_3_5 + sev_mental_ill + learning_disability + chronic_neuro_dis_inc_sig_learn_dis +
asplenia + chronic_liver_disease + chronis_respiratory_disease + immunosuppression_diagnosis +
immunosuppression_medication + imd + rural_urban + prior_covid + flu_vaccine + shielded + shielded_since_feb_15 +
rural_urban + region,
dist = "lognormal",
data = data_sub))
timings[3,2] <- fit3[3]
# AFT model b
fit4 <- system.time(survreg(Surv(follow_up_time, covid_vax) ~
ageband + sex + ethnicity + morbid_obesity +
chronic_heart_disease + diabetes + chronic_kidney_disease_diagnostic + chronic_kidney_disease_all_stages +
chronic_kidney_disease_all_stages_3_5 + sev_mental_ill + learning_disability + chronic_neuro_dis_inc_sig_learn_dis +
asplenia + chronic_liver_disease + chronis_respiratory_disease + immunosuppression_diagnosis +
immunosuppression_medication + imd + rural_urban + prior_covid + flu_vaccine + shielded + shielded_since_feb_15 +
rural_urban + region + strata(practice_id_latest_active_registration),
dist = "lognormal",
data = data_sub))
timings[4,2] <- fit4[3]
# AFT model c
fit5 <- system.time(survreg(Surv(follow_up_time, covid_vax) ~
ageband + sex + ethnicity + morbid_obesity +
chronic_heart_disease + diabetes + chronic_kidney_disease_diagnostic + chronic_kidney_disease_all_stages +
chronic_kidney_disease_all_stages_3_5 + sev_mental_ill + learning_disability + chronic_neuro_dis_inc_sig_learn_dis +
asplenia + chronic_liver_disease + chronis_respiratory_disease + immunosuppression_diagnosis +
immunosuppression_medication + imd + rural_urban + prior_covid + flu_vaccine + shielded + shielded_since_feb_15 +
rural_urban + region + frailty(practice_id_latest_active_registration),
dist = "lognormal",
data = data_sub))
timings[5,2] <- fit5[3]
timings_all <- left_join(timings_all, timings, by = c("Method"))
print(i)
}
## Plot timings
timings_plot <- timings_all %>%
pivot_longer(!Method, names_to = "Number of practices", values_to = "Time") %>%
mutate(`Number of practices` = ifelse(`Number of practices` == "Time.x", 10, `Number of practices`),
`Number of practices` = ifelse(`Number of practices` == "Time.y", 50, `Number of practices`),
`Number of practices` = ifelse(`Number of practices` == "Time.x.x", 100, `Number of practices`),
`Number of practices` = ifelse(`Number of practices` == "Time.y.y", 250, `Number of practices`),
`Number of practices` = as.numeric(`Number of practices`),
Method = factor(Method, levels = c("Coxph",
"Stratified coxph",
"AFT",
"Stratified AFT",
"AFT with RE")))
plot <- ggplot(timings_plot, aes(x = `Number of practices`, y = Time, colour = Method)) +
geom_point() +
geom_line()
ggsave(here::here("output", "models", "testing", "cox_vs_aft", "plot_models_fit_time.svg"),
plot,
units = "cm", width = 10, height = 8)