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03_Final_model.R
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03_Final_model.R
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######################################
# This script:
# - imports processed data
# - fits a mixed effects cox model using the coxme package
# - saves model summaries (tables and figures)
######################################
# Preliminaries ----
## Import libraries
library('tidyverse')
library('lubridate')
library('survival')
library('coxme')
library('gtsummary')
library('gt')
library('survminer')
#library('ehahelper')
## Create output directory
dir.create(here::here("output", "models"), showWarnings = FALSE, recursive=TRUE)
## Function to plot stratified cox model
ggforest2 <- function (model, data = NULL, main = "Hazard ratio", cpositions = c(0.02,
0.22, 0.4), fontsize = 0.7, refLabel = "reference", noDigits = 2)
{
conf.high <- conf.low <- estimate <- NULL
stopifnot(inherits(model, "coxph"))
data <- survminer:::.get_data(model, data = data)
terms <- attr(model$terms, "dataClasses")[-1]
coef <- as.data.frame(broom::tidy(model, conf.int = TRUE))
gmodel <- broom::glance(model)
allTerms <- lapply(seq_along(terms), function(i) {
var <- names(terms)[i]
if(var %in% colnames(data)) {
if (terms[i] %in% c("factor", "character")) {
adf <- as.data.frame(table(data[, var]))
cbind(var = var, adf, pos = 1:nrow(adf))
}
else if (terms[i] == "numeric") {
data.frame(var = var, Var1 = "", Freq = nrow(data),
pos = 1)
}
else {
vars = grep(paste0("^", var, "*."), coef$term,
value = TRUE)
data.frame(var = vars, Var1 = "", Freq = nrow(data),
pos = seq_along(vars))
}
} else {
message(var, "is not found in data columns, and will be skipped.")
}
})
allTermsDF <- do.call(rbind, allTerms)
colnames(allTermsDF) <- c("var", "level", "N", "pos")
inds <- apply(allTermsDF[, 1:2], 1, paste0, collapse = "")
rownames(coef) <- gsub(coef$term, pattern = "`", replacement = "")
toShow <- cbind(allTermsDF, coef[inds, ])[, c("var", "level",
"N", "p.value", "estimate", "conf.low", "conf.high",
"pos")]
toShowExp <- toShow[, 5:7]
toShowExp[is.na(toShowExp)] <- 0
toShowExp <- format(exp(toShowExp), digits = noDigits)
toShowExpClean <- data.frame(toShow, pvalue = signif(toShow[,
4], noDigits + 1), toShowExp)
toShowExpClean$stars <- paste0(round(toShowExpClean$p.value,
noDigits + 1), " ", ifelse(toShowExpClean$p.value < 0.05,
"*", ""), ifelse(toShowExpClean$p.value < 0.01, "*",
""), ifelse(toShowExpClean$p.value < 0.001, "*", ""))
toShowExpClean$ci <- paste0("(", toShowExpClean[, "conf.low.1"],
" - ", toShowExpClean[, "conf.high.1"], ")")
toShowExpClean$estimate.1[is.na(toShowExpClean$estimate)] = refLabel
toShowExpClean$stars[which(toShowExpClean$p.value < 0.001)] = "<0.001 ***"
toShowExpClean$stars[is.na(toShowExpClean$estimate)] = ""
toShowExpClean$ci[is.na(toShowExpClean$estimate)] = ""
toShowExpClean$estimate[is.na(toShowExpClean$estimate)] = 0
toShowExpClean$var = as.character(toShowExpClean$var)
toShowExpClean$var[duplicated(toShowExpClean$var)] = ""
toShowExpClean$N <- paste0("(N=", toShowExpClean$N, ")")
toShowExpClean <- toShowExpClean[nrow(toShowExpClean):1,
]
rangeb <- range(toShowExpClean$conf.low, toShowExpClean$conf.high,
na.rm = TRUE)
breaks <- axisTicks(rangeb/2, log = TRUE, nint = 7)
rangeplot <- rangeb
rangeplot[1] <- rangeplot[1] - diff(rangeb)
rangeplot[2] <- rangeplot[2] + 0.15 * diff(rangeb)
width <- diff(rangeplot)
y_variable <- rangeplot[1] + cpositions[1] * width
y_nlevel <- rangeplot[1] + cpositions[2] * width
y_cistring <- rangeplot[1] + cpositions[3] * width
y_stars <- rangeb[2]
x_annotate <- seq_len(nrow(toShowExpClean))
annot_size_mm <- fontsize * as.numeric(grid::convertX(unit(theme_get()$text$size,
"pt"), "mm"))
p <- ggplot(toShowExpClean, aes(seq_along(var), exp(estimate))) +
geom_rect(aes(xmin = seq_along(var) - 0.5, xmax = seq_along(var) +
0.5, ymin = exp(rangeplot[1]), ymax = exp(rangeplot[2]),
fill = ordered(seq_along(var)%%2 + 1))) + scale_fill_manual(values = c("#FFFFFF33",
"#00000033"), guide = "none") + geom_point(pch = 15,
size = 4) + geom_errorbar(aes(ymin = exp(conf.low), ymax = exp(conf.high)),
width = 0.15) + geom_hline(yintercept = 1, linetype = 3) +
coord_flip(ylim = exp(rangeplot)) + ggtitle(main) + scale_y_log10(name = "",
labels = sprintf("%g", breaks), expand = c(0.02, 0.02),
breaks = breaks) + theme_light() + theme(panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank(), panel.grid.major.y = element_blank(),
legend.position = "none", panel.border = element_blank(),
axis.title.y = element_blank(), axis.text.y = element_blank(),
axis.ticks.y = element_blank(), plot.title = element_text(hjust = 0.5)) +
xlab("") + annotate(geom = "text", x = x_annotate, y = exp(y_variable),
label = toShowExpClean$var, fontface = "bold", hjust = 0,
size = annot_size_mm) + annotate(geom = "text", x = x_annotate,
y = exp(y_nlevel), hjust = 0, label = toShowExpClean$level,
vjust = -0.1, size = annot_size_mm) + annotate(geom = "text",
x = x_annotate, y = exp(y_nlevel), label = toShowExpClean$N,
fontface = "italic", hjust = 0, vjust = ifelse(toShowExpClean$level ==
"", 0.5, 1.1), size = annot_size_mm) + annotate(geom = "text",
x = x_annotate, y = exp(y_cistring), label = toShowExpClean$estimate.1,
size = annot_size_mm, vjust = ifelse(toShowExpClean$estimate.1 ==
"reference", 0.5, -0.1)) + annotate(geom = "text",
x = x_annotate, y = exp(y_cistring), label = toShowExpClean$ci,
size = annot_size_mm, vjust = 1.1, fontface = "italic") +
annotate(geom = "text", x = x_annotate, y = exp(y_stars),
label = toShowExpClean$stars, size = annot_size_mm,
hjust = -0.2, fontface = "italic") + annotate(geom = "text",
x = 0.5, y = exp(y_variable), label = paste0("# Events: ",
gmodel$nevent, "; Global p-value (Log-Rank): ", format.pval(gmodel$p.value.log,
eps = ".001"), " \nAIC: ", round(gmodel$AIC,
2), "; Concordance Index: ", round(gmodel$concordance,
2)), size = annot_size_mm, hjust = 0, vjust = 1.2,
fontface = "italic")
gt <- ggplot_gtable(ggplot_build(p))
gt$layout$clip[gt$layout$name == "panel"] <- "off"
ggpubr::as_ggplot(gt)
}
## Import processed data
data_tte <- read_rds(here::here("output", "data", "data_all.rds"))
## Converts logical to integer so that model coefficients print nicely in gtsummary methods
data_cox <- data_tte %>%
mutate(
across(
where(is.logical),
~.x*1L
)
)
## Exclude practices with less than 100 registered patients
### Registered patients counts
practice_counts <- data_cox %>%
group_by(practice_id) %>%
summarise(`Number_of_registered_patients` = n())
## Exclude
data_cox_stratification <- data_cox %>%
filter(practice_id %in% subset(practice_counts, Number_of_registered_patients >= 100)$practice_id)
# MODELS ----
## Cox PH model - unadjusted
mod.coxph.unadj <- coxph(Surv(follow_up_time, covid_vax) ~ 1, data = data_cox_stratification)
write_rds(mod.coxph.unadj, here::here("output", "models", "mod_coxph_unadj.rds"), compress="gz")
## Cox PH model - adjusted; baseline demographics, comorbs, geographical, flu and 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_1_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 + stp + region + rural_urban + flu_vaccine + shielded +
shielded_since_feb_15,
data = data_cox_stratification)
write_rds(mod.coxph.adj, here::here("output", "models", "mod_coxph_adj.rds"), compress="gz")
# Cox model - adjusted; baseline demographics, comorbs, geographical, flu, shielding & practice id
mod.coxph.adjb <- 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_1_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 + region + rural_urban + flu_vaccine + shielded +
shielded_since_feb_15 + strata(practice_id),
data = data_cox_stratification)
write_rds(mod.coxph.adjb, here::here("output", "models", "mod_coxph_adjb.rds"), compress="gz")
# Cox model with RE for practice - adjusted; baseline demographics, comorbs, geographical, flu, shielding & practice id
mod.coxph.adjc <- 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_1_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 + region + rural_urban + flu_vaccine + shielded +
shielded_since_feb_15 + frailty(practice_id),
data = data_cox_stratification)
write_rds(mod.coxph.adjc, here::here("output", "models", "mod_coxph_adjc.rds"), compress="gz")
# Output model coefficients ----
## Summary tables
### Cox PH model - adjusted
tab_mod1 <- gtsummary::tbl_regression(mod.coxph.adj, exp = TRUE)
gtsave(tab_mod1 %>% as_gt(), here::here("output", "models", "tab_coxph.html"))
write_csv(tab_mod1$table_body, here::here("output", "models", "tab_coxph.csv"))
### Stratified Cox PH model - adjusted
tab_mod2 <- gtsummary::tbl_regression(mod.coxph.adjb, exp = TRUE)
gtsave(tab_mod2 %>% as_gt(), here::here("output", "models", "tab_coxphb.html"))
write_csv(tab_mod2$table_body, here::here("output", "models", "tab_coxphb.csv"))
### Cox PH model with REs- adjusted
tab_mod3 <- gtsummary::tbl_regression(mod.coxph.adjc, exp = TRUE)
gtsave(tab_mod3 %>% as_gt(), here::here("output", "models", "tab_coxphc.html"))
write_csv(tab_mod3$table_body, here::here("output", "models", "tab_coxphc.csv"))
# ## Mixed effects Cox model - adjusted
# tab_mod2 <- gtsummary::tbl_regression(mod.coxme.adj, exp = TRUE)
# gtsave(tab_mod2 %>% as_gt(), here::here("output", "models", "tab_coxme.html"))
# write_csv(tab_mod2$table_body, here::here("output", "models", "tab_coxme.csv"))
## Forest plots
### Cox PH model - adjusted
plot_coxph <- ggforest(mod.coxph.adj, data = data_cox_stratification)
ggsave(
here::here("output", "models", "plot_coxph.svg"),
plot_coxph,
units = "cm", width = 20, height = 30
)
### Stratified Cox PH model - adjusted
plot_coxph <- ggforest2(mod.coxph.adjb, data = data_cox_stratification)
ggsave(
here::here("output", "models", "plot_coxphb.svg"),
plot_coxph,
units = "cm", width = 20, height = 30
)
### Cox PH model with REs - adjusted
# plot_coxph <- ggforest2(mod.coxph.adjc, data = data_cox)
# ggsave(
# here::here("output", "models", "plot_coxphc.svg"),
# plot_coxph,
# units = "cm", width = 20, height = 30
# )
### Mixed effects Cox model - adjusted
# plot_coxme <- survminer::ggforest(mod.coxme.adj, data = data_cox)
# ggsave(
# here::here("output", "models", "plot_coxph.svg"),
# plot_coxph,
# units = "cm", width = 20, height = 25
# )