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Association3.0.Rmd
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Association3.0.Rmd
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---
title: "Association"
output: html_notebook
---
Run Calculating_BA.Rmd first
### Association between BA and ADRD
Prepare variables, recoding DispInk and UtbNiva
```{r}
# Calculate median and IQR
median_income <- median(to1po2$DispInk, na.rm = TRUE)
iqr_income <- IQR(to1po2$DispInk, na.rm = TRUE)
# Define breaks based on median and IQR
# Adjust these values as needed to better categorize your data
lower_break <- median_income - iqr_income
upper_break <- median_income + iqr_income
breaks <- c(-Inf, lower_break, upper_break, Inf)
labels <- c("Low Income", "Middle Income", "High Income")
to1po2 <- to1po2 %>%
mutate(
income_bins = cut(DispInk, breaks = breaks, labels = labels, include.lowest = TRUE),
income_bins = factor(income_bins, levels = labels) # Set the levels to maintain order
)
# Set 'Middle Income' as the reference category
to1po2$income_bins <- relevel(to1po2$income_bins, ref = "Middle Income")
# Check the distribution of the new bins
table(to1po2$income_bins)
# Education level
#to1po2$UtbNiva <- factor(to1po2$UtbNiva)
#to1po2$UtbNiva <- relevel(to1po2$UtbNiva, ref = "4")
library(dplyr)
to1po2 <- to1po2 %>%
mutate(utbNiva = replace(UtbNiva, UtbNiva == "", NA_character_)) %>%
mutate(NewUtbNiva = case_when(
utbNiva %in% c('1', '2', '3') ~ '1',
utbNiva %in% c('4', '5', '6', '7') ~ '2',
TRUE ~ NA_character_
)) %>%
mutate(NewUtbNiva = factor(NewUtbNiva, levels = c('1', '2')))
# Calculate age at FirstDate in years
to1po2$AgeAtFirstDate <- as.numeric(difftime(to1po2$firstDate, to1po2$FODDATn, units = "days") / 365.25)
# Calculate age at LastDate in years
to1po2$AgeAtLastDate <- as.numeric(difftime(to1po2$lastDate, to1po2$FODDATn, units = "days") / 365.25)
```
#======== Association with ADRD and subtypes
```{r}
calc_standardized_beta <- function(model, data, covariate) {
beta <- coef(model)[covariate]
sd_covariate <- sd(data[[covariate]], na.rm = TRUE)
std_beta <- beta * sd_covariate
return(std_beta)
}
```
ADRD
```{r}
# BA measures to be tested
BA_measures <- c("kdm_res", "phenoage_res", "hd_log_res")
# Correct the initialization of HR_BA data frame
HR_BA_adrd <- data.frame(
BA = rep(BA_measures, each = 2), # Repeat each BA measure twice
outcome = rep("ADRD", times = length(BA_measures) * 2),
model = rep(c("Sex-adjusted model", "Multivariable model"), times = length(BA_measures)),
beta = NA, se = NA, HR = NA, lb = NA, ub = NA, p = NA, p_PH_BA = NA, global_p_PH_BA = NA,
concordance = NA, N = NA, std_beta = NA # Add a column for the concordance index
)
for (j in 1:length(BA_measures)) {
# Sex-adjusted model
fit_gender <- coxph(as.formula(paste("Surv(AgeAtFirstDate, AgeAtLastDate, ADRD) ~", BA_measures[j], "+ gender")), data = to1po2)
coef_gender <- summary(fit_gender)
row_index_gender <- (j * 2) - 1
std_beta_gender <- calc_standardized_beta(fit_gender, to1po2, BA_measures[j])
HR_BA_adrd[row_index_gender, c(4, 5, 9, 12)] <- c(coef_gender$coefficients[1, c(1, 3, 5)], coef_gender$concordance[1])
HR_BA_adrd[row_index_gender, 6:8] <- coef_gender$conf.int[1, c(1, 3, 4)]
HR_BA_adrd[row_index_gender, 10] <- cox.zph(fit_gender)$table[1, 3]
HR_BA_adrd[row_index_gender, 11] <- cox.zph(fit_gender)$table[3, 3]
HR_BA_adrd[row_index_gender, "N"] <- coef_gender$n
HR_BA_adrd[row_index_gender, "std_beta"] <- std_beta_gender
# Multivariable model
fit_multivariable <- coxph(as.formula(paste("Surv(AgeAtFirstDate, AgeAtLastDate, ADRD) ~", BA_measures[j], "+ gender + income_bins + NewUtbNiva")), data = to1po2)
coef_multivariable <- summary(fit_multivariable)
row_index_multivariable <- j * 2
std_beta_multivariable <- calc_standardized_beta(fit_multivariable, to1po2, BA_measures[j])
HR_BA_adrd[row_index_multivariable, c(4, 5, 9, 12)] <- c(coef_multivariable$coefficients[1, c(1, 3, 5)], coef_multivariable$concordance[1])
HR_BA_adrd[row_index_multivariable, 6:8] <- coef_multivariable$conf.int[1, c(1, 3, 4)]
HR_BA_adrd[row_index_multivariable, 10] <- cox.zph(fit_multivariable)$table[1, 3]
HR_BA_adrd[row_index_multivariable, 11] <- cox.zph(fit_multivariable)$table[3, 3]
HR_BA_adrd[row_index_multivariable, "N"] <- coef_multivariable$n
HR_BA_adrd[row_index_multivariable, "std_beta"] <- std_beta_multivariable
}
ADRD_results <- HR_BA_adrd
# Print the modified HR_BA_adrd data frame
print(HR_BA_adrd)
```
ADRD based on SveDem
```{r}
# BA measures to be tested
BA_measures <- c("kdm_res", "phenoage_res", "hd_log_res")
# Correct the initialization of HR_BA_adrd data frame
HR_BA_s_adrd <- data.frame(
BA = rep(BA_measures, each = 2), # Repeat each BA measure twice
outcome = rep("SveDem ADRD", times = length(BA_measures) * 2),
model = rep(c("Sex-adjusted model", "Multivariable model"), times = length(BA_measures)),
beta = NA, se = NA, HR = NA, lb = NA, ub = NA, p = NA, p_PH_BA = NA, global_p_PH_BA = NA,
concordance = NA, N=NA, std_beta = NA # Add a column for the concordance index
)
for (j in 1:length(BA_measures)) {
# Sex-adjusted model
fit_gender <- coxph(as.formula(paste("Surv(AgeAtFirstDate, AgeAtLastDate, S_ADRD) ~", BA_measures[j], "+ gender")), data = to1po2)
coef_gender <- summary(fit_gender)
row_index_gender <- (j * 2) - 1
std_beta_gender <- calc_standardized_beta(fit_gender, to1po2, BA_measures[j])
HR_BA_s_adrd[row_index_gender, c(4, 5, 9, 12)] <- c(coef_gender$coefficients[1, c(1, 3, 5)], coef_gender$concordance[1])
HR_BA_s_adrd[row_index_gender, 6:8] <- coef_gender$conf.int[1, c(1, 3, 4)]
HR_BA_s_adrd[row_index_gender, 10] <- cox.zph(fit_gender)$table[1, 3]
HR_BA_s_adrd[row_index_gender, 11] <- cox.zph(fit_gender)$table[3, 3]
HR_BA_s_adrd[row_index_gender, "N"] <- coef_gender$n
HR_BA_s_adrd[row_index_gender, "std_beta"] <- std_beta_gender
# Multivariable model
fit_multivariable <- coxph(as.formula(paste("Surv(AgeAtFirstDate, AgeAtLastDate, S_ADRD) ~", BA_measures[j], "+ gender + income_bins + NewUtbNiva")), data = to1po2)
coef_multivariable <- summary(fit_multivariable)
row_index_multivariable <- j * 2
std_beta_multivariable <- calc_standardized_beta(fit_multivariable, to1po2, BA_measures[j])
HR_BA_s_adrd[row_index_multivariable, c(4, 5, 9, 12)] <- c(coef_multivariable$coefficients[1, c(1, 3, 5)], coef_multivariable$concordance[1])
HR_BA_s_adrd[row_index_multivariable, 6:8] <- coef_multivariable$conf.int[1, c(1, 3, 4)]
HR_BA_s_adrd[row_index_multivariable, 10] <- cox.zph(fit_multivariable)$table[1, 3]
HR_BA_s_adrd[row_index_multivariable, 11] <- cox.zph(fit_multivariable)$table[3, 3]
HR_BA_s_adrd[row_index_multivariable, "N"] <- coef_multivariable$n
HR_BA_s_adrd[row_index_multivariable, "std_beta"] <- std_beta_multivariable
}
S_ADRD_results <- HR_BA_s_adrd
# Print the modified HR_BA_adrd data frame
print(HR_BA_s_adrd)
```
ADRD excluding SveDem
```{r}
# BA measures to be tested
BA_measures <- c("kdm_res", "phenoage_res", "hd_log_res")
# Correct the initialization of HR_BA_adrd data frame
HR_BA_ns_adrd <- data.frame(
BA = rep(BA_measures, each = 2), # Repeat each BA measure twice
outcome = rep("Excluding SveDem ADRD", times = length(BA_measures) * 2),
model = rep(c("Sex-adjusted model", "Multivariable model"), times = length(BA_measures)),
beta = NA, se = NA, HR = NA, lb = NA, ub = NA, p = NA, p_PH_BA = NA, global_p_PH_BA = NA,
concordance = NA, N=NA, std_beta = NA # Add a column for the concordance index
)
for (j in 1:length(BA_measures)) {
# Sex-adjusted model
fit_gender <- coxph(as.formula(paste("Surv(AgeAtFirstDate, AgeAtLastDate, NS_ADRD) ~", BA_measures[j], "+ gender")), data = to1po2)
coef_gender <- summary(fit_gender)
row_index_gender <- (j * 2) - 1
std_beta_gender <- calc_standardized_beta(fit_gender, to1po2, BA_measures[j])
HR_BA_ns_adrd[row_index_gender, c(4, 5, 9, 12)] <- c(coef_gender$coefficients[1, c(1, 3, 5)], coef_gender$concordance[1])
HR_BA_ns_adrd[row_index_gender, 6:8] <- coef_gender$conf.int[1, c(1, 3, 4)]
HR_BA_ns_adrd[row_index_gender, 10] <- cox.zph(fit_gender)$table[1, 3]
HR_BA_ns_adrd[row_index_gender, 11] <- cox.zph(fit_gender)$table[3, 3]
HR_BA_ns_adrd[row_index_gender, "N"] <- coef_gender$n
HR_BA_ns_adrd[row_index_gender, "std_beta"] <- std_beta_gender
# Multivariable model
fit_multivariable <- coxph(as.formula(paste("Surv(AgeAtFirstDate, AgeAtLastDate, NS_ADRD) ~", BA_measures[j], "+ gender + income_bins + NewUtbNiva")), data = to1po2)
coef_multivariable <- summary(fit_multivariable)
row_index_multivariable <- j * 2
std_beta_multivariable <- calc_standardized_beta(fit_multivariable, to1po2, BA_measures[j])
HR_BA_ns_adrd[row_index_multivariable, c(4, 5, 9, 12)] <- c(coef_multivariable$coefficients[1, c(1, 3, 5)], coef_multivariable$concordance[1])
HR_BA_ns_adrd[row_index_multivariable, 6:8] <- coef_multivariable$conf.int[1, c(1, 3, 4)]
HR_BA_ns_adrd[row_index_multivariable, 10] <- cox.zph(fit_multivariable)$table[1, 3]
HR_BA_ns_adrd[row_index_multivariable, 11] <- cox.zph(fit_multivariable)$table[3, 3]
HR_BA_ns_adrd[row_index_multivariable, "N"] <- coef_multivariable$n
HR_BA_ns_adrd[row_index_multivariable, "std_beta"] <- std_beta_multivariable
}
NS_ADRD_results <- HR_BA_ns_adrd
# Print the modified HR_BA_adrd data frame
print(HR_BA_ns_adrd)
```
Alzheimers broad category ie not SveDem
```{r}
# BA measures to be tested
BA_measures <- c("kdm_res", "phenoage_res", "hd_log_res")
# Correct the initialization of HR_BA_adrd data frame
HR_BA_alzh <- data.frame(
BA = rep(BA_measures, each = 2), # Repeat each BA measure twice
outcome = rep("Alzheimers", times = length(BA_measures) * 2),
model = rep(c("Sex-adjusted model", "Multivariable model"), times = length(BA_measures)),
beta = NA, se = NA, HR = NA, lb = NA, ub = NA, p = NA, p_PH_BA = NA, global_p_PH_BA = NA,
concordance = NA, N=NA, std_beta = NA # Add a column for the concordance index
)
for (j in 1:length(BA_measures)) {
# Sex-adjusted model
fit_gender <- coxph(as.formula(paste("Surv(AgeAtFirstDate, AgeAtLastDate, Alzheimers) ~", BA_measures[j], "+ gender")), data = to1po2)
coef_gender <- summary(fit_gender)
row_index_gender <- (j * 2) - 1
std_beta_gender <- calc_standardized_beta(fit_gender, to1po2, BA_measures[j])
HR_BA_alzh[row_index_gender, c(4, 5, 9, 12)] <- c(coef_gender$coefficients[1, c(1, 3, 5)], coef_gender$concordance[1])
HR_BA_alzh[row_index_gender, 6:8] <- coef_gender$conf.int[1, c(1, 3, 4)]
HR_BA_alzh[row_index_gender, 10] <- cox.zph(fit_gender)$table[1, 3]
HR_BA_alzh[row_index_gender, 11] <- cox.zph(fit_gender)$table[3, 3]
HR_BA_alzh[row_index_gender, "N"] <- coef_gender$n
HR_BA_alzh[row_index_gender, "std_beta"] <- std_beta_gender
# Multivariable model
fit_multivariable <- coxph(as.formula(paste("Surv(AgeAtFirstDate, AgeAtLastDate, Alzheimers) ~", BA_measures[j], "+ gender + income_bins + NewUtbNiva")), data = to1po2)
coef_multivariable <- summary(fit_multivariable)
row_index_multivariable <- j * 2
std_beta_multivariable <- calc_standardized_beta(fit_multivariable, to1po2, BA_measures[j])
HR_BA_alzh[row_index_multivariable, c(4, 5, 9, 12)] <- c(coef_multivariable$coefficients[1, c(1, 3, 5)], coef_multivariable$concordance[1])
HR_BA_alzh[row_index_multivariable, 6:8] <- coef_multivariable$conf.int[1, c(1, 3, 4)]
HR_BA_alzh[row_index_multivariable, 10] <- cox.zph(fit_multivariable)$table[1, 3]
HR_BA_alzh[row_index_multivariable, 11] <- cox.zph(fit_multivariable)$table[3, 3]
HR_BA_alzh[row_index_multivariable, "N"] <- coef_multivariable$n
HR_BA_alzh[row_index_multivariable, "std_beta"] <- std_beta_multivariable
}
Alzh_results <- HR_BA_alzh
# Print the modified HR_BA_adrd data frame
print(HR_BA_alzh)
```
SveDem Alzheimers
```{r}
# BA measures to be tested
BA_measures <- c("kdm_res", "phenoage_res", "hd_log_res")
# Correct the initialization of HR_BA_adrd data frame
HR_BA_s_alzh <- data.frame(
BA = rep(BA_measures, each = 2), # Repeat each BA measure twice
outcome = rep("SveDem Alzheimers", times = length(BA_measures) * 2),
model = rep(c("Sex-adjusted model", "Multivariable model"), times = length(BA_measures)),
beta = NA, se = NA, HR = NA, lb = NA, ub = NA, p = NA, p_PH_BA = NA, global_p_PH_BA = NA,
concordance = NA, N=NA, std_beta = NA # Add a column for the concordance index
)
for (j in 1:length(BA_measures)) {
# Sex-adjusted model
fit_gender <- coxph(as.formula(paste("Surv(AgeAtFirstDate, AgeAtLastDate, S_Alzheimers) ~", BA_measures[j], "+ gender")), data = to1po2)
coef_gender <- summary(fit_gender)
row_index_gender <- (j * 2) - 1
std_beta_gender <- calc_standardized_beta(fit_gender, to1po2, BA_measures[j])
HR_BA_s_alzh[row_index_gender, c(4, 5, 9, 12)] <- c(coef_gender$coefficients[1, c(1, 3, 5)], coef_gender$concordance[1])
HR_BA_s_alzh[row_index_gender, 6:8] <- coef_gender$conf.int[1, c(1, 3, 4)]
HR_BA_s_alzh[row_index_gender, 10] <- cox.zph(fit_gender)$table[1, 3]
HR_BA_s_alzh[row_index_gender, 11] <- cox.zph(fit_gender)$table[3, 3]
HR_BA_s_alzh[row_index_gender, "N"] <- coef_gender$n
HR_BA_s_alzh[row_index_gender, "std_beta"] <- std_beta_gender
# Multivariable model
fit_multivariable <- coxph(as.formula(paste("Surv(AgeAtFirstDate, AgeAtLastDate, S_Alzheimers) ~", BA_measures[j], "+ gender + income_bins + NewUtbNiva")), data = to1po2)
coef_multivariable <- summary(fit_multivariable)
row_index_multivariable <- j * 2
std_beta_multivariable <- calc_standardized_beta(fit_multivariable, to1po2, BA_measures[j])
HR_BA_s_alzh[row_index_multivariable, c(4, 5, 9, 12)] <- c(coef_multivariable$coefficients[1, c(1, 3, 5)], coef_multivariable$concordance[1])
HR_BA_s_alzh[row_index_multivariable, 6:8] <- coef_multivariable$conf.int[1, c(1, 3, 4)]
HR_BA_s_alzh[row_index_multivariable, 10] <- cox.zph(fit_multivariable)$table[1, 3]
HR_BA_s_alzh[row_index_multivariable, 11] <- cox.zph(fit_multivariable)$table[3, 3]
HR_BA_s_alzh[row_index_multivariable, "N"] <- coef_multivariable$n
HR_BA_s_alzh[row_index_multivariable, "std_beta"] <- std_beta_multivariable
}
S_Alzh_results <- HR_BA_s_alzh
# Print the modified HR_BA_adrd data frame
print(HR_BA_s_alzh)
```
Alzheimers excluding SveDem
```{r}
# BA measures to be tested
BA_measures <- c("kdm_res", "phenoage_res", "hd_log_res")
# Correct the initialization of HR_BA_adrd data frame
HR_BA_ns_alzh <- data.frame(
BA = rep(BA_measures, each = 2), # Repeat each BA measure twice
outcome = rep("Excluding SveDem Alzheimers", times = length(BA_measures) * 2),
model = rep(c("Sex-adjusted model", "Multivariable model"), times = length(BA_measures)),
beta = NA, se = NA, HR = NA, lb = NA, ub = NA, p = NA, p_PH_BA = NA, global_p_PH_BA = NA,
concordance = NA, N=NA, std_beta = NA # Add a column for the concordance index
)
for (j in 1:length(BA_measures)) {
# Sex-adjusted model
fit_gender <- coxph(as.formula(paste("Surv(AgeAtFirstDate, AgeAtLastDate, NS_Alzheimers) ~", BA_measures[j], "+ gender")), data = to1po2)
coef_gender <- summary(fit_gender)
row_index_gender <- (j * 2) - 1
std_beta_gender <- calc_standardized_beta(fit_gender, to1po2, BA_measures[j])
HR_BA_ns_alzh[row_index_gender, c(4, 5, 9, 12)] <- c(coef_gender$coefficients[1, c(1, 3, 5)], coef_gender$concordance[1])
HR_BA_ns_alzh[row_index_gender, 6:8] <- coef_gender$conf.int[1, c(1, 3, 4)]
HR_BA_ns_alzh[row_index_gender, 10] <- cox.zph(fit_gender)$table[1, 3]
HR_BA_ns_alzh[row_index_gender, 11] <- cox.zph(fit_gender)$table[3, 3]
HR_BA_ns_alzh[row_index_gender, "N"] <- coef_gender$n
HR_BA_ns_alzh[row_index_gender, "std_beta"] <- std_beta_gender
# Multivariable model
fit_multivariable <- coxph(as.formula(paste("Surv(AgeAtFirstDate, AgeAtLastDate, NS_Alzheimers) ~", BA_measures[j], "+ gender + income_bins + NewUtbNiva")), data = to1po2)
coef_multivariable <- summary(fit_multivariable)
row_index_multivariable <- j * 2
std_beta_multivariable <- calc_standardized_beta(fit_multivariable, to1po2, BA_measures[j])
HR_BA_ns_alzh[row_index_multivariable, c(4, 5, 9, 12)] <- c(coef_multivariable$coefficients[1, c(1, 3, 5)], coef_multivariable$concordance[1])
HR_BA_ns_alzh[row_index_multivariable, 6:8] <- coef_multivariable$conf.int[1, c(1, 3, 4)]
HR_BA_ns_alzh[row_index_multivariable, 10] <- cox.zph(fit_multivariable)$table[1, 3]
HR_BA_ns_alzh[row_index_multivariable, 11] <- cox.zph(fit_multivariable)$table[3, 3]
HR_BA_ns_alzh[row_index_multivariable, "N"] <- coef_multivariable$n
HR_BA_ns_alzh[row_index_multivariable, "std_beta"] <- std_beta_multivariable
}
NS_Alzh_results <- HR_BA_ns_alzh
# Print the modified HR_BA_adrd data frame
print(HR_BA_ns_alzh)
```
Vascular
```{r}
# BA measures to be tested
BA_measures <- c("kdm_res", "phenoage_res", "hd_log_res")
# Correct the initialization of HR_BA_adrd data frame
HR_BA_vasc <- data.frame(
BA = rep(BA_measures, each = 2), # Repeat each BA measure twice
outcome = rep("Vascular Dementia", times = length(BA_measures) * 2),
model = rep(c("Sex-adjusted model", "Multivariable model"), times = length(BA_measures)),
beta = NA, se = NA, HR = NA, lb = NA, ub = NA, p = NA, p_PH_BA = NA, global_p_PH_BA = NA,
concordance = NA, N=NA, std_beta = NA # Add a column for the concordance index
)
for (j in 1:length(BA_measures)) {
# Sex-adjusted model
fit_gender <- coxph(as.formula(paste("Surv(AgeAtFirstDate, AgeAtLastDate, Vascular) ~", BA_measures[j], "+ gender")), data = to1po2)
coef_gender <- summary(fit_gender)
row_index_gender <- (j * 2) - 1
std_beta_gender <- calc_standardized_beta(fit_gender, to1po2, BA_measures[j])
HR_BA_vasc[row_index_gender, c(4, 5, 9, 12)] <- c(coef_gender$coefficients[1, c(1, 3, 5)], coef_gender$concordance[1])
HR_BA_vasc[row_index_gender, 6:8] <- coef_gender$conf.int[1, c(1, 3, 4)]
HR_BA_vasc[row_index_gender, 10] <- cox.zph(fit_gender)$table[1, 3]
HR_BA_vasc[row_index_gender, 11] <- cox.zph(fit_gender)$table[3, 3]
HR_BA_vasc[row_index_gender, "N"] <- coef_gender$n
HR_BA_vasc[row_index_gender, "std_beta"] <- std_beta_gender
# Multivariable model
fit_multivariable <- coxph(as.formula(paste("Surv(AgeAtFirstDate, AgeAtLastDate, Vascular) ~", BA_measures[j], "+ gender + income_bins + NewUtbNiva")), data = to1po2)
coef_multivariable <- summary(fit_multivariable)
row_index_multivariable <- j * 2
std_beta_multivariable <- calc_standardized_beta(fit_multivariable, to1po2, BA_measures[j])
HR_BA_vasc[row_index_multivariable, c(4, 5, 9, 12)] <- c(coef_multivariable$coefficients[1, c(1, 3, 5)], coef_multivariable$concordance[1])
HR_BA_vasc[row_index_multivariable, 6:8] <- coef_multivariable$conf.int[1, c(1, 3, 4)]
HR_BA_vasc[row_index_multivariable, 10] <- cox.zph(fit_multivariable)$table[1, 3]
HR_BA_vasc[row_index_multivariable, 11] <- cox.zph(fit_multivariable)$table[3, 3]
HR_BA_vasc[row_index_multivariable, "N"] <- coef_multivariable$n
HR_BA_vasc[row_index_multivariable, "std_beta"] <- std_beta_multivariable
}
Vasc_results <- HR_BA_vasc
# Print the modified HR_BA_adrd data frame
print(HR_BA_vasc)
```
Svedem Vascular
```{r}
# BA measures to be tested
BA_measures <- c("kdm_res", "phenoage_res", "hd_log_res")
# Correct the initialization of HR_BA_adrd data frame
HR_BA_s_vasc <- data.frame(
BA = rep(BA_measures, each = 2), # Repeat each BA measure twice
outcome = rep("SveDem Vascular Dementia", times = length(BA_measures) * 2),
model = rep(c("Sex-adjusted model", "Multivariable model"), times = length(BA_measures)),
beta = NA, se = NA, HR = NA, lb = NA, ub = NA, p = NA, p_PH_BA = NA, global_p_PH_BA = NA,
concordance = NA, N=NA, std_beta = NA # Add a column for the concordance index
)
for (j in 1:length(BA_measures)) {
# Sex-adjusted model
fit_gender <- coxph(as.formula(paste("Surv(AgeAtFirstDate, AgeAtLastDate, S_Vascular) ~", BA_measures[j], "+ gender")), data = to1po2)
coef_gender <- summary(fit_gender)
row_index_gender <- (j * 2) - 1
std_beta_gender <- calc_standardized_beta(fit_gender, to1po2, BA_measures[j])
HR_BA_s_vasc[row_index_gender, c(4, 5, 9, 12)] <- c(coef_gender$coefficients[1, c(1, 3, 5)], coef_gender$concordance[1])
HR_BA_s_vasc[row_index_gender, 6:8] <- coef_gender$conf.int[1, c(1, 3, 4)]
HR_BA_s_vasc[row_index_gender, 10] <- cox.zph(fit_gender)$table[1, 3]
HR_BA_s_vasc[row_index_gender, 11] <- cox.zph(fit_gender)$table[3, 3]
HR_BA_s_vasc[row_index_gender, "N"] <- coef_gender$n
HR_BA_s_vasc[row_index_gender, "std_beta"] <- std_beta_gender
# Multivariable model
fit_multivariable <- coxph(as.formula(paste("Surv(AgeAtFirstDate, AgeAtLastDate, S_Vascular) ~", BA_measures[j], "+ gender + income_bins + NewUtbNiva")), data = to1po2)
coef_multivariable <- summary(fit_multivariable)
row_index_multivariable <- j * 2
std_beta_multivariable <- calc_standardized_beta(fit_multivariable, to1po2, BA_measures[j])
HR_BA_s_vasc[row_index_multivariable, c(4, 5, 9, 12)] <- c(coef_multivariable$coefficients[1, c(1, 3, 5)], coef_multivariable$concordance[1])
HR_BA_s_vasc[row_index_multivariable, 6:8] <- coef_multivariable$conf.int[1, c(1, 3, 4)]
HR_BA_s_vasc[row_index_multivariable, 10] <- cox.zph(fit_multivariable)$table[1, 3]
HR_BA_s_vasc[row_index_multivariable, 11] <- cox.zph(fit_multivariable)$table[3, 3]
HR_BA_s_vasc[row_index_multivariable, "N"] <- coef_multivariable$n
HR_BA_s_vasc[row_index_multivariable, "std_beta"] <- std_beta_multivariable
}
S_Vasc_results <- HR_BA_s_vasc
# Print the modified HR_BA_adrd data frame
print(HR_BA_s_vasc)
```
Exluding SveDem Vascular
```{r}
# BA measures to be tested
BA_measures <- c("kdm_res", "phenoage_res", "hd_log_res")
# Correct the initialization of HR_BA_adrd data frame
HR_BA_ns_vasc <- data.frame(
BA = rep(BA_measures, each = 2), # Repeat each BA measure twice
outcome = rep("Excluding SveDem Vascular Dementia", times = length(BA_measures) * 2),
model = rep(c("Sex-adjusted model", "Multivariable model"), times = length(BA_measures)),
beta = NA, se = NA, HR = NA, lb = NA, ub = NA, p = NA, p_PH_BA = NA, global_p_PH_BA = NA,
concordance = NA, N=NA, std_beta = NA # Add a column for the concordance index
)
for (j in 1:length(BA_measures)) {
# Sex-adjusted model
fit_gender <- coxph(as.formula(paste("Surv(AgeAtFirstDate, AgeAtLastDate, NS_Vascular) ~", BA_measures[j], "+ gender")), data = to1po2)
coef_gender <- summary(fit_gender)
row_index_gender <- (j * 2) - 1
std_beta_gender <- calc_standardized_beta(fit_gender, to1po2, BA_measures[j])
HR_BA_ns_vasc[row_index_gender, c(4, 5, 9, 12)] <- c(coef_gender$coefficients[1, c(1, 3, 5)], coef_gender$concordance[1])
HR_BA_ns_vasc[row_index_gender, 6:8] <- coef_gender$conf.int[1, c(1, 3, 4)]
HR_BA_ns_vasc[row_index_gender, 10] <- cox.zph(fit_gender)$table[1, 3]
HR_BA_ns_vasc[row_index_gender, 11] <- cox.zph(fit_gender)$table[3, 3]
HR_BA_ns_vasc[row_index_gender, "N"] <- coef_gender$n
HR_BA_ns_vasc[row_index_gender, "std_beta"] <- std_beta_gender
# Multivariable model
fit_multivariable <- coxph(as.formula(paste("Surv(AgeAtFirstDate, AgeAtLastDate, NS_Vascular) ~", BA_measures[j], "+ gender + income_bins + NewUtbNiva")), data = to1po2)
coef_multivariable <- summary(fit_multivariable)
row_index_multivariable <- j * 2
std_beta_multivariable <- calc_standardized_beta(fit_multivariable, to1po2, BA_measures[j])
HR_BA_ns_vasc[row_index_multivariable, c(4, 5, 9, 12)] <- c(coef_multivariable$coefficients[1, c(1, 3, 5)], coef_multivariable$concordance[1])
HR_BA_ns_vasc[row_index_multivariable, 6:8] <- coef_multivariable$conf.int[1, c(1, 3, 4)]
HR_BA_ns_vasc[row_index_multivariable, 10] <- cox.zph(fit_multivariable)$table[1, 3]
HR_BA_ns_vasc[row_index_multivariable, 11] <- cox.zph(fit_multivariable)$table[3, 3]
HR_BA_ns_vasc[row_index_multivariable, "N"] <- coef_multivariable$n
HR_BA_ns_vasc[row_index_multivariable, "std_beta"] <- std_beta_multivariable
}
NS_Vasc_results <- HR_BA_ns_vasc
# Print the modified HR_BA_adrd data frame
print(HR_BA_ns_vasc)
```
Other
```{r}
# BA measures to be tested
BA_measures <- c("kdm_res", "phenoage_res", "hd_log_res")
# Correct the initialization of HR_BA_adrd data frame
HR_BA_othe <- data.frame(
BA = rep(BA_measures, each = 2), # Repeat each BA measure twice
outcome = rep("Other Dementia", times = length(BA_measures) * 2),
model = rep(c("Sex-adjusted model", "Multivariable model"), times = length(BA_measures)),
beta = NA, se = NA, HR = NA, lb = NA, ub = NA, p = NA, p_PH_BA = NA, global_p_PH_BA = NA,
concordance = NA, N=NA, std_beta = NA # Add a column for the concordance index
)
for (j in 1:length(BA_measures)) {
# Sex-adjusted model
fit_gender <- coxph(as.formula(paste("Surv(AgeAtFirstDate, AgeAtLastDate, Other) ~", BA_measures[j], "+ gender")), data = to1po2)
coef_gender <- summary(fit_gender)
row_index_gender <- (j * 2) - 1
std_beta_gender <- calc_standardized_beta(fit_gender, to1po2, BA_measures[j])
HR_BA_othe[row_index_gender, c(4, 5, 9, 12)] <- c(coef_gender$coefficients[1, c(1, 3, 5)], coef_gender$concordance[1])
HR_BA_othe[row_index_gender, 6:8] <- coef_gender$conf.int[1, c(1, 3, 4)]
HR_BA_othe[row_index_gender, 10] <- cox.zph(fit_gender)$table[1, 3]
HR_BA_othe[row_index_gender, 11] <- cox.zph(fit_gender)$table[3, 3]
HR_BA_othe[row_index_gender, "N"] <- coef_gender$n
HR_BA_othe[row_index_gender, "std_beta"] <- std_beta_gender
# Multivariable model
fit_multivariable <- coxph(as.formula(paste("Surv(AgeAtFirstDate, AgeAtLastDate, Other) ~", BA_measures[j], "+ gender + income_bins + NewUtbNiva")), data = to1po2)
coef_multivariable <- summary(fit_multivariable)
row_index_multivariable <- j * 2
std_beta_multivariable <- calc_standardized_beta(fit_multivariable, to1po2, BA_measures[j])
HR_BA_othe[row_index_multivariable, c(4, 5, 9, 12)] <- c(coef_multivariable$coefficients[1, c(1, 3, 5)], coef_multivariable$concordance[1])
HR_BA_othe[row_index_multivariable, 6:8] <- coef_multivariable$conf.int[1, c(1, 3, 4)]
HR_BA_othe[row_index_multivariable, 10] <- cox.zph(fit_multivariable)$table[1, 3]
HR_BA_othe[row_index_multivariable, 11] <- cox.zph(fit_multivariable)$table[3, 3]
HR_BA_othe[row_index_multivariable, "N"] <- coef_multivariable$n
HR_BA_othe[row_index_multivariable, "std_beta"] <- std_beta_multivariable
}
Othe_results <- HR_BA_othe
# Print the modified HR_BA_adrd data frame
print(HR_BA_othe)
```
SveDem Other
```{r}
# BA measures to be tested
BA_measures <- c("kdm_res", "phenoage_res", "hd_log_res")
# Correct the initialization of HR_BA_adrd data frame
HR_BA_s_othe <- data.frame(
BA = rep(BA_measures, each = 2), # Repeat each BA measure twice
outcome = rep("SveDem Other Dementia", times = length(BA_measures) * 2),
model = rep(c("Sex-adjusted model", "Multivariable model"), times = length(BA_measures)),
beta = NA, se = NA, HR = NA, lb = NA, ub = NA, p = NA, p_PH_BA = NA, global_p_PH_BA = NA,
concordance = NA, N=NA, std_beta = NA # Add a column for the concordance index
)
for (j in 1:length(BA_measures)) {
# Sex-adjusted model
fit_gender <- coxph(as.formula(paste("Surv(AgeAtFirstDate, AgeAtLastDate, S_other) ~", BA_measures[j], "+ gender")), data = to1po2)
coef_gender <- summary(fit_gender)
row_index_gender <- (j * 2) - 1
std_beta_gender <- calc_standardized_beta(fit_gender, to1po2, BA_measures[j])
HR_BA_s_othe[row_index_gender, c(4, 5, 9, 12)] <- c(coef_gender$coefficients[1, c(1, 3, 5)], coef_gender$concordance[1])
HR_BA_s_othe[row_index_gender, 6:8] <- coef_gender$conf.int[1, c(1, 3, 4)]
HR_BA_s_othe[row_index_gender, 10] <- cox.zph(fit_gender)$table[1, 3]
HR_BA_s_othe[row_index_gender, 11] <- cox.zph(fit_gender)$table[3, 3]
HR_BA_s_othe[row_index_gender, "N"] <- coef_gender$n
HR_BA_s_othe[row_index_gender, "std_beta"] <- std_beta_gender
# Multivariable model
fit_multivariable <- coxph(as.formula(paste("Surv(AgeAtFirstDate, AgeAtLastDate, S_other) ~", BA_measures[j], "+ gender + income_bins + NewUtbNiva")), data = to1po2)
coef_multivariable <- summary(fit_multivariable)
row_index_multivariable <- j * 2
std_beta_multivariable <- calc_standardized_beta(fit_multivariable, to1po2, BA_measures[j])
HR_BA_s_othe[row_index_multivariable, c(4, 5, 9, 12)] <- c(coef_multivariable$coefficients[1, c(1, 3, 5)], coef_multivariable$concordance[1])
HR_BA_s_othe[row_index_multivariable, 6:8] <- coef_multivariable$conf.int[1, c(1, 3, 4)]
HR_BA_s_othe[row_index_multivariable, 10] <- cox.zph(fit_multivariable)$table[1, 3]
HR_BA_s_othe[row_index_multivariable, 11] <- cox.zph(fit_multivariable)$table[3, 3]
HR_BA_s_othe[row_index_multivariable, "N"] <- coef_multivariable$n
HR_BA_s_othe[row_index_multivariable, "std_beta"] <- std_beta_multivariable
}
S_Othe_results <- HR_BA_s_othe
# Print the modified HR_BA_adrd data frame
print(HR_BA_s_othe)
```
Excluding SveDem other
```{r}
# BA measures to be tested
BA_measures <- c("kdm_res", "phenoage_res", "hd_log_res")
# Correct the initialization of HR_BA_adrd data frame
HR_BA_ns_othe <- data.frame(
BA = rep(BA_measures, each = 2), # Repeat each BA measure twice
outcome = rep("Excluding SveDem Other Dementia", times = length(BA_measures) * 2),
model = rep(c("Sex-adjusted model", "Multivariable model"), times = length(BA_measures)),
beta = NA, se = NA, HR = NA, lb = NA, ub = NA, p = NA, p_PH_BA = NA, global_p_PH_BA = NA,
concordance = NA, N=NA, std_beta = NA # Add a column for the concordance index
)
for (j in 1:length(BA_measures)) {
# Sex-adjusted model
fit_gender <- coxph(as.formula(paste("Surv(AgeAtFirstDate, AgeAtLastDate, NS_other) ~", BA_measures[j], "+ gender")), data = to1po2)
coef_gender <- summary(fit_gender)
row_index_gender <- (j * 2) - 1
std_beta_gender <- calc_standardized_beta(fit_gender, to1po2, BA_measures[j])
HR_BA_ns_othe[row_index_gender, c(4, 5, 9, 12)] <- c(coef_gender$coefficients[1, c(1, 3, 5)], coef_gender$concordance[1])
HR_BA_ns_othe[row_index_gender, 6:8] <- coef_gender$conf.int[1, c(1, 3, 4)]
HR_BA_ns_othe[row_index_gender, 10] <- cox.zph(fit_gender)$table[1, 3]
HR_BA_ns_othe[row_index_gender, 11] <- cox.zph(fit_gender)$table[3, 3]
HR_BA_ns_othe[row_index_gender, "N"] <- coef_gender$n
HR_BA_ns_othe[row_index_gender, "std_beta"] <- std_beta_gender
# Multivariable model
fit_multivariable <- coxph(as.formula(paste("Surv(AgeAtFirstDate, AgeAtLastDate, NS_other) ~", BA_measures[j], "+ gender + income_bins + NewUtbNiva")), data = to1po2)
coef_multivariable <- summary(fit_multivariable)
row_index_multivariable <- j * 2
std_beta_multivariable <- calc_standardized_beta(fit_multivariable, to1po2, BA_measures[j])
HR_BA_ns_othe[row_index_multivariable, c(4, 5, 9, 12)] <- c(coef_multivariable$coefficients[1, c(1, 3, 5)], coef_multivariable$concordance[1])
HR_BA_ns_othe[row_index_multivariable, 6:8] <- coef_multivariable$conf.int[1, c(1, 3, 4)]
HR_BA_ns_othe[row_index_multivariable, 10] <- cox.zph(fit_multivariable)$table[1, 3]
HR_BA_ns_othe[row_index_multivariable, 11] <- cox.zph(fit_multivariable)$table[3, 3]
HR_BA_ns_othe[row_index_multivariable, "N"] <- coef_multivariable$n
HR_BA_ns_othe[row_index_multivariable, "std_beta"] <- std_beta_multivariable
}
NS_Othe_results <- HR_BA_ns_othe
# Print the modified HR_BA_adrd data frame
print(HR_BA_ns_othe)
```
Binding
```{r}
# Combine all data frames into one large data frame
combined_df_results <- rbind(
HR_BA_adrd,
HR_BA_s_adrd,
HR_BA_ns_adrd,
HR_BA_alzh,
HR_BA_s_alzh,
HR_BA_ns_alzh,
HR_BA_vasc,
HR_BA_s_vasc,
HR_BA_ns_vasc,
HR_BA_othe,
HR_BA_s_othe,
HR_BA_ns_othe
)
# View the combined data frame
head(combined_df_results)
```
Forest plot phenoage_res
```{r}
library(ggplot2)
library(dplyr)
library(stringr)
# Assuming the dataframe is named 'your_dataframe'
# Filter for "phenoage_res" and add a unique row identifier combining outcome and model
combined_plot_df <- combined_df_results %>%
filter(BA == "phenoage_res") %>%
mutate(unique_id = paste(outcome, model, sep = "_"))
# Creating the combined forest plot
combined_forest_plot <- ggplot(combined_plot_df, aes(y = HR, x = as.factor(unique_id))) +
geom_point(aes(color = model), size = 3) +
geom_errorbar(aes(ymin = lb, ymax = ub), width = 0.2, size = 0.7) +
geom_hline(yintercept = 1, linetype = "dashed", color = "blue", size = 0.7) +
theme_minimal() +
labs(title = "PhenoAge Residuals", y = "Hazard Ratio (HR)", x = "Outcome and Model Type") +
coord_flip() +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 45, hjust = 1, size = 10),
axis.title.x = element_blank())
# Display the combined plot
combined_forest_plot
```
Looking at it separately:
```{r}
library(ggplot2)
library(dplyr)
library(stringr)
# Assuming your dataframe is named 'combined_df_results'
# Filter for "phenoage_res" and add a unique row identifier combining outcome and model
combined_plot_df <- combined_df_results %>%
filter(BA == "phenoage_res") %>%
mutate(unique_id = paste(outcome, model, sep = "_"))
# Function to create a forest plot
create_forest_plot <- function(data, title_suffix) {
ggplot(data, aes(y = HR, x = as.factor(unique_id))) +
geom_point(aes(color = model), size = 3) +
geom_errorbar(aes(ymin = lb, ymax = ub), width = 0.2, size = 0.7) +
geom_hline(yintercept = 1, linetype = "dashed", color = "blue", size = 0.7) +
theme_minimal() +
labs(title = paste("PhenoAge Residuals -", title_suffix), y = "Hazard Ratio (HR)", x = "Outcome and Model Type") +
coord_flip() +
scale_y_continuous(limits = c(0.95, 1.15)) +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 45, hjust = 1, size = 10),
axis.title.x = element_blank())
}
# Outcomes starting with 'S'
outcomes_start_with_S <- combined_plot_df %>%
filter(str_starts(outcome, "S"))
forest_plot_S <- create_forest_plot(outcomes_start_with_S, "SveDem")
forest_plot_S
# Outcomes starting with 'E'
outcomes_start_with_E <- combined_plot_df %>%
filter(str_starts(outcome, "E"))
forest_plot_E <- create_forest_plot(outcomes_start_with_E, "Excluding SveDem")
forest_plot_E
# Outcomes not starting with 'S' or 'E'
outcomes_not_start_with_SE <- combined_plot_df %>%
filter(!str_starts(outcome, "S") & !str_starts(outcome, "E"))
forest_plot_Others <- create_forest_plot(outcomes_not_start_with_SE, "All")
forest_plot_Others
```
#==========================================
#======== Subgroup analysis ===============
#==========================================
```{r}
p_value_results <- data.frame(
Variable = character(),
AgeGroup1 = character(),
AgeGroup2 = character(),
P_Value = numeric(),
stringsAsFactors = FALSE
)
```
## Under 65 vs over 65
ADRD
```{r}
BA_measures <- c("kdm_res", "phenoage_res", "hd_log_res")
# Initialize HR_BA_adrd data frame
HR_BA <- data.frame(
BA = rep(BA_measures, each = 2),
AgeGroup = rep(c("Under 65", "65 and over"), times = length(BA_measures)),
model = rep("ADRD_agegroup", times = 2 * length(BA_measures)),
beta = NA, se = NA, HR = NA,
CI_lower = NA, CI_upper = NA, P_PH_BA = NA, global_p_PH_BA = NA, concordance = NA, N = NA, std_beta = NA
)
for (j in 1:length(BA_measures)) {
# Loop over each BA measure
for (age_group in c("Under 65", "65 and over")) {
# Subset data based on age group
if (age_group == "Under 65") {
data_subset <- to1po2[to1po2$AgeAtLastDate < 65, ]
} else {
data_subset <- to1po2[to1po2$AgeAtLastDate >= 65, ]
}
# Fit Sex-adjusted model
fit_multivariable <- coxph(as.formula(paste("Surv(AgeAtFirstDate, AgeAtLastDate, ADRD) ~", BA_measures[j], "+ gender + income_bins + NewUtbNiva")), data = data_subset)
coef_multivariable <- summary(fit_multivariable)
std_beta <- calc_standardized_beta(fit_multivariable, data_subset, BA_measures[j])
# Determine row index for updating HR_BA
if (age_group == "Under 65") {
row_index <- (j - 1) * 2 + 1
} else {
row_index <- j * 2
}
# Update HR_BA with model results
HR_BA[row_index, c("beta", "se", "HR")] <- coef_multivariable$coefficients[1, c(1, 3, 2)]
HR_BA[row_index, c("CI_lower", "CI_upper")] <- coef_multivariable$conf.int[1, c(3, 4)]
HR_BA[row_index, "P_PH_BA"] <- cox.zph(fit_multivariable)$table[1, 3]
HR_BA[row_index, "global_p_PH_BA"] <- cox.zph(fit_multivariable)$table[3, 3]
HR_BA[row_index, "concordance"] <- coef_multivariable$concordance[1]
HR_BA[row_index, "N"] <- coef_multivariable$n
HR_BA[row_index, "std_beta"] <- std_beta
}
}
ADRD_agegroup <- HR_BA
# Print the updated HR_BA data frame
print(HR_BA)
```
```{r}
# Assuming the rows corresponding to phenoage_res are correctly ordered in HR_BA
phenoage_under65_row <- which(HR_BA$BA == "phenoage_res" & HR_BA$AgeGroup == "Under 65")
phenoage_over65_row <- which(HR_BA$BA == "phenoage_res" & HR_BA$AgeGroup == "65 and over")
# Extract coefficients and standard errors
beta_under65 <- HR_BA$beta[phenoage_under65_row]
se_under65 <- HR_BA$se[phenoage_under65_row]
beta_over65 <- HR_BA$beta[phenoage_over65_row]
se_over65 <- HR_BA$se[phenoage_over65_row]
# Calculate z-statistic
z <- (beta_under65 - beta_over65) / sqrt(se_under65^2 + se_over65^2)
# Calculate p-value
p_value <- 2 * (1 - pnorm(abs(z)))
# Output the result
p_value
# Add the result to the dataframe
p_value_results <- rbind(p_value_results, data.frame(
Variable = "phenoage_res",
AgeGroup1 = "Under 65",
AgeGroup2 = "65 and over",
P_Value = p_value
))
#0.338 not significant
```
SveDem ADRD
```{r}
BA_measures <- c("kdm_res", "phenoage_res", "hd_log_res")
# Initialize HR_BA data frame
HR_BA <- data.frame(
BA = rep(BA_measures, each = 2),
AgeGroup = rep(c("Under 65", "65 and over"), times = length(BA_measures)),
model = rep("S_ADRD_agegroup", times = 2 * length(BA_measures)),
beta = NA, se = NA, HR = NA,
CI_lower = NA, CI_upper = NA, P_PH_BA = NA, global_p_PH_BA = NA, concordance = NA, N = NA, std_beta = NA
)
for (j in 1:length(BA_measures)) {
# Loop over each BA measure
for (age_group in c("Under 65", "65 and over")) {
# Subset data based on age group
if (age_group == "Under 65") {
data_subset <- to1po2[to1po2$AgeAtLastDate < 65, ]
} else {
data_subset <- to1po2[to1po2$AgeAtLastDate >= 65, ]
}
# Fit Sex-adjusted model
fit_multivariable <- coxph(as.formula(paste("Surv(AgeAtFirstDate, AgeAtLastDate, S_ADRD) ~", BA_measures[j], "+ gender + income_bins + NewUtbNiva")), data = data_subset)
coef_multivariable <- summary(fit_multivariable)
std_beta <- calc_standardized_beta(fit_multivariable, data_subset, BA_measures[j])
# Determine row index for updating HR_BA
if (age_group == "Under 65") {
row_index <- (j - 1) * 2 + 1
} else {
row_index <- j * 2
}
# Update HR_BA with model results
HR_BA[row_index, c("beta", "se", "HR")] <- coef_multivariable$coefficients[1, c(1, 3, 2)]
HR_BA[row_index, c("CI_lower", "CI_upper")] <- coef_multivariable$conf.int[1, c(3, 4)]
HR_BA[row_index, "P_PH_BA"] <- cox.zph(fit_multivariable)$table[1, 3]
HR_BA[row_index, "global_p_PH_BA"] <- cox.zph(fit_multivariable)$table[3, 3]
HR_BA[row_index, "concordance"] <- coef_multivariable$concordance[1]
HR_BA[row_index, "N"] <- coef_multivariable$n
HR_BA[row_index, "std_beta"] <- std_beta
}
}
S_ADRD_agegroup <- HR_BA
# Print the updated HR_BA data frame
print(HR_BA)
```
```{r}
# Assuming the rows corresponding to phenoage_res are correctly ordered in HR_BA
phenoage_under65_row <- which(HR_BA$BA == "phenoage_res" & HR_BA$AgeGroup == "Under 65")
phenoage_over65_row <- which(HR_BA$BA == "phenoage_res" & HR_BA$AgeGroup == "65 and over")
# Extract coefficients and standard errors
beta_under65 <- HR_BA$beta[phenoage_under65_row]
se_under65 <- HR_BA$se[phenoage_under65_row]
beta_over65 <- HR_BA$beta[phenoage_over65_row]
se_over65 <- HR_BA$se[phenoage_over65_row]
# Calculate z-statistic
z <- (beta_under65 - beta_over65) / sqrt(se_under65^2 + se_over65^2)
# Calculate p-value
p_value <- 2 * (1 - pnorm(abs(z)))
# Output the result
p_value
# Add the result to the dataframe
p_value_results <- rbind(p_value_results, data.frame(
Variable = "phenoage_res",
AgeGroup1 = "Under 65",
AgeGroup2 = "65 and over",
P_Value = p_value
))
#0.771 not significant
```
Excluding SveDem ADRD
```{r}
BA_measures <- c("kdm_res", "phenoage_res", "hd_log_res")
# Initialize HR_BA data frame
HR_BA <- data.frame(
BA = rep(BA_measures, each = 2),
AgeGroup = rep(c("Under 65", "65 and over"), times = length(BA_measures)),
model = rep("NS_ADRD_agegroup", times = 2 * length(BA_measures)),
beta = NA, se = NA, HR = NA,
CI_lower = NA, CI_upper = NA, P_PH_BA = NA, global_p_PH_BA = NA, concordance = NA, N = NA, std_beta = NA
)
for (j in 1:length(BA_measures)) {
# Loop over each BA measure
for (age_group in c("Under 65", "65 and over")) {
# Subset data based on age group
if (age_group == "Under 65") {
data_subset <- to1po2[to1po2$AgeAtLastDate < 65, ]
} else {
data_subset <- to1po2[to1po2$AgeAtLastDate >= 65, ]
}
# Fit Sex-adjusted model
fit_multivariable <- coxph(as.formula(paste("Surv(AgeAtFirstDate, AgeAtLastDate, NS_ADRD) ~", BA_measures[j], "+ gender + income_bins + NewUtbNiva")), data = data_subset)
coef_multivariable <- summary(fit_multivariable)
std_beta <- calc_standardized_beta(fit_multivariable, data_subset, BA_measures[j])
# Determine row index for updating HR_BA
if (age_group == "Under 65") {
row_index <- (j - 1) * 2 + 1
} else {
row_index <- j * 2
}
# Update HR_BA with model results
HR_BA[row_index, c("beta", "se", "HR")] <- coef_multivariable$coefficients[1, c(1, 3, 2)]
HR_BA[row_index, c("CI_lower", "CI_upper")] <- coef_multivariable$conf.int[1, c(3, 4)]
HR_BA[row_index, "P_PH_BA"] <- cox.zph(fit_multivariable)$table[1, 3]
HR_BA[row_index, "global_p_PH_BA"] <- cox.zph(fit_multivariable)$table[3, 3]
HR_BA[row_index, "concordance"] <- coef_multivariable$concordance[1]
HR_BA[row_index, "N"] <- coef_multivariable$n
HR_BA[row_index, "std_beta"] <- std_beta
}
}
NS_ADRD_agegroup <- HR_BA
# Print the updated HR_BA data frame
print(HR_BA)
```
```{r}
# Assuming the rows corresponding to phenoage_res are correctly ordered in HR_BA
phenoage_under65_row <- which(HR_BA$BA == "phenoage_res" & HR_BA$AgeGroup == "Under 65")
phenoage_over65_row <- which(HR_BA$BA == "phenoage_res" & HR_BA$AgeGroup == "65 and over")
# Extract coefficients and standard errors
beta_under65 <- HR_BA$beta[phenoage_under65_row]
se_under65 <- HR_BA$se[phenoage_under65_row]
beta_over65 <- HR_BA$beta[phenoage_over65_row]
se_over65 <- HR_BA$se[phenoage_over65_row]
# Calculate z-statistic
z <- (beta_under65 - beta_over65) / sqrt(se_under65^2 + se_over65^2)
# Calculate p-value
p_value <- 2 * (1 - pnorm(abs(z)))
# Output the result
p_value
# Add the result to the dataframe
p_value_results <- rbind(p_value_results, data.frame(
Variable = "phenoage_res",
AgeGroup1 = "Under 65",
AgeGroup2 = "65 and over",
P_Value = p_value
))
#0.368 not significant
```
Alzheimers
```{r}
BA_measures <- c("kdm_res", "phenoage_res", "hd_log_res")
# Initialize HR_BA data frame
HR_BA <- data.frame(
BA = rep(BA_measures, each = 2),
AgeGroup = rep(c("Under 65", "65 and over"), times = length(BA_measures)),
model = rep("Alzheimers_agegroup", times = 2 * length(BA_measures)),
beta = NA, se = NA, HR = NA,
CI_lower = NA, CI_upper = NA, P_PH_BA = NA, global_p_PH_BA = NA, concordance = NA, N = NA, std_beta = NA