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script.R
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# Packages ----
library(haven)
library(dagitty)
library(mice)
library(lavaan)
library(survival)
library(riskRegression)
library(prodlim)
library(rms)
library(ggplot2)
# Data ----
...
# Subsets ----
## Participants without diabetes ----
data.no_diabetes <- subset(data, GTS.diabetes_all_types_1 == 0)
nrow(data.no_diabetes)
## Participants with diabetes ----
data.diabetes <- subset(data, GTS.diabetes_all_types_1 == 1)
nrow(data.diabetes)
# Descriptive analysis ----
## Participants without diabetes ----
table(data.no_diabetes$event, exclude = NULL)
prop.table(table(data.no_diabetes$event, exclude = NULL))
summary(data.no_diabetes$time)
sd(data.no_diabetes$time, na.rm = TRUE)
summary(data.no_diabetes$max_time)
sd(data.no_diabetes$max_time, na.rm = TRUE)
table(data.no_diabetes$sex.male_1, exclude = NULL)
prop.table(table(data.no_diabetes$sex.male_1, exclude = NULL))
summary(data.no_diabetes$age)
sd(data.no_diabetes$age, na.rm = TRUE)
table(data.no_diabetes$pre_existing_ASCVD.yes_1, exclude = FALSE)
prop.table(table(data.no_diabetes$pre_existing_ASCVD.yes_1, exclude = FALSE))
summary(data.no_diabetes$SES)
sd(data.no_diabetes$SES, na.rm = TRUE)
sum(is.na(data.no_diabetes$SES)) / nrow(data.no_diabetes)
table(data.no_diabetes$smoking.current_1, exclude = FALSE)
prop.table(table(data.no_diabetes$smoking.current_1, exclude = FALSE))
summary(data.no_diabetes$MVPA)
sd(data.no_diabetes$MVPA, na.rm = TRUE)
sum(is.na(data.no_diabetes$MVPA)) / nrow(data.no_diabetes)
summary(data.no_diabetes$DHD)
sd(data.no_diabetes$DHD, na.rm = TRUE)
sum(is.na(data.no_diabetes$DHD)) / nrow(data.no_diabetes)
## Participants with diabetes ----
table(data.diabetes$event, exclude = NULL)
prop.table(table(data.diabetes$event, exclude = NULL))
summary(data.diabetes$time)
sd(data.diabetes$time, na.rm = TRUE)
summary(data.diabetes$max_time)
sd(data.diabetes$max_time, na.rm = TRUE)
table(data.diabetes$sex.male_1, exclude = NULL)
prop.table(table(data.diabetes$sex.male_1, exclude = NULL))
summary(data.diabetes$age)
sd(data.diabetes$age, na.rm = TRUE)
table(data.diabetes$pre_existing_ASCVD.yes_1, exclude = FALSE)
prop.table(table(data.diabetes$pre_existing_ASCVD.yes_1, exclude = FALSE))
summary(data.diabetes$SES)
sd(data.diabetes$SES, na.rm = TRUE)
sum(is.na(data.diabetes$SES)) / nrow(data.diabetes)
table(data.diabetes$smoking.current_1, exclude = FALSE)
prop.table(table(data.diabetes$smoking.current_1, exclude = FALSE))
summary(data.diabetes$MVPA)
sd(data.diabetes$MVPA, na.rm = TRUE)
sum(is.na(data.diabetes$MVPA)) / nrow(data.diabetes)
summary(data.diabetes$DHD)
sd(data.diabetes$DHD, na.rm = TRUE)
sum(is.na(data.diabetes$DHD)) / nrow(data.diabetes)
# Imputations ----
## Number of imputations ----
n_imp <- 15
## Participants without diabetes ----
imp.no_diabetes <- data.no_diabetes[, c( "event",
"time",
"sex.male_1",
"age",
"pre_existing_ASCVD.yes_1",
"SES",
"smoking.current_1",
"MVPA",
"DHD")]
imp.no_diabetes <- sapply(imp.no_diabetes, zap_labels)
imp.no_diabetes <- mice(imp.no_diabetes, method = "pmm", m = n_imp, seed = ...)
imp.no_diabetes.stacked <- list()
for (i in 1:n_imp) {
imp.no_diabetes.stacked[[i]] <- complete(imp.no_diabetes, i)
imp.no_diabetes.stacked[[i]][["ASCVD_composite_event"]] <- ifelse(imp.no_diabetes.stacked[[i]][["event"]] == 1,
1,
0)
imp.no_diabetes.stacked[[i]][["overall_mortality_excl_ASCVD_mortality_event"]] <- ifelse(imp.no_diabetes.stacked[[i]][["event"]] == 2,
1,
0)
}
for (i in 1:n_imp) {
print(paste("imputation", i))
print(table(imp.no_diabetes.stacked[[i]]$pre_existing_ASCVD.yes_1, exclude = FALSE))
print(prop.table(table(imp.no_diabetes.stacked[[i]]$pre_existing_ASCVD.yes_1, exclude = FALSE)))
print(summary(imp.no_diabetes.stacked[[i]]$SES))
print(sd(imp.no_diabetes.stacked[[i]]$SES))
print(table(imp.no_diabetes.stacked[[i]]$smoking.current_1, exclude = FALSE))
print(prop.table(table(imp.no_diabetes.stacked[[i]]$smoking.current_1, exclude = FALSE)))
print(summary(imp.no_diabetes.stacked[[i]]$MVPA))
print(sd(imp.no_diabetes.stacked[[i]]$MVPA))
print(summary(imp.no_diabetes.stacked[[i]]$DHD))
print(sd(imp.no_diabetes.stacked[[i]]$DHD))
}
## Participants with diabetes ----
imp.diabetes <- data.diabetes[, c( "event",
"time",
"sex.male_1",
"age",
"pre_existing_ASCVD.yes_1",
"SES",
"smoking.current_1",
"MVPA",
"DHD")]
imp.diabetes <- sapply(imp.diabetes, zap_labels)
imp.diabetes <- mice(imp.diabetes, method = "pmm", m = n_imp, seed = ...)
imp.diabetes.stacked <- list()
for (i in 1:n_imp) {
imp.diabetes.stacked[[i]] <- complete(imp.diabetes, i)
imp.diabetes.stacked[[i]][["ASCVD_composite_event"]] <- ifelse(imp.diabetes.stacked[[i]][["event"]] == 1,
1,
0)
imp.diabetes.stacked[[i]][["overall_mortality_excl_ASCVD_mortality_event"]] <- ifelse(imp.diabetes.stacked[[i]][["event"]] == 2,
1,
0)
}
for (i in 1:n_imp) {
print(paste("imputation", i))
print(table(imp.diabetes.stacked[[i]]$pre_existing_ASCVD.yes_1, exclude = FALSE))
print(prop.table(table(imp.diabetes.stacked[[i]]$pre_existing_ASCVD.yes_1, exclude = FALSE)))
print(summary(imp.diabetes.stacked[[i]]$SES))
print(sd(imp.diabetes.stacked[[i]]$SES))
print(table(imp.diabetes.stacked[[i]]$smoking.current_1, exclude = FALSE))
print(prop.table(table(imp.diabetes.stacked[[i]]$smoking.current_1, exclude = FALSE)))
print(summary(imp.diabetes.stacked[[i]]$MVPA))
print(sd(imp.diabetes.stacked[[i]]$MVPA))
print(summary(imp.diabetes.stacked[[i]]$DHD))
print(sd(imp.diabetes.stacked[[i]]$DHD))
}
# Causal structure ----
## DAG ----
dag <- dagitty("dag {
sex.male_1 -> age.ord
sex.male_1 -> SES.ord
sex.male_1 -> pre_existing_ASCVD.yes_1
sex.male_1 -> smoking.current_1
sex.male_1 -> MVPA.ord
sex.male_1 -> DHD.ord
sex.male_1 -> overall_mortality_excl_ASCVD_mortality_event
sex.male_1 -> ASCVD_composite_event
age.ord -> SES.ord
age.ord -> pre_existing_ASCVD.yes_1
age.ord -> smoking.current_1
age.ord -> MVPA.ord
age.ord -> DHD.ord
age.ord -> overall_mortality_excl_ASCVD_mortality_event
age.ord -> ASCVD_composite_event
SES.ord -> pre_existing_ASCVD.yes_1
SES.ord -> smoking.current_1
SES.ord -> MVPA.ord
SES.ord -> DHD.ord
SES.ord -> overall_mortality_excl_ASCVD_mortality_event
SES.ord -> ASCVD_composite_event
pre_existing_ASCVD.yes_1 -> smoking.current_1
pre_existing_ASCVD.yes_1 -> MVPA.ord
pre_existing_ASCVD.yes_1 -> DHD.ord
pre_existing_ASCVD.yes_1 -> ASCVD_composite_event
smoking.current_1 -> overall_mortality_excl_ASCVD_mortality_event
smoking.current_1 -> ASCVD_composite_event
MVPA.ord -> overall_mortality_excl_ASCVD_mortality_event
MVPA.ord -> ASCVD_composite_event
DHD.ord -> overall_mortality_excl_ASCVD_mortality_event
DHD.ord -> ASCVD_composite_event
overall_mortality_excl_ASCVD_mortality_event -> ASCVD_composite_event
}")
## Investigating conditional independencies ----
impliedConditionalIndependencies(dag)
### Participants without diabetes ----
#### Point estimates ----
no_diabetes.cond_ind.stacked <- list()
for (i in 1:n_imp) {
# Data
temp.data <- imp.no_diabetes.stacked[[i]]
# Define ordinal categorical variables
temp.data$age.ord <- ifelse(temp.data$age <= quantile(temp.data$age, 0.25)[[1]],
1,
0)
temp.data$age.ord <- ifelse((temp.data$age > quantile(temp.data$age, 0.25)[[1]])
&
(temp.data$age <= quantile(temp.data$age, 0.50)[[1]]),
2,
temp.data$age.ord)
temp.data$age.ord <- ifelse((temp.data$age > quantile(temp.data$age, 0.50)[[1]])
&
(temp.data$age <= quantile(temp.data$age, 0.75)[[1]]),
3,
temp.data$age.ord)
temp.data$age.ord <- ifelse(temp.data$age > quantile(temp.data$age, 0.75)[[1]],
4,
temp.data$age.ord)
temp.data$age.ord <- ordered(temp.data$age.ord)
temp.data$SES.ord <- ifelse(temp.data$SES <= quantile(temp.data$SES, 0.25)[[1]],
1,
0)
temp.data$SES.ord <- ifelse((temp.data$SES > quantile(temp.data$SES, 0.25)[[1]])
&
(temp.data$SES <= quantile(temp.data$SES, 0.50)[[1]]),
2,
temp.data$SES.ord)
temp.data$SES.ord <- ifelse((temp.data$SES > quantile(temp.data$SES, 0.50)[[1]])
&
(temp.data$SES <= quantile(temp.data$SES, 0.75)[[1]]),
3,
temp.data$SES.ord)
temp.data$SES.ord <- ifelse(temp.data$SES > quantile(temp.data$SES, 0.75)[[1]],
4,
temp.data$SES.ord)
temp.data$SES.ord <- ordered(temp.data$SES.ord)
temp.data$MVPA.ord <- ifelse(temp.data$MVPA <= quantile(temp.data$MVPA, 0.25)[[1]],
1,
0)
temp.data$MVPA.ord <- ifelse((temp.data$MVPA > quantile(temp.data$MVPA, 0.25)[[1]])
&
(temp.data$MVPA <= quantile(temp.data$MVPA, 0.50)[[1]]),
2,
temp.data$MVPA.ord)
temp.data$MVPA.ord <- ifelse((temp.data$MVPA > quantile(temp.data$MVPA, 0.50)[[1]])
&
(temp.data$MVPA <= quantile(temp.data$MVPA, 0.75)[[1]]),
3,
temp.data$MVPA.ord)
temp.data$MVPA.ord <- ifelse(temp.data$MVPA > quantile(temp.data$MVPA, 0.75)[[1]],
4,
temp.data$MVPA.ord)
temp.data$MVPA.ord <- ordered(temp.data$MVPA.ord)
temp.data$DHD.ord <- ifelse(temp.data$DHD <= quantile(temp.data$DHD, 0.25)[[1]],
1,
0)
temp.data$DHD.ord <- ifelse((temp.data$DHD > quantile(temp.data$DHD, 0.25)[[1]])
&
(temp.data$DHD <= quantile(temp.data$DHD, 0.50)[[1]]),
2,
temp.data$DHD.ord)
temp.data$DHD.ord <- ifelse((temp.data$DHD > quantile(temp.data$DHD, 0.50)[[1]])
&
(temp.data$DHD <= quantile(temp.data$DHD, 0.75)[[1]]),
3,
temp.data$DHD.ord)
temp.data$DHD.ord <- ifelse(temp.data$DHD > quantile(temp.data$DHD, 0.75)[[1]],
4,
temp.data$DHD.ord)
temp.data$DHD.ord <- ordered(temp.data$DHD.ord)
# Define binary variables
temp.data$sex.male_1 <- as.integer(temp.data$sex.male_1)
temp.data$pre_existing_ASCVD.yes_1 <- as.integer(temp.data$pre_existing_ASCVD.yes_1)
temp.data$smoking.current_1 <- as.integer(temp.data$smoking.current_1)
temp.data$ASCVD_composite_event <- as.integer(temp.data$ASCVD_composite_event)
temp.data$overall_mortality_excl_ASCVD_mortality_event <- as.integer(temp.data$overall_mortality_excl_ASCVD_mortality_event)
# Unadjusted correlations
temp.cor <- lavCor(temp.data[, c("sex.male_1",
"age.ord",
"pre_existing_ASCVD.yes_1",
"SES.ord",
"smoking.current_1",
"MVPA.ord",
"DHD.ord",
"ASCVD_composite_event",
"overall_mortality_excl_ASCVD_mortality_event")])
# Adjusted correlations
no_diabetes.cond_ind.stacked[[i]] <- localTests(dag,
sample.cov = temp.cor,
sample.nobs = nrow(temp.data))
}
no_diabetes.cond_ind.1 <- c()
for (i in 1:n_imp) {
no_diabetes.cond_ind.1 <- append(no_diabetes.cond_ind.1, no_diabetes.cond_ind.stacked[[i]][[1]][[1]])
}
mean.no_diabetes.cond_ind.1 <- mean(no_diabetes.cond_ind.1)
mean.no_diabetes.cond_ind.1
no_diabetes.cond_ind.2 <- c()
for (i in 1:n_imp) {
no_diabetes.cond_ind.2 <- append(no_diabetes.cond_ind.2, no_diabetes.cond_ind.stacked[[i]][[1]][[2]])
}
mean.no_diabetes.cond_ind.2 <- mean(no_diabetes.cond_ind.2)
mean.no_diabetes.cond_ind.2
no_diabetes.cond_ind.3 <- c()
for (i in 1:n_imp) {
no_diabetes.cond_ind.3 <- append(no_diabetes.cond_ind.3, no_diabetes.cond_ind.stacked[[i]][[1]][[3]])
}
mean.no_diabetes.cond_ind.3 <- mean(no_diabetes.cond_ind.3)
mean.no_diabetes.cond_ind.3
no_diabetes.cond_ind.4 <- c()
for (i in 1:n_imp) {
no_diabetes.cond_ind.4 <- append(no_diabetes.cond_ind.4, no_diabetes.cond_ind.stacked[[i]][[1]][[4]])
}
mean.no_diabetes.cond_ind.4 <- mean(no_diabetes.cond_ind.4)
mean.no_diabetes.cond_ind.4
#### Bootstrapped confidence intervals ----
# Seed
set.seed(...)
# Number of bootstraps
n_b <- 150
# Creating object to capture output
boot.mean.no_diabetes.cond_ind <- as.data.frame(matrix(nrow = n_b,
ncol = 5))
colnames(boot.mean.no_diabetes.cond_ind) <- c("b",
"boot.mean.no_diabetes.cond_ind.1",
"boot.mean.no_diabetes.cond_ind.2",
"boot.mean.no_diabetes.cond_ind.3",
"boot.mean.no_diabetes.cond_ind.4")
boot.mean.no_diabetes.cond_ind$b <- 1:n_b
# Loop
for (b in 1:n_b) {
boot.index <- sample(1:nrow(data.no_diabetes), size = nrow(data.no_diabetes), replace = TRUE)
boot.data.no_diabetes <- data.no_diabetes[boot.index, ]
boot.imp.no_diabetes <- boot.data.no_diabetes[, c( "event",
"time",
"sex.male_1",
"age",
"pre_existing_ASCVD.yes_1",
"SES",
"smoking.current_1",
"MVPA",
"DHD")]
boot.imp.no_diabetes <- sapply(boot.imp.no_diabetes, zap_labels)
boot.imp.no_diabetes <- mice(boot.imp.no_diabetes, method = "pmm", m = n_imp, print = FALSE)
boot.imp.no_diabetes.stacked <- list()
for (i in 1:n_imp) {
boot.imp.no_diabetes.stacked[[i]] <- complete(boot.imp.no_diabetes, i)
boot.imp.no_diabetes.stacked[[i]][["ASCVD_composite_event"]] <- ifelse(boot.imp.no_diabetes.stacked[[i]][["event"]] == 1,
1,
0)
boot.imp.no_diabetes.stacked[[i]][["overall_mortality_excl_ASCVD_mortality_event"]] <- ifelse(boot.imp.no_diabetes.stacked[[i]][["event"]] == 2,
1,
0)
}
boot.no_diabetes.cond_ind.stacked <- list()
for (i in 1:n_imp) {
# Data
boot.temp.data <- boot.imp.no_diabetes.stacked[[i]]
# Define ordinal categorical variables
boot.temp.data$age.ord <- ifelse(boot.temp.data$age <= quantile(boot.temp.data$age, 0.25)[[1]],
1,
0)
boot.temp.data$age.ord <- ifelse((boot.temp.data$age > quantile(boot.temp.data$age, 0.25)[[1]])
&
(boot.temp.data$age <= quantile(boot.temp.data$age, 0.50)[[1]]),
2,
boot.temp.data$age.ord)
boot.temp.data$age.ord <- ifelse((boot.temp.data$age > quantile(boot.temp.data$age, 0.50)[[1]])
&
(boot.temp.data$age <= quantile(boot.temp.data$age, 0.75)[[1]]),
3,
boot.temp.data$age.ord)
boot.temp.data$age.ord <- ifelse(boot.temp.data$age > quantile(boot.temp.data$age, 0.75)[[1]],
4,
boot.temp.data$age.ord)
boot.temp.data$age.ord <- ordered(boot.temp.data$age.ord)
boot.temp.data$SES.ord <- ifelse(boot.temp.data$SES <= quantile(boot.temp.data$SES, 0.25)[[1]],
1,
0)
boot.temp.data$SES.ord <- ifelse((boot.temp.data$SES > quantile(boot.temp.data$SES, 0.25)[[1]])
&
(boot.temp.data$SES <= quantile(boot.temp.data$SES, 0.50)[[1]]),
2,
boot.temp.data$SES.ord)
boot.temp.data$SES.ord <- ifelse((boot.temp.data$SES > quantile(boot.temp.data$SES, 0.50)[[1]])
&
(boot.temp.data$SES <= quantile(boot.temp.data$SES, 0.75)[[1]]),
3,
boot.temp.data$SES.ord)
boot.temp.data$SES.ord <- ifelse(boot.temp.data$SES > quantile(boot.temp.data$SES, 0.75)[[1]],
4,
boot.temp.data$SES.ord)
boot.temp.data$SES.ord <- ordered(boot.temp.data$SES.ord)
boot.temp.data$MVPA.ord <- ifelse(boot.temp.data$MVPA <= quantile(boot.temp.data$MVPA, 0.25)[[1]],
1,
0)
boot.temp.data$MVPA.ord <- ifelse((boot.temp.data$MVPA > quantile(boot.temp.data$MVPA, 0.25)[[1]])
&
(boot.temp.data$MVPA <= quantile(boot.temp.data$MVPA, 0.50)[[1]]),
2,
boot.temp.data$MVPA.ord)
boot.temp.data$MVPA.ord <- ifelse((boot.temp.data$MVPA > quantile(boot.temp.data$MVPA, 0.50)[[1]])
&
(boot.temp.data$MVPA <= quantile(boot.temp.data$MVPA, 0.75)[[1]]),
3,
boot.temp.data$MVPA.ord)
boot.temp.data$MVPA.ord <- ifelse(boot.temp.data$MVPA > quantile(boot.temp.data$MVPA, 0.75)[[1]],
4,
boot.temp.data$MVPA.ord)
boot.temp.data$MVPA.ord <- ordered(boot.temp.data$MVPA.ord)
boot.temp.data$DHD.ord <- ifelse(boot.temp.data$DHD <= quantile(boot.temp.data$DHD, 0.25)[[1]],
1,
0)
boot.temp.data$DHD.ord <- ifelse((boot.temp.data$DHD > quantile(boot.temp.data$DHD, 0.25)[[1]])
&
(boot.temp.data$DHD <= quantile(boot.temp.data$DHD, 0.50)[[1]]),
2,
boot.temp.data$DHD.ord)
boot.temp.data$DHD.ord <- ifelse((boot.temp.data$DHD > quantile(boot.temp.data$DHD, 0.50)[[1]])
&
(boot.temp.data$DHD <= quantile(boot.temp.data$DHD, 0.75)[[1]]),
3,
boot.temp.data$DHD.ord)
boot.temp.data$DHD.ord <- ifelse(boot.temp.data$DHD > quantile(boot.temp.data$DHD, 0.75)[[1]],
4,
boot.temp.data$DHD.ord)
boot.temp.data$DHD.ord <- ordered(boot.temp.data$DHD.ord)
# Define binary variables
boot.temp.data$sex.male_1 <- as.integer(boot.temp.data$sex.male_1)
boot.temp.data$pre_existing_ASCVD.yes_1 <- as.integer(boot.temp.data$pre_existing_ASCVD.yes_1)
boot.temp.data$smoking.current_1 <- as.integer(boot.temp.data$smoking.current_1)
boot.temp.data$ASCVD_composite_event <- as.integer(boot.temp.data$ASCVD_composite_event)
boot.temp.data$overall_mortality_excl_ASCVD_mortality_event <- as.integer(boot.temp.data$overall_mortality_excl_ASCVD_mortality_event)
# Unadjusted correlations
boot.temp.cor <- lavCor(boot.temp.data[, c("sex.male_1",
"age.ord",
"pre_existing_ASCVD.yes_1",
"SES.ord",
"smoking.current_1",
"MVPA.ord",
"DHD.ord",
"ASCVD_composite_event",
"overall_mortality_excl_ASCVD_mortality_event")])
# Adjusted correlations
boot.no_diabetes.cond_ind.stacked[[i]] <- localTests(dag,
sample.cov = boot.temp.cor,
sample.nobs = nrow(boot.temp.data))
}
boot.no_diabetes.cond_ind.1 <- c()
for (i in 1:n_imp) {
boot.no_diabetes.cond_ind.1 <- append(boot.no_diabetes.cond_ind.1, boot.no_diabetes.cond_ind.stacked[[i]][[1]][[1]])
}
boot.mean.no_diabetes.cond_ind.1 <- mean(boot.no_diabetes.cond_ind.1)
boot.mean.no_diabetes.cond_ind[b, "boot.mean.no_diabetes.cond_ind.1"] <- boot.mean.no_diabetes.cond_ind.1
boot.no_diabetes.cond_ind.2 <- c()
for (i in 1:n_imp) {
boot.no_diabetes.cond_ind.2 <- append(boot.no_diabetes.cond_ind.2, boot.no_diabetes.cond_ind.stacked[[i]][[1]][[2]])
}
boot.mean.no_diabetes.cond_ind.2 <- mean(boot.no_diabetes.cond_ind.2)
boot.mean.no_diabetes.cond_ind[b, "boot.mean.no_diabetes.cond_ind.2"] <- boot.mean.no_diabetes.cond_ind.2
boot.no_diabetes.cond_ind.3 <- c()
for (i in 1:n_imp) {
boot.no_diabetes.cond_ind.3 <- append(boot.no_diabetes.cond_ind.3, boot.no_diabetes.cond_ind.stacked[[i]][[1]][[3]])
}
boot.mean.no_diabetes.cond_ind.3 <- mean(boot.no_diabetes.cond_ind.3)
boot.mean.no_diabetes.cond_ind[b, "boot.mean.no_diabetes.cond_ind.3"] <- boot.mean.no_diabetes.cond_ind.3
boot.no_diabetes.cond_ind.4 <- c()
for (i in 1:n_imp) {
boot.no_diabetes.cond_ind.4 <- append(boot.no_diabetes.cond_ind.4, boot.no_diabetes.cond_ind.stacked[[i]][[1]][[4]])
}
boot.mean.no_diabetes.cond_ind.4 <- mean(boot.no_diabetes.cond_ind.4)
boot.mean.no_diabetes.cond_ind[b, "boot.mean.no_diabetes.cond_ind.4"] <- boot.mean.no_diabetes.cond_ind.4
}
# Output
for (b in 10:n_b) {
boot.mean.no_diabetes.cond_ind[b, "boot.mean.no_diabetes.cond_ind.1.lb"] <- quantile(boot.mean.no_diabetes.cond_ind[1:b, "boot.mean.no_diabetes.cond_ind.1"], 0.025)
boot.mean.no_diabetes.cond_ind[b, "boot.mean.no_diabetes.cond_ind.1.ub"] <- quantile(boot.mean.no_diabetes.cond_ind[1:b, "boot.mean.no_diabetes.cond_ind.1"], 0.975)
}
ggplot() +
geom_point(data = boot.mean.no_diabetes.cond_ind, aes(x = b, y = boot.mean.no_diabetes.cond_ind.1.lb), colour = "skyblue") +
geom_point(data = boot.mean.no_diabetes.cond_ind, aes(x = b, y = boot.mean.no_diabetes.cond_ind.1.ub), colour = "skyblue4") +
scale_x_discrete(name = "last bootstrap") +
scale_y_continuous(name = "cond_ind.1") +
theme_minimal()
quantile(boot.mean.no_diabetes.cond_ind$boot.mean.no_diabetes.cond_ind.1, c(0.025, 0.975))
for (b in 10:n_b) {
boot.mean.no_diabetes.cond_ind[b, "boot.mean.no_diabetes.cond_ind.2.lb"] <- quantile(boot.mean.no_diabetes.cond_ind[1:b, "boot.mean.no_diabetes.cond_ind.2"], 0.025)
boot.mean.no_diabetes.cond_ind[b, "boot.mean.no_diabetes.cond_ind.2.ub"] <- quantile(boot.mean.no_diabetes.cond_ind[1:b, "boot.mean.no_diabetes.cond_ind.2"], 0.975)
}
ggplot() +
geom_point(data = boot.mean.no_diabetes.cond_ind, aes(x = b, y = boot.mean.no_diabetes.cond_ind.2.lb), colour = "skyblue") +
geom_point(data = boot.mean.no_diabetes.cond_ind, aes(x = b, y = boot.mean.no_diabetes.cond_ind.2.ub), colour = "skyblue4") +
scale_x_discrete(name = "last bootstrap") +
scale_y_continuous(name = "cond_ind.2") +
theme_minimal()
quantile(boot.mean.no_diabetes.cond_ind$boot.mean.no_diabetes.cond_ind.2, c(0.025, 0.975))
for (b in 10:n_b) {
boot.mean.no_diabetes.cond_ind[b, "boot.mean.no_diabetes.cond_ind.3.lb"] <- quantile(boot.mean.no_diabetes.cond_ind[1:b, "boot.mean.no_diabetes.cond_ind.3"], 0.025)
boot.mean.no_diabetes.cond_ind[b, "boot.mean.no_diabetes.cond_ind.3.ub"] <- quantile(boot.mean.no_diabetes.cond_ind[1:b, "boot.mean.no_diabetes.cond_ind.3"], 0.975)
}
ggplot() +
geom_point(data = boot.mean.no_diabetes.cond_ind, aes(x = b, y = boot.mean.no_diabetes.cond_ind.3.lb), colour = "skyblue") +
geom_point(data = boot.mean.no_diabetes.cond_ind, aes(x = b, y = boot.mean.no_diabetes.cond_ind.3.ub), colour = "skyblue4") +
scale_x_discrete(name = "last bootstrap") +
scale_y_continuous(name = "cond_ind.3") +
theme_minimal()
quantile(boot.mean.no_diabetes.cond_ind$boot.mean.no_diabetes.cond_ind.3, c(0.025, 0.975))
for (b in 10:n_b) {
boot.mean.no_diabetes.cond_ind[b, "boot.mean.no_diabetes.cond_ind.4.lb"] <- quantile(boot.mean.no_diabetes.cond_ind[1:b, "boot.mean.no_diabetes.cond_ind.4"], 0.025)
boot.mean.no_diabetes.cond_ind[b, "boot.mean.no_diabetes.cond_ind.4.ub"] <- quantile(boot.mean.no_diabetes.cond_ind[1:b, "boot.mean.no_diabetes.cond_ind.4"], 0.975)
}
ggplot() +
geom_point(data = boot.mean.no_diabetes.cond_ind, aes(x = b, y = boot.mean.no_diabetes.cond_ind.4.lb), colour = "skyblue") +
geom_point(data = boot.mean.no_diabetes.cond_ind, aes(x = b, y = boot.mean.no_diabetes.cond_ind.4.ub), colour = "skyblue4") +
scale_x_discrete(name = "last bootstrap") +
scale_y_continuous(name = "cond_ind.4") +
theme_minimal()
quantile(boot.mean.no_diabetes.cond_ind$boot.mean.no_diabetes.cond_ind.4, c(0.025, 0.975))
### Participants with diabetes ----
#### Point estimates ----
diabetes.cond_ind.stacked <- list()
for (i in 1:n_imp) {
# Data
temp.data <- imp.diabetes.stacked[[i]]
# Define ordinal categorical variables
temp.data$age.ord <- ifelse(temp.data$age <= quantile(temp.data$age, 0.25)[[1]],
1,
0)
temp.data$age.ord <- ifelse((temp.data$age > quantile(temp.data$age, 0.25)[[1]])
&
(temp.data$age <= quantile(temp.data$age, 0.50)[[1]]),
2,
temp.data$age.ord)
temp.data$age.ord <- ifelse((temp.data$age > quantile(temp.data$age, 0.50)[[1]])
&
(temp.data$age <= quantile(temp.data$age, 0.75)[[1]]),
3,
temp.data$age.ord)
temp.data$age.ord <- ifelse(temp.data$age > quantile(temp.data$age, 0.75)[[1]],
4,
temp.data$age.ord)
temp.data$age.ord <- ordered(temp.data$age.ord)
temp.data$SES.ord <- ifelse(temp.data$SES <= quantile(temp.data$SES, 0.25)[[1]],
1,
0)
temp.data$SES.ord <- ifelse((temp.data$SES > quantile(temp.data$SES, 0.25)[[1]])
&
(temp.data$SES <= quantile(temp.data$SES, 0.50)[[1]]),
2,
temp.data$SES.ord)
temp.data$SES.ord <- ifelse((temp.data$SES > quantile(temp.data$SES, 0.50)[[1]])
&
(temp.data$SES <= quantile(temp.data$SES, 0.75)[[1]]),
3,
temp.data$SES.ord)
temp.data$SES.ord <- ifelse(temp.data$SES > quantile(temp.data$SES, 0.75)[[1]],
4,
temp.data$SES.ord)
temp.data$SES.ord <- ordered(temp.data$SES.ord)
temp.data$MVPA.ord <- ifelse(temp.data$MVPA <= quantile(temp.data$MVPA, 0.25)[[1]],
1,
0)
temp.data$MVPA.ord <- ifelse((temp.data$MVPA > quantile(temp.data$MVPA, 0.25)[[1]])
&
(temp.data$MVPA <= quantile(temp.data$MVPA, 0.50)[[1]]),
2,
temp.data$MVPA.ord)
temp.data$MVPA.ord <- ifelse((temp.data$MVPA > quantile(temp.data$MVPA, 0.50)[[1]])
&
(temp.data$MVPA <= quantile(temp.data$MVPA, 0.75)[[1]]),
3,
temp.data$MVPA.ord)
temp.data$MVPA.ord <- ifelse(temp.data$MVPA > quantile(temp.data$MVPA, 0.75)[[1]],
4,
temp.data$MVPA.ord)
temp.data$MVPA.ord <- ordered(temp.data$MVPA.ord)
temp.data$DHD.ord <- ifelse(temp.data$DHD <= quantile(temp.data$DHD, 0.25)[[1]],
1,
0)
temp.data$DHD.ord <- ifelse((temp.data$DHD > quantile(temp.data$DHD, 0.25)[[1]])
&
(temp.data$DHD <= quantile(temp.data$DHD, 0.50)[[1]]),
2,
temp.data$DHD.ord)
temp.data$DHD.ord <- ifelse((temp.data$DHD > quantile(temp.data$DHD, 0.50)[[1]])
&
(temp.data$DHD <= quantile(temp.data$DHD, 0.75)[[1]]),
3,
temp.data$DHD.ord)
temp.data$DHD.ord <- ifelse(temp.data$DHD > quantile(temp.data$DHD, 0.75)[[1]],
4,
temp.data$DHD.ord)
temp.data$DHD.ord <- ordered(temp.data$DHD.ord)
# Define binary variables
temp.data$sex.male_1 <- as.integer(temp.data$sex.male_1)
temp.data$pre_existing_ASCVD.yes_1 <- as.integer(temp.data$pre_existing_ASCVD.yes_1)
temp.data$smoking.current_1 <- as.integer(temp.data$smoking.current_1)
temp.data$ASCVD_composite_event <- as.integer(temp.data$ASCVD_composite_event)
temp.data$overall_mortality_excl_ASCVD_mortality_event <- as.integer(temp.data$overall_mortality_excl_ASCVD_mortality_event)
# Unadjusted correlations
temp.cor <- lavCor(temp.data[, c("sex.male_1",
"age.ord",
"pre_existing_ASCVD.yes_1",
"SES.ord",
"smoking.current_1",
"MVPA.ord",
"DHD.ord",
"ASCVD_composite_event",
"overall_mortality_excl_ASCVD_mortality_event")])
# Adjusted correlations
diabetes.cond_ind.stacked[[i]] <- localTests(dag,
sample.cov = temp.cor,
sample.nobs = nrow(temp.data))
}
diabetes.cond_ind.1 <- c()
for (i in 1:n_imp) {
diabetes.cond_ind.1 <- append(diabetes.cond_ind.1, diabetes.cond_ind.stacked[[i]][[1]][[1]])
}
mean.diabetes.cond_ind.1 <- mean(diabetes.cond_ind.1)
mean.diabetes.cond_ind.1
diabetes.cond_ind.2 <- c()
for (i in 1:n_imp) {
diabetes.cond_ind.2 <- append(diabetes.cond_ind.2, diabetes.cond_ind.stacked[[i]][[1]][[2]])
}
mean.diabetes.cond_ind.2 <- mean(diabetes.cond_ind.2)
mean.diabetes.cond_ind.2
diabetes.cond_ind.3 <- c()
for (i in 1:n_imp) {
diabetes.cond_ind.3 <- append(diabetes.cond_ind.3, diabetes.cond_ind.stacked[[i]][[1]][[3]])
}
mean.diabetes.cond_ind.3 <- mean(diabetes.cond_ind.3)
mean.diabetes.cond_ind.3
diabetes.cond_ind.4 <- c()
for (i in 1:n_imp) {
diabetes.cond_ind.4 <- append(diabetes.cond_ind.4, diabetes.cond_ind.stacked[[i]][[1]][[4]])
}
mean.diabetes.cond_ind.4 <- mean(diabetes.cond_ind.4)
mean.diabetes.cond_ind.4
#### Bootstrapped confidence intervals ----
# Seed
set.seed(...)
# Number of bootstraps
n_b <- 150
# Creating object to capture output
boot.mean.diabetes.cond_ind <- as.data.frame(matrix(nrow = n_b,
ncol = 5))
colnames(boot.mean.diabetes.cond_ind) <- c("b",
"boot.mean.diabetes.cond_ind.1",
"boot.mean.diabetes.cond_ind.2",
"boot.mean.diabetes.cond_ind.3",
"boot.mean.diabetes.cond_ind.4")
boot.mean.diabetes.cond_ind$b <- 1:n_b
# Loop
for (b in 1:n_b) {
boot.index <- sample(1:nrow(data.diabetes), size = nrow(data.diabetes), replace = TRUE)
boot.data.diabetes <- data.diabetes[boot.index, ]
boot.imp.diabetes <- boot.data.diabetes[, c( "event",
"time",
"sex.male_1",
"age",
"pre_existing_ASCVD.yes_1",
"SES",
"smoking.current_1",
"MVPA",
"DHD")]
boot.imp.diabetes <- sapply(boot.imp.diabetes, zap_labels)
boot.imp.diabetes <- mice(boot.imp.diabetes, method = "pmm", m = n_imp, print = FALSE)
boot.imp.diabetes.stacked <- list()
for (i in 1:n_imp) {
boot.imp.diabetes.stacked[[i]] <- complete(boot.imp.diabetes, i)
boot.imp.diabetes.stacked[[i]][["ASCVD_composite_event"]] <- ifelse(boot.imp.diabetes.stacked[[i]][["event"]] == 1,
1,
0)
boot.imp.diabetes.stacked[[i]][["overall_mortality_excl_ASCVD_mortality_event"]] <- ifelse(boot.imp.diabetes.stacked[[i]][["event"]] == 2,
1,
0)
}
boot.diabetes.cond_ind.stacked <- list()
for (i in 1:n_imp) {
# Data
boot.temp.data <- boot.imp.diabetes.stacked[[i]]
# Define ordinal categorical variables
boot.temp.data$age.ord <- ifelse(boot.temp.data$age <= quantile(boot.temp.data$age, 0.25)[[1]],
1,
0)
boot.temp.data$age.ord <- ifelse((boot.temp.data$age > quantile(boot.temp.data$age, 0.25)[[1]])
&
(boot.temp.data$age <= quantile(boot.temp.data$age, 0.50)[[1]]),
2,
boot.temp.data$age.ord)
boot.temp.data$age.ord <- ifelse((boot.temp.data$age > quantile(boot.temp.data$age, 0.50)[[1]])
&
(boot.temp.data$age <= quantile(boot.temp.data$age, 0.75)[[1]]),
3,
boot.temp.data$age.ord)
boot.temp.data$age.ord <- ifelse(boot.temp.data$age > quantile(boot.temp.data$age, 0.75)[[1]],
4,
boot.temp.data$age.ord)
boot.temp.data$age.ord <- ordered(boot.temp.data$age.ord)
boot.temp.data$SES.ord <- ifelse(boot.temp.data$SES <= quantile(boot.temp.data$SES, 0.25)[[1]],
1,
0)
boot.temp.data$SES.ord <- ifelse((boot.temp.data$SES > quantile(boot.temp.data$SES, 0.25)[[1]])
&
(boot.temp.data$SES <= quantile(boot.temp.data$SES, 0.50)[[1]]),
2,
boot.temp.data$SES.ord)
boot.temp.data$SES.ord <- ifelse((boot.temp.data$SES > quantile(boot.temp.data$SES, 0.50)[[1]])
&
(boot.temp.data$SES <= quantile(boot.temp.data$SES, 0.75)[[1]]),
3,
boot.temp.data$SES.ord)
boot.temp.data$SES.ord <- ifelse(boot.temp.data$SES > quantile(boot.temp.data$SES, 0.75)[[1]],
4,
boot.temp.data$SES.ord)
boot.temp.data$SES.ord <- ordered(boot.temp.data$SES.ord)
boot.temp.data$MVPA.ord <- ifelse(boot.temp.data$MVPA <= quantile(boot.temp.data$MVPA, 0.25)[[1]],
1,
0)
boot.temp.data$MVPA.ord <- ifelse((boot.temp.data$MVPA > quantile(boot.temp.data$MVPA, 0.25)[[1]])
&
(boot.temp.data$MVPA <= quantile(boot.temp.data$MVPA, 0.50)[[1]]),
2,
boot.temp.data$MVPA.ord)
boot.temp.data$MVPA.ord <- ifelse((boot.temp.data$MVPA > quantile(boot.temp.data$MVPA, 0.50)[[1]])
&
(boot.temp.data$MVPA <= quantile(boot.temp.data$MVPA, 0.75)[[1]]),
3,
boot.temp.data$MVPA.ord)
boot.temp.data$MVPA.ord <- ifelse(boot.temp.data$MVPA > quantile(boot.temp.data$MVPA, 0.75)[[1]],
4,
boot.temp.data$MVPA.ord)
boot.temp.data$MVPA.ord <- ordered(boot.temp.data$MVPA.ord)
boot.temp.data$DHD.ord <- ifelse(boot.temp.data$DHD <= quantile(boot.temp.data$DHD, 0.25)[[1]],
1,
0)
boot.temp.data$DHD.ord <- ifelse((boot.temp.data$DHD > quantile(boot.temp.data$DHD, 0.25)[[1]])
&
(boot.temp.data$DHD <= quantile(boot.temp.data$DHD, 0.50)[[1]]),
2,
boot.temp.data$DHD.ord)
boot.temp.data$DHD.ord <- ifelse((boot.temp.data$DHD > quantile(boot.temp.data$DHD, 0.50)[[1]])
&
(boot.temp.data$DHD <= quantile(boot.temp.data$DHD, 0.75)[[1]]),
3,
boot.temp.data$DHD.ord)
boot.temp.data$DHD.ord <- ifelse(boot.temp.data$DHD > quantile(boot.temp.data$DHD, 0.75)[[1]],
4,
boot.temp.data$DHD.ord)
boot.temp.data$DHD.ord <- ordered(boot.temp.data$DHD.ord)
# Define binary variables
boot.temp.data$sex.male_1 <- as.integer(boot.temp.data$sex.male_1)
boot.temp.data$pre_existing_ASCVD.yes_1 <- as.integer(boot.temp.data$pre_existing_ASCVD.yes_1)
boot.temp.data$smoking.current_1 <- as.integer(boot.temp.data$smoking.current_1)
boot.temp.data$ASCVD_composite_event <- as.integer(boot.temp.data$ASCVD_composite_event)
boot.temp.data$overall_mortality_excl_ASCVD_mortality_event <- as.integer(boot.temp.data$overall_mortality_excl_ASCVD_mortality_event)
# Unadjusted correlations
boot.temp.cor <- lavCor(boot.temp.data[, c("sex.male_1",
"age.ord",
"pre_existing_ASCVD.yes_1",
"SES.ord",
"smoking.current_1",
"MVPA.ord",
"DHD.ord",
"ASCVD_composite_event",
"overall_mortality_excl_ASCVD_mortality_event")])
# Adjusted correlations
boot.diabetes.cond_ind.stacked[[i]] <- localTests(dag,
sample.cov = boot.temp.cor,
sample.nobs = nrow(boot.temp.data))
}
boot.diabetes.cond_ind.1 <- c()
for (i in 1:n_imp) {
boot.diabetes.cond_ind.1 <- append(boot.diabetes.cond_ind.1, boot.diabetes.cond_ind.stacked[[i]][[1]][[1]])
}
boot.mean.diabetes.cond_ind.1 <- mean(boot.diabetes.cond_ind.1)
boot.mean.diabetes.cond_ind[b, "boot.mean.diabetes.cond_ind.1"] <- boot.mean.diabetes.cond_ind.1
boot.diabetes.cond_ind.2 <- c()
for (i in 1:n_imp) {
boot.diabetes.cond_ind.2 <- append(boot.diabetes.cond_ind.2, boot.diabetes.cond_ind.stacked[[i]][[1]][[2]])
}
boot.mean.diabetes.cond_ind.2 <- mean(boot.diabetes.cond_ind.2)
boot.mean.diabetes.cond_ind[b, "boot.mean.diabetes.cond_ind.2"] <- boot.mean.diabetes.cond_ind.2
boot.diabetes.cond_ind.3 <- c()
for (i in 1:n_imp) {
boot.diabetes.cond_ind.3 <- append(boot.diabetes.cond_ind.3, boot.diabetes.cond_ind.stacked[[i]][[1]][[3]])
}
boot.mean.diabetes.cond_ind.3 <- mean(boot.diabetes.cond_ind.3)
boot.mean.diabetes.cond_ind[b, "boot.mean.diabetes.cond_ind.3"] <- boot.mean.diabetes.cond_ind.3
boot.diabetes.cond_ind.4 <- c()
for (i in 1:n_imp) {
boot.diabetes.cond_ind.4 <- append(boot.diabetes.cond_ind.4, boot.diabetes.cond_ind.stacked[[i]][[1]][[4]])
}
boot.mean.diabetes.cond_ind.4 <- mean(boot.diabetes.cond_ind.4)
boot.mean.diabetes.cond_ind[b, "boot.mean.diabetes.cond_ind.4"] <- boot.mean.diabetes.cond_ind.4
}
# Output
for (b in 10:n_b) {
boot.mean.diabetes.cond_ind[b, "boot.mean.diabetes.cond_ind.1.lb"] <- quantile(boot.mean.diabetes.cond_ind[1:b, "boot.mean.diabetes.cond_ind.1"], 0.025)
boot.mean.diabetes.cond_ind[b, "boot.mean.diabetes.cond_ind.1.ub"] <- quantile(boot.mean.diabetes.cond_ind[1:b, "boot.mean.diabetes.cond_ind.1"], 0.975)
}
ggplot() +
geom_point(data = boot.mean.diabetes.cond_ind, aes(x = b, y = boot.mean.diabetes.cond_ind.1.lb), colour = "skyblue") +
geom_point(data = boot.mean.diabetes.cond_ind, aes(x = b, y = boot.mean.diabetes.cond_ind.1.ub), colour = "skyblue4") +
scale_x_discrete(name = "last bootstrap") +
scale_y_continuous(name = "cond_ind.1") +
theme_minimal()
quantile(boot.mean.diabetes.cond_ind$boot.mean.diabetes.cond_ind.1, c(0.025, 0.975))
for (b in 10:n_b) {
boot.mean.diabetes.cond_ind[b, "boot.mean.diabetes.cond_ind.2.lb"] <- quantile(boot.mean.diabetes.cond_ind[1:b, "boot.mean.diabetes.cond_ind.2"], 0.025)
boot.mean.diabetes.cond_ind[b, "boot.mean.diabetes.cond_ind.2.ub"] <- quantile(boot.mean.diabetes.cond_ind[1:b, "boot.mean.diabetes.cond_ind.2"], 0.975)
}
ggplot() +
geom_point(data = boot.mean.diabetes.cond_ind, aes(x = b, y = boot.mean.diabetes.cond_ind.2.lb), colour = "skyblue") +
geom_point(data = boot.mean.diabetes.cond_ind, aes(x = b, y = boot.mean.diabetes.cond_ind.2.ub), colour = "skyblue4") +
scale_x_discrete(name = "last bootstrap") +
scale_y_continuous(name = "cond_ind.2") +
theme_minimal()
quantile(boot.mean.diabetes.cond_ind$boot.mean.diabetes.cond_ind.2, c(0.025, 0.975))
for (b in 10:n_b) {
boot.mean.diabetes.cond_ind[b, "boot.mean.diabetes.cond_ind.3.lb"] <- quantile(boot.mean.diabetes.cond_ind[1:b, "boot.mean.diabetes.cond_ind.3"], 0.025)
boot.mean.diabetes.cond_ind[b, "boot.mean.diabetes.cond_ind.3.ub"] <- quantile(boot.mean.diabetes.cond_ind[1:b, "boot.mean.diabetes.cond_ind.3"], 0.975)
}
ggplot() +
geom_point(data = boot.mean.diabetes.cond_ind, aes(x = b, y = boot.mean.diabetes.cond_ind.3.lb), colour = "skyblue") +
geom_point(data = boot.mean.diabetes.cond_ind, aes(x = b, y = boot.mean.diabetes.cond_ind.3.ub), colour = "skyblue4") +
scale_x_discrete(name = "last bootstrap") +
scale_y_continuous(name = "cond_ind.3") +
theme_minimal()
quantile(boot.mean.diabetes.cond_ind$boot.mean.diabetes.cond_ind.3, c(0.025, 0.975))
for (b in 10:n_b) {
boot.mean.diabetes.cond_ind[b, "boot.mean.diabetes.cond_ind.4.lb"] <- quantile(boot.mean.diabetes.cond_ind[1:b, "boot.mean.diabetes.cond_ind.4"], 0.025)
boot.mean.diabetes.cond_ind[b, "boot.mean.diabetes.cond_ind.4.ub"] <- quantile(boot.mean.diabetes.cond_ind[1:b, "boot.mean.diabetes.cond_ind.4"], 0.975)
}
ggplot() +
geom_point(data = boot.mean.diabetes.cond_ind, aes(x = b, y = boot.mean.diabetes.cond_ind.4.lb), colour = "skyblue") +
geom_point(data = boot.mean.diabetes.cond_ind, aes(x = b, y = boot.mean.diabetes.cond_ind.4.ub), colour = "skyblue4") +