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model_development.R
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model_development.R
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# Packages ----
library(haven)
library(mice)
library(cmprsk)
library(survival)
library(riskRegression)
library(prodlim)
# Set seed ----
set.seed(...)
# Data ----
...
# Descriptive analysis ----
## Participants without diabetes ----
nrow(subset(data, GTS.diabetes_all_types_1 == 0))
table(subset(data, GTS.diabetes_all_types_1 == 0)$event, exclude = NULL)
prop.table(table(subset(data, GTS.diabetes_all_types_1 == 0)$event, exclude = NULL))
summary(subset(data, GTS.diabetes_all_types_1 == 0)$time)
sd(subset(data, GTS.diabetes_all_types_1 == 0)$time, na.rm = TRUE)
summary(subset(data, GTS.diabetes_all_types_1 == 0)$max_time)
sd(subset(data, GTS.diabetes_all_types_1 == 0)$max_time, na.rm = TRUE)
table(subset(data, GTS.diabetes_all_types_1 == 0)$sex.male_1, exclude = NULL)
prop.table(table(subset(data, GTS.diabetes_all_types_1 == 0)$sex.male_1, exclude = NULL))
summary(subset(data, GTS.diabetes_all_types_1 == 0)$age)
sd(subset(data, GTS.diabetes_all_types_1 == 0)$age, na.rm = TRUE)
table(subset(data, GTS.diabetes_all_types_1 == 0)$pre_existing_ASCVD.yes_1, exclude = FALSE)
prop.table(table(subset(data, GTS.diabetes_all_types_1 == 0)$pre_existing_ASCVD.yes_1, exclude = FALSE))
summary(subset(data, GTS.diabetes_all_types_1 == 0)$SESWOA_2014)
sd(subset(data, GTS.diabetes_all_types_1 == 0)$SESWOA_2014, na.rm = TRUE)
sum(is.na(subset(data, GTS.diabetes_all_types_1 == 0)$SESWOA_2014)) / nrow(subset(data, GTS.diabetes_all_types_1 == 0))
summary(subset(data, GTS.diabetes_all_types_1 == 0)$SBP)
sd(subset(data, GTS.diabetes_all_types_1 == 0)$SBP, na.rm = TRUE)
sum(is.na(subset(data, GTS.diabetes_all_types_1 == 0)$SBP)) / nrow(subset(data, GTS.diabetes_all_types_1 == 0))
table(subset(data, GTS.diabetes_all_types_1 == 0)$treatment_current.BP_1, exclude = FALSE)
prop.table(table(subset(data, GTS.diabetes_all_types_1 == 0)$treatment_current.BP_1, exclude = FALSE))
summary(subset(data, GTS.diabetes_all_types_1 == 0)$TC)
sd(subset(data, GTS.diabetes_all_types_1 == 0)$TC, na.rm = TRUE)
sum(is.na(subset(data, GTS.diabetes_all_types_1 == 0)$TC)) / nrow(subset(data, GTS.diabetes_all_types_1 == 0))
summary(subset(data, GTS.diabetes_all_types_1 == 0)$HDL)
sd(subset(data, GTS.diabetes_all_types_1 == 0)$HDL, na.rm = TRUE)
sum(is.na(subset(data, GTS.diabetes_all_types_1 == 0)$HDL)) / nrow(subset(data, GTS.diabetes_all_types_1 == 0))
table(subset(data, GTS.diabetes_all_types_1 == 0)$treatment_current.lipids_1, exclude = FALSE)
prop.table(table(subset(data, GTS.diabetes_all_types_1 == 0)$treatment_current.lipids_1, exclude = FALSE))
table(subset(data, GTS.diabetes_all_types_1 == 0)$smoking.3_cat, exclude = FALSE)
prop.table(table(subset(data, GTS.diabetes_all_types_1 == 0)$smoking.3_cat, exclude = FALSE))
summary(subset(data, GTS.diabetes_all_types_1 == 0)$MVPA)
sd(subset(data, GTS.diabetes_all_types_1 == 0)$MVPA, na.rm = TRUE)
sum(is.na(subset(data, GTS.diabetes_all_types_1 == 0)$MVPA)) / nrow(subset(data, GTS.diabetes_all_types_1 == 0))
summary(subset(data, GTS.diabetes_all_types_1 == 0)$DHD)
sd(subset(data, GTS.diabetes_all_types_1 == 0)$DHD, na.rm = TRUE)
sum(is.na(subset(data, GTS.diabetes_all_types_1 == 0)$DHD)) / nrow(subset(data, GTS.diabetes_all_types_1 == 0))
## Participants with diabetes ----
nrow(subset(data, GTS.diabetes_all_types_1 == 1))
table(subset(data, GTS.diabetes_all_types_1 == 1)$event, exclude = NULL)
prop.table(table(subset(data, GTS.diabetes_all_types_1 == 1)$event, exclude = NULL))
summary(subset(data, GTS.diabetes_all_types_1 == 1)$time)
sd(subset(data, GTS.diabetes_all_types_1 == 1)$time, na.rm = TRUE)
summary(subset(data, GTS.diabetes_all_types_1 == 1)$max_time)
sd(subset(data, GTS.diabetes_all_types_1 == 1)$max_time, na.rm = TRUE)
table(subset(data, GTS.diabetes_all_types_1 == 1)$sex.male_1, exclude = NULL)
prop.table(table(subset(data, GTS.diabetes_all_types_1 == 1)$sex.male_1, exclude = NULL))
summary(subset(data, GTS.diabetes_all_types_1 == 1)$age)
sd(subset(data, GTS.diabetes_all_types_1 == 1)$age, na.rm = TRUE)
table(subset(data, GTS.diabetes_all_types_1 == 1)$pre_existing_ASCVD.yes_1, exclude = FALSE)
prop.table(table(subset(data, GTS.diabetes_all_types_1 == 1)$pre_existing_ASCVD.yes_1, exclude = FALSE))
summary(subset(data, GTS.diabetes_all_types_1 == 1)$SESWOA_2014)
sd(subset(data, GTS.diabetes_all_types_1 == 1)$SESWOA_2014, na.rm = TRUE)
sum(is.na(subset(data, GTS.diabetes_all_types_1 == 1)$SESWOA_2014)) / nrow(subset(data, GTS.diabetes_all_types_1 == 1))
summary(subset(data, GTS.diabetes_all_types_1 == 1)$SBP)
sd(subset(data, GTS.diabetes_all_types_1 == 1)$SBP, na.rm = TRUE)
sum(is.na(subset(data, GTS.diabetes_all_types_1 == 1)$SBP)) / nrow(subset(data, GTS.diabetes_all_types_1 == 1))
table(subset(data, GTS.diabetes_all_types_1 == 1)$treatment_current.BP_1, exclude = FALSE)
prop.table(table(subset(data, GTS.diabetes_all_types_1 == 1)$treatment_current.BP_1, exclude = FALSE))
summary(subset(data, GTS.diabetes_all_types_1 == 1)$TC)
sd(subset(data, GTS.diabetes_all_types_1 == 1)$TC, na.rm = TRUE)
sum(is.na(subset(data, GTS.diabetes_all_types_1 == 1)$TC)) / nrow(subset(data, GTS.diabetes_all_types_1 == 1))
summary(subset(data, GTS.diabetes_all_types_1 == 1)$HDL)
sd(subset(data, GTS.diabetes_all_types_1 == 1)$HDL, na.rm = TRUE)
sum(is.na(subset(data, GTS.diabetes_all_types_1 == 1)$HDL)) / nrow(subset(data, GTS.diabetes_all_types_1 == 1))
table(subset(data, GTS.diabetes_all_types_1 == 1)$treatment_current.lipids_1, exclude = FALSE)
prop.table(table(subset(data, GTS.diabetes_all_types_1 == 1)$treatment_current.lipids_1, exclude = FALSE))
table(subset(data, GTS.diabetes_all_types_1 == 1)$smoking.3_cat, exclude = FALSE)
prop.table(table(subset(data, GTS.diabetes_all_types_1 == 1)$smoking.3_cat, exclude = FALSE))
summary(subset(data, GTS.diabetes_all_types_1 == 1)$MVPA)
sd(subset(data, GTS.diabetes_all_types_1 == 1)$MVPA, na.rm = TRUE)
sum(is.na(subset(data, GTS.diabetes_all_types_1 == 1)$MVPA)) / nrow(subset(data, GTS.diabetes_all_types_1 == 1))
summary(subset(data, GTS.diabetes_all_types_1 == 1)$DHD)
sd(subset(data, GTS.diabetes_all_types_1 == 1)$DHD, na.rm = TRUE)
sum(is.na(subset(data, GTS.diabetes_all_types_1 == 1)$DHD)) / nrow(subset(data, GTS.diabetes_all_types_1 == 1))
# CI ----
CI <- cuminc(ftime = data$time,
fstatus = data$event,
cencode = 0)
CI
plot(CI)
timepoints(CI, c(5, 10))
table(subset(data, max_time > 9.5)$event)
table(subset(data, max_time > 10)$event)
CI.no_diabetes <- cuminc(ftime = subset(data, GTS.diabetes_all_types_1 == 0)$time,
fstatus = subset(data, GTS.diabetes_all_types_1 == 0)$event,
cencode = 0)
CI.no_diabetes
plot(CI.no_diabetes)
timepoints(CI.no_diabetes, c(5, 10))
table(subset(data, (GTS.diabetes_all_types_1 == 0) & (max_time > 9.5))$event)
table(subset(data, (GTS.diabetes_all_types_1 == 0) & (max_time > 10))$event)
CI.diabetes <- cuminc(ftime = subset(data, GTS.diabetes_all_types_1 == 1)$time,
fstatus = subset(data, GTS.diabetes_all_types_1 == 1)$event,
cencode = 0)
CI.diabetes
plot(CI.no_diabetes)
timepoints(CI.diabetes, c(5, 10))
table(subset(data, (GTS.diabetes_all_types_1 == 1) & (max_time > 9.5))$event)
table(subset(data, (GTS.diabetes_all_types_1 == 1) & (max_time > 10))$event)
# Standardising continuous variables ----
data$age.std <- (data$age - mean(data$age, na.rm = TRUE)) / sd(data$age, na.rm = TRUE)
data$SESWOA_2014.std <- (data$SESWOA_2014 - mean(data$SESWOA_2014, na.rm = TRUE)) / sd(data$SESWOA_2014, na.rm = TRUE)
data$SBP.std <- (data$SBP - mean(data$SBP, na.rm = TRUE)) / sd(data$SBP, na.rm = TRUE)
data$TC.std <- (data$TC - mean(data$TC, na.rm = TRUE)) / sd(data$TC, na.rm = TRUE)
data$HDL.std <- (data$HDL - mean(data$HDL, na.rm = TRUE)) / sd(data$HDL, na.rm = TRUE)
data$MVPA.std <- (data$MVPA - mean(data$MVPA, na.rm = TRUE)) / sd(data$MVPA, na.rm = TRUE)
data$DHD.std <- (data$DHD - mean(data$DHD, na.rm = TRUE)) / sd(data$DHD, na.rm = TRUE)
# Model 1 ----
# Imputations
n_imp <- 10
data <- data[, c("time",
"event",
"age.std",
"sex.male_1",
"SESWOA_2014.std",
"GTS.diabetes_all_types_1",
"pre_existing_ASCVD.yes_1",
"SBP.std",
"treatment_current.BP_1",
"TC.std",
"HDL.std",
"treatment_current.lipids_1",
"smoking.3_cat",
"MVPA.std",
"DHD.std")]
data <- sapply(data, zap_labels)
imp <- mice(data,
method = "pmm",
m = n_imp)
imp.stacked <- list()
for(i in 1:n_imp) {
imp.stacked[[i]] <- complete(imp,
i)
imp.stacked[[i]][["smoking.current_1"]] <- ifelse(imp.stacked[[i]][["smoking.3_cat"]] == 3,
1,
0)
imp.stacked[[i]][["smoking.ex_1"]] <- ifelse(imp.stacked[[i]][["smoking.3_cat"]] == 2,
1,
0)
}
# Model
fit_1.stacked <- list()
for(i in 1:n_imp) {
fit_1.stacked[[i]] <- FGR(Hist(time, event) ~
age.std +
sex.male_1 +
SESWOA_2014.std +
GTS.diabetes_all_types_1 +
pre_existing_ASCVD.yes_1 +
SBP.std +
treatment_current.BP_1 +
TC.std +
HDL.std +
treatment_current.lipids_1 +
smoking.current_1 +
smoking.ex_1 +
MVPA.std +
DHD.std,
cause = 1,
data = imp.stacked[[i]])
}
new_data <- as.data.frame(matrix(nrow = 1,
ncol = 14))
colnames(new_data) <- c("age.std",
"sex.male_1",
"SESWOA_2014.std",
"GTS.diabetes_all_types_1",
"pre_existing_ASCVD.yes_1",
"SBP.std",
"treatment_current.BP_1",
"TC.std",
"HDL.std",
"treatment_current.lipids_1",
"smoking.current_1",
"smoking.ex_1",
"MVPA.std",
"DHD.std")
new_data[1,] <- 0
fit_1.baseline.5Y <- vector()
for(i in 1:n_imp) {
fit_1.baseline.5Y <- append(fit_1.baseline.5Y, predictRisk(fit_1.stacked[[i]], new_data, times = 5))
}
fit_1.baseline.5Y <- mean(fit_1.baseline.5Y)
fit_1.baseline.5Y
fit_1.baseline.10Y <- vector()
for(i in 1:n_imp) {
fit_1.baseline.10Y <- append(fit_1.baseline.10Y, predictRisk(fit_1.stacked[[i]], new_data, times = 10))
}
fit_1.baseline.10Y <- mean(fit_1.baseline.10Y)
fit_1.baseline.10Y
fit_1.coef <- as.data.frame(matrix(nrow = 14,
ncol = (1 + n_imp)))
colnames(fit_1.coef) <- c("x",
paste0("imp_", 1:n_imp))
fit_1.coef[1, "x"] <- "age.std"
fit_1.coef[2, "x"] <- "sex.male_1"
fit_1.coef[3, "x"] <- "SESWOA_2014.std"
fit_1.coef[4, "x"] <- "GTS.diabetes_all_types_1"
fit_1.coef[5, "x"] <- "pre_existing_ASCVD.yes_1"
fit_1.coef[6, "x"] <- "SBP.std"
fit_1.coef[7, "x"] <- "treatment_current.BP_1"
fit_1.coef[8, "x"] <- "TC.std"
fit_1.coef[9, "x"] <- "HDL.std"
fit_1.coef[10, "x"] <- "treatment_current.lipids_1"
fit_1.coef[11, "x"] <- "smoking.current_1"
fit_1.coef[12, "x"] <- "smoking.ex_1"
fit_1.coef[13, "x"] <- "MVPA.std"
fit_1.coef[14, "x"] <- "DHD.std"
for(i in 1:n_imp) {
for(j in 1:14) {
fit_1.coef[j, 1+i] <- fit_1.stacked[[i]][["crrFit"]][["coef"]][[j]]
}
}
fit_1.coef$coef <- apply(fit_1.coef[, 2:(1 + n_imp)],
1,
mean)
fit_1.coef <- fit_1.coef[, c("x",
"coef")]
fit_1.coef$e_coef <- exp(fit_1.coef$coef)
fit_1.coef
fit_1.pred.5Y.fun <- function(age.std,
sex.male_1,
SESWOA_2014.std,
GTS.diabetes_all_types_1,
pre_existing_ASCVD.yes_1,
SBP.std,
treatment_current.BP_1,
TC.std,
HDL.std,
treatment_current.lipids_1,
smoking.current_1,
smoking.ex_1,
MVPA.std,
DHD.std) {
(1 - ((1 - fit_1.baseline.5Y) ** exp(subset(fit_1.coef, x == "age.std")$coef * age.std +
subset(fit_1.coef, x == "sex.male_1")$coef * sex.male_1 +
subset(fit_1.coef, x == "SESWOA_2014.std")$coef * SESWOA_2014.std +
subset(fit_1.coef, x == "GTS.diabetes_all_types_1")$coef * GTS.diabetes_all_types_1 +
subset(fit_1.coef, x == "pre_existing_ASCVD.yes_1")$coef * pre_existing_ASCVD.yes_1 +
subset(fit_1.coef, x == "SBP.std")$coef * SBP.std +
subset(fit_1.coef, x == "treatment_current.BP_1")$coef * treatment_current.BP_1 +
subset(fit_1.coef, x == "TC.std")$coef * TC.std +
subset(fit_1.coef, x == "HDL.std")$coef * HDL.std +
subset(fit_1.coef, x == "treatment_current.lipids_1")$coef * treatment_current.lipids_1 +
subset(fit_1.coef, x == "smoking.current_1")$coef * smoking.current_1 +
subset(fit_1.coef, x == "smoking.ex_1")$coef * smoking.ex_1 +
subset(fit_1.coef, x == "MVPA.std")$coef * MVPA.std +
subset(fit_1.coef, x == "DHD.std")$coef * DHD.std
)))
}
fit_1.pred.10Y.fun <- function(age.std,
sex.male_1,
SESWOA_2014.std,
GTS.diabetes_all_types_1,
pre_existing_ASCVD.yes_1,
SBP.std,
treatment_current.BP_1,
TC.std,
HDL.std,
treatment_current.lipids_1,
smoking.current_1,
smoking.ex_1,
MVPA.std,
DHD.std) {
(1 - ((1 - fit_1.baseline.10Y) ** exp(subset(fit_1.coef, x == "age.std")$coef * age.std +
subset(fit_1.coef, x == "sex.male_1")$coef * sex.male_1 +
subset(fit_1.coef, x == "SESWOA_2014.std")$coef * SESWOA_2014.std +
subset(fit_1.coef, x == "GTS.diabetes_all_types_1")$coef * GTS.diabetes_all_types_1 +
subset(fit_1.coef, x == "pre_existing_ASCVD.yes_1")$coef * pre_existing_ASCVD.yes_1 +
subset(fit_1.coef, x == "SBP.std")$coef * SBP.std +
subset(fit_1.coef, x == "treatment_current.BP_1")$coef * treatment_current.BP_1 +
subset(fit_1.coef, x == "TC.std")$coef * TC.std +
subset(fit_1.coef, x == "HDL.std")$coef * HDL.std +
subset(fit_1.coef, x == "treatment_current.lipids_1")$coef * treatment_current.lipids_1 +
subset(fit_1.coef, x == "smoking.current_1")$coef * smoking.current_1 +
subset(fit_1.coef, x == "smoking.ex_1")$coef * smoking.ex_1 +
subset(fit_1.coef, x == "MVPA.std")$coef * MVPA.std +
subset(fit_1.coef, x == "DHD.std")$coef * DHD.std
)))
}
# AUC
fit_1.AUC.5Y <- as.data.frame(matrix(nrow = 1,
ncol = n_imp))
colnames(fit_1.AUC.5Y) <- c(paste0("imp_", 1:n_imp))
for(i in 1:n_imp) {
fit_1.AUC.5Y[1, i] <- Score(list(fit_1.stacked[[1]]),
formula = Hist(time, event) ~ 1,
data = imp.stacked[[i]],
metrics = "auc",
times = 5,
se.fit = FALSE)[["AUC"]][["score"]][["AUC"]]
}
fit_1.AUC.5Y <- apply(fit_1.AUC.5Y,
1,
mean)
fit_1.AUC.5Y
fit_1.AUC.10Y <- as.data.frame(matrix(nrow = 1,
ncol = n_imp))
colnames(fit_1.AUC.10Y) <- c(paste0("imp_", 1:n_imp))
for(i in 1:n_imp) {
fit_1.AUC.10Y[1, i] <- Score(list(fit_1.stacked[[1]]),
formula = Hist(time, event) ~ 1,
data = imp.stacked[[i]],
metrics = "auc",
times = 10,
se.fit = FALSE)[["AUC"]][["score"]][["AUC"]]
}
fit_1.AUC.10Y <- apply(fit_1.AUC.10Y,
1,
mean)
fit_1.AUC.10Y
# Calibration
for(i in 1:n_imp) {
imp.stacked[[i]][["fit_1.pred.5Y"]] <- fit_1.pred.5Y.fun(age.std = imp.stacked[[i]][["age.std"]],
sex.male_1 = imp.stacked[[i]][["sex.male_1"]],
SESWOA_2014.std = imp.stacked[[i]][["SESWOA_2014.std"]],
GTS.diabetes_all_types_1 = imp.stacked[[i]][["GTS.diabetes_all_types_1"]],
pre_existing_ASCVD.yes_1 = imp.stacked[[i]][["pre_existing_ASCVD.yes_1"]],
SBP.std = imp.stacked[[i]][["SBP.std"]],
treatment_current.BP_1 = imp.stacked[[i]][["treatment_current.BP_1"]],
TC.std = imp.stacked[[i]][["TC.std"]],
HDL.std = imp.stacked[[i]][["HDL.std"]],
treatment_current.lipids_1 = imp.stacked[[i]][["treatment_current.lipids_1"]],
smoking.current_1 = imp.stacked[[i]][["smoking.current_1"]],
smoking.ex_1 = imp.stacked[[i]][["smoking.ex_1"]],
MVPA.std = imp.stacked[[i]][["MVPA.std"]],
DHD.std = imp.stacked[[i]][["DHD.std"]])
}
fit_1.CAL.5Y.x <- list()
fit_1.CAL.5Y.y <- list()
for(i in 1:n_imp) {
imp.stacked[[i]][["fit_1.pred.5Y.decile"]] <- cut(imp.stacked[[i]][["fit_1.pred.5Y"]],
breaks = quantile(imp.stacked[[i]][["fit_1.pred.5Y"]], prob = seq(0, 1, 0.1)),
labels = c(1:10),
include.lowest = TRUE)
temp.fit_1.CAL.5Y.x <- aggregate(imp.stacked[[i]][["fit_1.pred.5Y"]],
list(imp.stacked[[i]][["fit_1.pred.5Y.decile"]]),
mean)
colnames(temp.fit_1.CAL.5Y.x) <- c("fit_1.pred.5Y.decile",
"fit_1.pred.5Y.mean")
fit_1.CAL.5Y.x <- append(fit_1.CAL.5Y.x, list(temp.fit_1.CAL.5Y.x))
temp.fit_1.CAL.5Y.y <- as.data.frame(matrix(nrow = 10,
ncol = 2))
colnames(temp.fit_1.CAL.5Y.y) <- c("fit_1.pred.5Y.decile",
"CI.ASCVD_composite_event.5Y")
temp.fit_1.CAL.5Y.y$fit_1.pred.5Y.decile <- 1:10
for(d in 1:10) {
temp.CI <- cuminc(ftime = subset(imp.stacked[[i]], fit_1.pred.5Y.decile == d)$time,
fstatus = subset(imp.stacked[[i]], fit_1.pred.5Y.decile == d)$event,
cencode = 0)
temp.fit_1.CAL.5Y.y[d, "CI.ASCVD_composite_event.5Y"] <- timepoints(temp.CI, 5)[[1]][[1]]
}
fit_1.CAL.5Y.y <- append(fit_1.CAL.5Y.y, list(temp.fit_1.CAL.5Y.y))
}
fit_1.CAL.5Y <- as.data.frame(matrix(nrow = 10,
ncol = 3))
colnames(fit_1.CAL.5Y) <- c("fit_1.pred.5Y.decile",
"fit_1.pred.5Y.mean.mean",
"ASCVD_composite_event.mean.mean")
fit_1.CAL.5Y$fit_1.pred.5Y.decile <- 1:10
temp <- vector()
for(d in 1:10) {
for(i in 1:n_imp){
temp <- append(temp, fit_1.CAL.5Y.x[[i]][[2]][[d]])
}
fit_1.CAL.5Y[d, "fit_1.pred.5Y.mean.mean"] <- mean(temp)
}
temp <- vector()
for(d in 1:10) {
for(i in 1:n_imp) {
temp <- append(temp, fit_1.CAL.5Y.y[[i]][[2]][[d]])
}
fit_1.CAL.5Y[d, "ASCVD_composite_event.mean.mean"] <- mean(temp)
}
fit_1.CAL.5Y
for(i in 1:n_imp) {
imp.stacked[[i]][["fit_1.pred.10Y"]] <- fit_1.pred.10Y.fun(age.std = imp.stacked[[i]][["age.std"]],
sex.male_1 = imp.stacked[[i]][["sex.male_1"]],
SESWOA_2014.std = imp.stacked[[i]][["SESWOA_2014.std"]],
GTS.diabetes_all_types_1 = imp.stacked[[i]][["GTS.diabetes_all_types_1"]],
pre_existing_ASCVD.yes_1 = imp.stacked[[i]][["pre_existing_ASCVD.yes_1"]],
SBP.std = imp.stacked[[i]][["SBP.std"]],
treatment_current.BP_1 = imp.stacked[[i]][["treatment_current.BP_1"]],
TC.std = imp.stacked[[i]][["TC.std"]],
HDL.std = imp.stacked[[i]][["HDL.std"]],
treatment_current.lipids_1 = imp.stacked[[i]][["treatment_current.lipids_1"]],
smoking.current_1 = imp.stacked[[i]][["smoking.current_1"]],
smoking.ex_1 = imp.stacked[[i]][["smoking.ex_1"]],
MVPA.std = imp.stacked[[i]][["MVPA.std"]],
DHD.std = imp.stacked[[i]][["DHD.std"]])
}
fit_1.CAL.10Y.x <- list()
fit_1.CAL.10Y.y <- list()
for(i in 1:n_imp) {
imp.stacked[[i]][["fit_1.pred.10Y.decile"]] <- cut(imp.stacked[[i]][["fit_1.pred.10Y"]],
breaks = quantile(imp.stacked[[i]][["fit_1.pred.10Y"]], prob = seq(0, 1, 0.1)),
labels = c(1:10),
include.lowest = TRUE)
temp.fit_1.CAL.10Y.x <- aggregate(imp.stacked[[i]][["fit_1.pred.10Y"]],
list(imp.stacked[[i]][["fit_1.pred.10Y.decile"]]),
mean)
colnames(temp.fit_1.CAL.10Y.x) <- c("fit_1.pred.10Y.decile",
"fit_1.pred.10Y.mean")
fit_1.CAL.10Y.x <- append(fit_1.CAL.10Y.x, list(temp.fit_1.CAL.10Y.x))
temp.fit_1.CAL.10Y.y <- as.data.frame(matrix(nrow = 10,
ncol = 2))
colnames(temp.fit_1.CAL.10Y.y) <- c("fit_1.pred.10Y.decile",
"CI.ASCVD_composite_event.10Y")
temp.fit_1.CAL.10Y.y$fit_1.pred.10Y.decile <- 1:10
for(d in 1:10) {
temp.CI <- cuminc(ftime = subset(imp.stacked[[i]], fit_1.pred.10Y.decile == d)$time,
fstatus = subset(imp.stacked[[i]], fit_1.pred.10Y.decile == d)$event,
cencode = 0)
temp.fit_1.CAL.10Y.y[d, "CI.ASCVD_composite_event.10Y"] <- timepoints(temp.CI, 10)[[1]][[1]]
}
fit_1.CAL.10Y.y <- append(fit_1.CAL.10Y.y, list(temp.fit_1.CAL.10Y.y))
}
fit_1.CAL.10Y <- as.data.frame(matrix(nrow = 10,
ncol = 3))
colnames(fit_1.CAL.10Y) <- c("fit_1.pred.10Y.decile",
"fit_1.pred.10Y.mean.mean",
"CI.ASCVD_composite_event.10Y.mean")
fit_1.CAL.10Y$fit_1.pred.10Y.decile <- 1:10
temp <- vector()
for(d in 1:10) {
for(i in 1:n_imp) {
temp <- append(temp, fit_1.CAL.10Y.x[[i]][[2]][[d]])
}
fit_1.CAL.10Y[d, "fit_1.pred.10Y.mean.mean"] <- mean(temp)
}
temp <- vector()
for(d in 1:10) {
for(i in 1:n_imp) {
temp <- append(temp, fit_1.CAL.10Y.y[[i]][[2]][[d]])
}
fit_1.CAL.10Y[d, "CI.ASCVD_composite_event.10Y.mean"] <- mean(temp)
}
fit_1.CAL.10Y
## Bootstrap ----
n_b <- 100
# Object to capture output model coefficients
boot.fit_1.coef <- as.data.frame(matrix(nrow = 14,
ncol = 1 + n_b))
colnames(boot.fit_1.coef) <- c("x",
paste0("boot_", 1:n_b, ".coef"))
boot.fit_1.coef[1, "x"] <- "age.std"
boot.fit_1.coef[2, "x"] <- "sex.male_1"
boot.fit_1.coef[3, "x"] <- "SESWOA_2014.std"
boot.fit_1.coef[4, "x"] <- "GTS.diabetes_all_types_1"
boot.fit_1.coef[5, "x"] <- "pre_existing_ASCVD.yes_1"
boot.fit_1.coef[6, "x"] <- "SBP.std"
boot.fit_1.coef[7, "x"] <- "treatment_current.BP_1"
boot.fit_1.coef[8, "x"] <- "TC.std"
boot.fit_1.coef[9, "x"] <- "HDL.std"
boot.fit_1.coef[10, "x"] <- "treatment_current.lipids_1"
boot.fit_1.coef[11, "x"] <- "smoking.current_1"
boot.fit_1.coef[12, "x"] <- "smoking.ex_1"
boot.fit_1.coef[13, "x"] <- "MVPA.std"
boot.fit_1.coef[14, "x"] <- "DHD.std"
# Objects to capture output AUC
boot.fit_1.AUC.5Y <- as.data.frame(matrix(nrow = 1,
ncol = n_b))
colnames(boot.fit_1.AUC.5Y ) <- c(paste0("boot_", 1:n_b, ".AUC.5Y"))
boot.fit_1.AUC.10Y <- as.data.frame(matrix(nrow = 1,
ncol = n_b))
colnames(boot.fit_1.AUC.10Y) <- c(paste0("boot_", 1:n_b, ".AUC.10Y"))
# Objects to capture output calibration
boot.fit_1.CAL.5Y <- as.data.frame(matrix(nrow = 0,
ncol = 3))
colnames(boot.fit_1.CAL.5Y) <- c("boot",
"boot.fit_1.pred.5Y.decile",
"boot.fit_1.pred.5Y.mean.mean")
# Loop
for(b in 1:n_b) {
boot.index <- sample(1:nrow(data), size = nrow(data), replace = TRUE)
boot.data <- data[boot.index, ]
# Imputations
boot.imp <- mice(boot.data,
method = "pmm",
m = n_imp,
print = FALSE)
boot.imp.stacked <- list()
for(i in 1:n_imp) {
boot.imp.stacked[[i]] <- complete(boot.imp,
i)
boot.imp.stacked[[i]][["smoking.current_1"]] <- ifelse(boot.imp.stacked[[i]][["smoking.3_cat"]] == 3,
1,
0)
boot.imp.stacked[[i]][["smoking.ex_1"]] <- ifelse(boot.imp.stacked[[i]][["smoking.3_cat"]] == 2,
1,
0)
}
# Model
boot.fit_1.stacked <- list()
for(i in 1:n_imp) {
boot.fit_1.stacked[[i]] <- FGR(Hist(time, event) ~
age.std +
sex.male_1 +
SESWOA_2014.std +
GTS.diabetes_all_types_1 +
pre_existing_ASCVD.yes_1 +
SBP.std +
treatment_current.BP_1 +
TC.std +
HDL.std +
treatment_current.lipids_1 +
smoking.current_1 +
smoking.ex_1 +
MVPA.std +
DHD.std,
cause = 1,
data = boot.imp.stacked[[i]])
}
boot.fit_1.baseline.5Y <- vector()
for(i in 1:n_imp) {
boot.fit_1.baseline.5Y <- append(boot.fit_1.baseline.5Y, predictRisk(boot.fit_1.stacked[[i]], new_data, times = 5))
}
boot.fit_1.baseline.5Y <- mean(boot.fit_1.baseline.5Y)
boot.fit_1.baseline.5Y
temp.boot.fit_1.coef <- as.data.frame(matrix(nrow = 14,
ncol = (1 + n_imp)))
colnames(temp.boot.fit_1.coef) <- c("x",
paste0("imp_", 1:n_imp))
temp.boot.fit_1.coef[1, "x"] <- "age.std"
temp.boot.fit_1.coef[2, "x"] <- "sex.male_1"
temp.boot.fit_1.coef[3, "x"] <- "SESWOA_2014.std"
temp.boot.fit_1.coef[4, "x"] <- "GTS.diabetes_all_types_1"
temp.boot.fit_1.coef[5, "x"] <- "pre_existing_ASCVD.yes_1"
temp.boot.fit_1.coef[6, "x"] <- "SBP.std"
temp.boot.fit_1.coef[7, "x"] <- "treatment_current.BP_1"
temp.boot.fit_1.coef[8, "x"] <- "TC.std"
temp.boot.fit_1.coef[9, "x"] <- "HDL.std"
temp.boot.fit_1.coef[10, "x"] <- "treatment_current.lipids_1"
temp.boot.fit_1.coef[11, "x"] <- "smoking.current_1"
temp.boot.fit_1.coef[12, "x"] <- "smoking.ex_1"
temp.boot.fit_1.coef[13, "x"] <- "MVPA.std"
temp.boot.fit_1.coef[14, "x"] <- "DHD.std"
for(i in 1:n_imp) {
for(j in 1:14) {
temp.boot.fit_1.coef[j, 1+i] <- boot.fit_1.stacked[[i]][["crrFit"]][["coef"]][[j]]
}
}
temp.boot.fit_1.coef$coef <- apply(temp.boot.fit_1.coef[, 2:(1 + n_imp)],
1,
mean)
boot.fit_1.coef[, 1 + b] <- temp.boot.fit_1.coef$coef
boot.fit_1.pred.5Y.fun <- function(age.std,
sex.male_1,
SESWOA_2014.std,
GTS.diabetes_all_types_1,
pre_existing_ASCVD.yes_1,
SBP.std,
treatment_current.BP_1,
TC.std,
HDL.std,
treatment_current.lipids_1,
smoking.current_1,
smoking.ex_1,
MVPA.std,
DHD.std) {
(1 - ((1 - boot.fit_1.baseline.5Y) ** exp(subset(boot.fit_1.coef, x == "age.std")[, 1 + b] * age.std +
subset(boot.fit_1.coef, x == "sex.male_1")[, 1 + b] * sex.male_1 +
subset(boot.fit_1.coef, x == "SESWOA_2014.std")[, 1 + b] * SESWOA_2014.std +
subset(boot.fit_1.coef, x == "GTS.diabetes_all_types_1")[, 1 + b] * GTS.diabetes_all_types_1 +
subset(boot.fit_1.coef, x == "pre_existing_ASCVD.yes_1")[, 1 + b] * pre_existing_ASCVD.yes_1 +
subset(boot.fit_1.coef, x == "SBP.std")[, 1 + b] * SBP.std +
subset(boot.fit_1.coef, x == "treatment_current.BP_1")[, 1 + b] * treatment_current.BP_1 +
subset(boot.fit_1.coef, x == "TC.std")[, 1 + b] * TC.std +
subset(boot.fit_1.coef, x == "HDL.std")[, 1 + b] * HDL.std +
subset(boot.fit_1.coef, x == "treatment_current.lipids_1")[, 1 + b] * treatment_current.lipids_1 +
subset(boot.fit_1.coef, x == "smoking.current_1")[, 1 + b] * smoking.current_1 +
subset(boot.fit_1.coef, x == "smoking.ex_1")[, 1 + b] * smoking.ex_1 +
subset(boot.fit_1.coef, x == "MVPA.std")[, 1 + b] * MVPA.std +
subset(boot.fit_1.coef, x == "DHD.std")[, 1 + b] * DHD.std
)))
}
# AUC
temp.boot.fit_1.AUC.5Y <- as.data.frame(matrix(nrow = 1,
ncol = n_imp))
colnames(temp.boot.fit_1.AUC.5Y) <- c(paste0("imp_", 1:n_imp))
for(i in 1:n_imp) {
temp.boot.fit_1.AUC.5Y[1, i] <- Score(list(boot.fit_1.stacked[[i]]),
formula = Hist(time, event) ~ 1,
data = boot.imp.stacked[[i]],
metrics = "auc",
times = 5,
se.fit = FALSE)[["AUC"]][["score"]][["AUC"]]
}
boot.fit_1.AUC.5Y[ ,b] <- apply(temp.boot.fit_1.AUC.5Y,
1,
mean)
temp.boot.fit_1.AUC.10Y <- as.data.frame(matrix(nrow = 1,
ncol = n_imp))
colnames(temp.boot.fit_1.AUC.10Y) <- c(paste0("imp_", 1:n_imp))
for(i in 1:n_imp) {
temp.boot.fit_1.AUC.10Y[1, i] <- Score(list(boot.fit_1.stacked[[i]]),
formula = Hist(time, event) ~ 1,
data = boot.imp.stacked[[i]],
metrics = "auc",
times = 10,
se.fit = FALSE)[["AUC"]][["score"]][["AUC"]]
}
boot.fit_1.AUC.10Y[ ,b ] <- apply(temp.boot.fit_1.AUC.10Y,
1,
mean)
# Calibration
for(i in 1:n_imp) {
boot.imp.stacked[[i]][["boot.fit_1.pred.5Y"]] <- boot.fit_1.pred.5Y.fun(age.std = boot.imp.stacked[[i]][["age.std"]],
sex.male_1 = boot.imp.stacked[[i]][["sex.male_1"]],
SESWOA_2014.std = boot.imp.stacked[[i]][["SESWOA_2014.std"]],
GTS.diabetes_all_types_1 = boot.imp.stacked[[i]][["GTS.diabetes_all_types_1"]],
pre_existing_ASCVD.yes_1 = boot.imp.stacked[[i]][["pre_existing_ASCVD.yes_1"]],
SBP.std = boot.imp.stacked[[i]][["SBP.std"]],
treatment_current.BP_1 = boot.imp.stacked[[i]][["treatment_current.BP_1"]],
TC.std = boot.imp.stacked[[i]][["TC.std"]],
HDL.std = boot.imp.stacked[[i]][["HDL.std"]],
treatment_current.lipids_1 = boot.imp.stacked[[i]][["treatment_current.lipids_1"]],
smoking.current_1 = boot.imp.stacked[[i]][["smoking.current_1"]],
smoking.ex_1 = boot.imp.stacked[[i]][["smoking.ex_1"]],
MVPA.std = boot.imp.stacked[[i]][["MVPA.std"]],
DHD.std = boot.imp.stacked[[i]][["DHD.std"]])
}
boot.fit_1.CAL.5Y.x <- list()
boot.fit_1.CAL.5Y.y <- list()
for(i in 1:n_imp) {
boot.imp.stacked[[i]][["boot.fit_1.pred.5Y.decile"]] <- cut(boot.imp.stacked[[i]][["boot.fit_1.pred.5Y"]],
breaks = quantile(boot.imp.stacked[[i]][["boot.fit_1.pred.5Y"]], prob = seq(0, 1, 0.1)),
labels = c(1:10),
include.lowest = TRUE)
boot.temp.fit_1.CAL.5Y.x <- aggregate(boot.imp.stacked[[i]][["boot.fit_1.pred.5Y"]],
list(boot.imp.stacked[[i]][["boot.fit_1.pred.5Y.decile"]]),
mean)
colnames(boot.temp.fit_1.CAL.5Y.x) <- c("boot.fit_1.pred.5Y.decile",
"boot.fit_1.pred.5Y.mean")
boot.fit_1.CAL.5Y.x <- append(boot.fit_1.CAL.5Y.x, list(boot.temp.fit_1.CAL.5Y.x))
}
temp.boot.fit_1.CAL.5Y <- as.data.frame(matrix(nrow = 10,
ncol = 2))
colnames(temp.boot.fit_1.CAL.5Y) <- c("boot.fit_1.pred.5Y.decile",
"boot.fit_1.pred.5Y.mean.mean")
temp.boot.fit_1.CAL.5Y$boot.fit_1.pred.5Y.decile <- 1:10
boot.temp <- vector()
for(d in 1:10) {
for(i in 1:n_imp) {
boot.temp <- append(boot.temp, boot.fit_1.CAL.5Y.x[[i]][[2]][[d]])
}
temp.boot.fit_1.CAL.5Y[d, "boot.fit_1.pred.5Y.mean.mean"] <- mean(boot.temp)
}
temp.boot.fit_1.CAL.5Y$boot <- b
temp.boot.fit_1.CAL.5Y <- temp.boot.fit_1.CAL.5Y[, c("boot",
"boot.fit_1.pred.5Y.decile",
"boot.fit_1.pred.5Y.mean.mean")]
boot.fit_1.CAL.5Y <- rbind(boot.fit_1.CAL.5Y, temp.boot.fit_1.CAL.5Y)
}
# 95% CI fit_1 coefficients
quantile(t(subset(boot.fit_1.coef, x =="age.std")[, 2:ncol(boot.fit_1.coef)]), c(0.025, 0.975))
quantile(t(subset(boot.fit_1.coef, x =="sex.male_1")[, 2:ncol(boot.fit_1.coef)]), c(0.025, 0.975))
quantile(t(subset(boot.fit_1.coef, x =="SESWOA_2014.std")[, 2:ncol(boot.fit_1.coef)]), c(0.025, 0.975))
quantile(t(subset(boot.fit_1.coef, x =="GTS.diabetes_all_types_1")[, 2:ncol(boot.fit_1.coef)]), c(0.025, 0.975))
quantile(t(subset(boot.fit_1.coef, x =="pre_existing_ASCVD.yes_1")[, 2:ncol(boot.fit_1.coef)]), c(0.025, 0.975))
quantile(t(subset(boot.fit_1.coef, x =="SBP.std")[, 2:ncol(boot.fit_1.coef)]), c(0.025, 0.975))
quantile(t(subset(boot.fit_1.coef, x =="treatment_current.BP_1")[, 2:ncol(boot.fit_1.coef)]), c(0.025, 0.975))
quantile(t(subset(boot.fit_1.coef, x =="TC.std")[, 2:ncol(boot.fit_1.coef)]), c(0.025, 0.975))
quantile(t(subset(boot.fit_1.coef, x =="HDL.std")[, 2:ncol(boot.fit_1.coef)]), c(0.025, 0.975))
quantile(t(subset(boot.fit_1.coef, x =="treatment_current.lipids_1")[, 2:ncol(boot.fit_1.coef)]), c(0.025, 0.975))
quantile(t(subset(boot.fit_1.coef, x =="smoking.current_1")[, 2:ncol(boot.fit_1.coef)]), c(0.025, 0.975))
quantile(t(subset(boot.fit_1.coef, x =="smoking.ex_1")[, 2:ncol(boot.fit_1.coef)]), c(0.025, 0.975))
quantile(t(subset(boot.fit_1.coef, x =="MVPA.std")[, 2:ncol(boot.fit_1.coef)]), c(0.025, 0.975))
quantile(t(subset(boot.fit_1.coef, x =="DHD.std")[, 2:ncol(boot.fit_1.coef)]), c(0.025, 0.975))
exp(quantile(t(subset(boot.fit_1.coef, x =="age.std")[, 2:ncol(boot.fit_1.coef)]), c(0.025, 0.975)))
exp(quantile(t(subset(boot.fit_1.coef, x =="sex.male_1")[, 2:ncol(boot.fit_1.coef)]), c(0.025, 0.975)))
exp(quantile(t(subset(boot.fit_1.coef, x =="SESWOA_2014.std")[, 2:ncol(boot.fit_1.coef)]), c(0.025, 0.975)))
exp(quantile(t(subset(boot.fit_1.coef, x =="GTS.diabetes_all_types_1")[, 2:ncol(boot.fit_1.coef)]), c(0.025, 0.975)))
exp(quantile(t(subset(boot.fit_1.coef, x =="pre_existing_ASCVD.yes_1")[, 2:ncol(boot.fit_1.coef)]), c(0.025, 0.975)))
exp(quantile(t(subset(boot.fit_1.coef, x =="SBP.std")[, 2:ncol(boot.fit_1.coef)]), c(0.025, 0.975)))
exp(quantile(t(subset(boot.fit_1.coef, x =="treatment_current.BP_1")[, 2:ncol(boot.fit_1.coef)]), c(0.025, 0.975)))
exp(quantile(t(subset(boot.fit_1.coef, x =="TC.std")[, 2:ncol(boot.fit_1.coef)]), c(0.025, 0.975)))
exp(quantile(t(subset(boot.fit_1.coef, x =="HDL.std")[, 2:ncol(boot.fit_1.coef)]), c(0.025, 0.975)))
exp(quantile(t(subset(boot.fit_1.coef, x =="treatment_current.lipids_1")[, 2:ncol(boot.fit_1.coef)]), c(0.025, 0.975)))
exp(quantile(t(subset(boot.fit_1.coef, x =="smoking.current_1")[, 2:ncol(boot.fit_1.coef)]), c(0.025, 0.975)))
exp(quantile(t(subset(boot.fit_1.coef, x =="smoking.ex_1")[, 2:ncol(boot.fit_1.coef)]), c(0.025, 0.975)))
exp(quantile(t(subset(boot.fit_1.coef, x =="MVPA.std")[, 2:ncol(boot.fit_1.coef)]), c(0.025, 0.975)))
exp(quantile(t(subset(boot.fit_1.coef, x =="DHD.std")[, 2:ncol(boot.fit_1.coef)]), c(0.025, 0.975)))
# 95% CI AUC
quantile(t(boot.fit_1.AUC.5Y), c(0.025, 0.975))
quantile(t(boot.fit_1.AUC.10Y), c(0.025, 0.975))
# 95% CI calibration
for(d in 1:10) {
print(d)
print(quantile(subset(boot.fit_1.CAL.5Y, boot.fit_1.pred.5Y.decile == d)$boot.fit_1.pred.5Y.mean.mean, c(0.025, 0.975)))
}
# Model 2 ----
# Model
fit_2.stacked <- list()
for(i in 1:n_imp) {
fit_2.stacked[[i]] <- FGR(Hist(time, event) ~
age.std +
sex.male_1 +
SESWOA_2014.std +
GTS.diabetes_all_types_1 +
pre_existing_ASCVD.yes_1 +
smoking.current_1 +
smoking.ex_1 +
MVPA.std +
DHD.std,
cause = 1,
data = imp.stacked[[i]])
}
fit_2.baseline.5Y <- vector()
for(i in 1:n_imp) {
fit_2.baseline.5Y <- append(fit_2.baseline.5Y, predictRisk(fit_2.stacked[[i]], new_data, times = 5))
}
fit_2.baseline.5Y <- mean(fit_2.baseline.5Y)
fit_2.baseline.5Y
fit_2.baseline.10Y <- vector()
for(i in 1:n_imp) {
fit_2.baseline.10Y <- append(fit_2.baseline.10Y, predictRisk(fit_2.stacked[[i]], new_data, times = 10))
}
fit_2.baseline.10Y <- mean(fit_2.baseline.10Y)
fit_2.baseline.10Y
fit_2.coef <- as.data.frame(matrix(nrow = 9,
ncol = (1 + n_imp)))
colnames(fit_2.coef) <- c("x",
paste0("imp_", 1:n_imp))
fit_2.coef[1, "x"] <- "age.std"
fit_2.coef[2, "x"] <- "sex.male_1"
fit_2.coef[3, "x"] <- "SESWOA_2014.std"
fit_2.coef[4, "x"] <- "GTS.diabetes_all_types_1"
fit_2.coef[5, "x"] <- "pre_existing_ASCVD.yes_1"
fit_2.coef[6, "x"] <- "smoking.current_1"
fit_2.coef[7, "x"] <- "smoking.ex_1"
fit_2.coef[8, "x"] <- "MVPA.std"
fit_2.coef[9, "x"] <- "DHD.std"
for(i in 1:n_imp) {
for (j in 1:9) {
fit_2.coef[j, 1+i] <- fit_2.stacked[[i]][["crrFit"]][["coef"]][[j]]
}
}
fit_2.coef$coef <- apply(fit_2.coef[, 2:(1 + n_imp)],
1,
mean)
fit_2.coef <- fit_2.coef[, c("x",
"coef")]
fit_2.coef$e_coef <- exp(fit_2.coef$coef)
fit_2.coef
fit_2.pred.5Y.fun <- function(age.std,
sex.male_1,
SESWOA_2014.std,
GTS.diabetes_all_types_1,
pre_existing_ASCVD.yes_1,
smoking.current_1,
smoking.ex_1,
MVPA.std,
DHD.std) {
(1 - ((1 - fit_2.baseline.5Y) ** exp(subset(fit_2.coef, x == "age.std")$coef * age.std +
subset(fit_2.coef, x == "sex.male_1")$coef * sex.male_1 +
subset(fit_2.coef, x == "SESWOA_2014.std")$coef * SESWOA_2014.std +
subset(fit_2.coef, x == "GTS.diabetes_all_types_1")$coef * GTS.diabetes_all_types_1 +
subset(fit_2.coef, x == "pre_existing_ASCVD.yes_1")$coef * pre_existing_ASCVD.yes_1 +
subset(fit_2.coef, x == "smoking.current_1")$coef * smoking.current_1 +
subset(fit_2.coef, x == "smoking.ex_1")$coef * smoking.ex_1 +
subset(fit_2.coef, x == "MVPA.std")$coef * MVPA.std +
subset(fit_2.coef, x == "DHD.std")$coef * DHD.std
)))
}
fit_2.pred.10Y.fun <- function(age.std,
sex.male_1,
SESWOA_2014.std,
GTS.diabetes_all_types_1,
pre_existing_ASCVD.yes_1,
smoking.current_1,
smoking.ex_1,
MVPA.std,
DHD.std) {
(1 - ((1 - fit_2.baseline.10Y) ** exp(subset(fit_2.coef, x == "age.std")$coef * age.std +
subset(fit_2.coef, x == "sex.male_1")$coef * sex.male_1 +
subset(fit_2.coef, x == "SESWOA_2014.std")$coef * SESWOA_2014.std +
subset(fit_2.coef, x == "GTS.diabetes_all_types_1")$coef * GTS.diabetes_all_types_1 +
subset(fit_2.coef, x == "pre_existing_ASCVD.yes_1")$coef * pre_existing_ASCVD.yes_1 +
subset(fit_2.coef, x == "smoking.current_1")$coef * smoking.current_1 +
subset(fit_2.coef, x == "smoking.ex_1")$coef * smoking.ex_1 +
subset(fit_2.coef, x == "MVPA.std")$coef * MVPA.std +
subset(fit_2.coef, x == "DHD.std")$coef * DHD.std
)))
}
# AUC
fit_2.AUC.5Y <- as.data.frame(matrix(nrow = 1,
ncol = n_imp))
colnames(fit_2.AUC.5Y) <- c(paste0("imp_", 1:n_imp))
for(i in 1:n_imp) {
fit_2.AUC.5Y[1, i] <- Score(list(fit_2.stacked[[1]]),
formula = Hist(time, event) ~ 1,
data = imp.stacked[[i]],
metrics = "auc",
times = 5,
se.fit = FALSE)[["AUC"]][["score"]][["AUC"]]
}
fit_2.AUC.5Y <- apply(fit_2.AUC.5Y,
1,
mean)
fit_2.AUC.5Y
fit_2.AUC.10Y <- as.data.frame(matrix(nrow = 1,
ncol = n_imp))
colnames(fit_2.AUC.10Y) <- c(paste0("imp_", 1:n_imp))
for(i in 1:n_imp) {
fit_2.AUC.10Y[1, i] <- Score(list(fit_2.stacked[[1]]),
formula = Hist(time, event) ~ 1,
data = imp.stacked[[i]],
metrics = "auc",
times = 10,
se.fit = FALSE)[["AUC"]][["score"]][["AUC"]]
}
fit_2.AUC.10Y <- apply(fit_2.AUC.10Y,
1,
mean)
fit_2.AUC.10Y
# Calibration
for(i in 1:n_imp) {
imp.stacked[[i]][["fit_2.pred.5Y"]] <- fit_2.pred.5Y.fun(age.std = imp.stacked[[i]][["age.std"]],
sex.male_1 = imp.stacked[[i]][["sex.male_1"]],
SESWOA_2014.std = imp.stacked[[i]][["SESWOA_2014.std"]],
GTS.diabetes_all_types_1 = imp.stacked[[i]][["GTS.diabetes_all_types_1"]],
pre_existing_ASCVD.yes_1 = imp.stacked[[i]][["pre_existing_ASCVD.yes_1"]],
smoking.current_1 = imp.stacked[[i]][["smoking.current_1"]],
smoking.ex_1 = imp.stacked[[i]][["smoking.ex_1"]],