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CodeS1_step0_create_synthetic_dataset.r
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# Start #####################################################################
# Title: Estimating Costs Associated with Disease Model States Using Generalized Linear Models: A Tutorial
# Step 0: Generation of synthetic dataset
# Author: Junwen Zhou
# Date: 1 August 2023
rm(list = ls())
# Set path
# setwd("C:\\XXX\\XXX") # If you don't set it, it will be the directory where you first open the r program
path_output <- getwd()
# Source packages
library(tidyverse)
select <- dplyr::select
# Prepare data ###############################################################
input <- list()
input$base <- list(
age = c(56, 8.1),
male = 0.439,
race = list(white = 0.94,
black = 0.016,
asian = 0.016,
other = 0.022),
townsend = list(q1 = 0.374,
q2 = 0.201,
q3 = 0.163,
q4 = 0.145,
q5 = 0.117),
smoke = list(none = 0.559,
former = 0.332,
current = 0.103),
pa = list(low = 0.148,
moderate = 0.329,
high = 0.327),
unhealthy_diet = 0.335 / (0.335 + 0.643),
bmi = list(underweight = 0.005,
normal = 0.335,
overweight = 0.423,
obesity1 = 0.167,
obesity2 = 0.046,
obesity3 = 0.018),
ldl = c(3.6, 0.8),
hdl = c(1.5, 0.4),
creatinine = c(71.5, 15.1),
sbp = c(137.8, 18.6),
dbp = c(82.4, 10.1),
atht = 0.162,
db = 0.049 ,
cancer = 0.074,
mental = 0.081
)
input$evt <- tibble(mi = 0.01,
stroke = 0.009,
vd = 0.004,
nvd = 0.02) %>%
mutate_all(~(-log( 1- .) / 7.1))
input$cost_p1 <- c(
base = 0.13,
male = 0.92,
cur_age = 1.38,
mi1 = 47.09,
mi2 = 1.76,
mi3 = 1.44,
mi4 = 1.35,
stroke1 = 47.08,
stroke2 = 2.58,
stroke3 = 1.78,
stroke4 = 1.49,
vd1 = 2.32,
nvd1 = 11.4) %>%
log() # Perform natural log transformation easier for matrix calculation
input$cost_p2 <- c(
base = 2102,
male = -65,
cur_age = 173,
mi1 = 3054,
mi2 = 670,
mi3 = 304,
mi4 = 304,
stroke1 = 4485,
stroke2 = 2192,
stroke3 = 833,
stroke4 = 833,
vd1 = 4318,
nvd1 = 4792)
# Simulate participant ----
set.seed(1234)
# > Simulate baseline ----
f_simulate_patient <- function(npat, input_base){
para_n <- map_dbl(input_base, ~length(.x))
para_type <- ifelse(para_n == 1, "Binary", ifelse(para_n == 2, "Continuous", "Categorical"))
dat_con <- input_base[para_type == "Continuous"]
dat_bin <- input_base[para_type == "Binary"]
dat_cat <- map(input_base[para_type == "Categorical"],
~bind_cols(.x) %>%
gather(key = "cat", value = "prob") %>%
mutate(prob = prob / sum(prob)))
pat_con <- map(dat_con, ~rnorm(n = npat, mean = .x[[1]], sd = .x[[2]]))
neg_con <- map_dbl(pat_con, ~sum(.x <0))
pat_con[neg_con>0] <- map(pat_con[neg_con>0], ~ifelse(.x <0, min(.x[.x > 0]), .x))
pat_bin <- map(dat_bin, ~sample(c("1","0"), npat, replace=TRUE, prob=c(.x,1-.x)))
pat_cat <- map(dat_cat, ~sample(.x$cat, npat, replace=TRUE, prob= .x$prob))
output <- bind_cols(id = 1:npat, c(pat_con, pat_bin, pat_cat)[names(input_base)])
return(output)
}
pat_base <- f_simulate_patient(10000, input$base)
# > Simulate incident event ----
f_simulate_event <- function(npat = 10000, nyear = 10, input_evt = input$evt){
evt_t <- map(input_evt, ~rexp(npat, rate = .x))
evt_year <- map(evt_t, ~ tibble(id = 1:npat, evtyear = ifelse(.x > nyear, nyear + 1, ceiling(.x))))
lastyear <- map2_dbl(evt_year$vd$evtyear, evt_year$nvd$evtyear, ~min(.x, .y, nyear))
pat_year <- expand_grid(id = 1:npat, year = 1:nyear) %>%
left_join(tibble(id = 1:npat, yearlast = lastyear), by = "id") %>%
filter(year <= yearlast) %>%
select(id, year)
pat_evt_year <- map2(evt_year, names(evt_year),
~left_join(pat_year, .x, by = "id") %>%
mutate(evtyear2 = ifelse(year >= evtyear, 1, 0)) %>%
group_by(id) %>%
arrange(id, year) %>%
mutate(evtyear3 = cumsum(evtyear2)) %>%
ungroup() %>%
arrange(id, year) %>%
pull(evtyear3))
output <- bind_cols(pat_year, pat_evt_year)
return(output)
}
pat_evt <- f_simulate_event(npat = 10000, nyear = 10, input_evt = input$evt)
# > Simulate costs ----
f_simulate_cost <- function(sim_pat = pat_base %>% select(id, age, male),
sim_evt = pat_evt,
input_p1 = input$cost_p1,
input_p2 = input$cost_p2){
mx_evt <- left_join(sim_evt, sim_pat, by = "id") %>%
transmute(male = male,
cur_age = (age + year - 1 - 60) / 10,
mi1 = mi == 1,
mi2 = mi == 2,
mi3 = mi == 3,
mi4 = mi >= 4,
stroke1 = stroke == 1,
stroke2 = stroke == 2,
stroke3 = stroke == 3,
stroke4 = stroke >= 4,
vd1 = vd == 1,
nvd1 = nvd == 1) %>%
mutate_all(~as.numeric(.)) %>%
mutate(base = 1) %>%
relocate(base) %>%
as.matrix()
pat_year_odd <- exp(mx_evt %*% input_p1)
pat_year_prob <- pat_year_odd / (pat_year_odd + 1)
pat_year_cost <- mx_evt %*% input_p2
npatyr <- nrow(mx_evt)
pat_year_cost_p1 <- ifelse(runif(npatyr) < pat_year_prob, 1, 0)
pat_year_cost_p2 <- rnorm(npatyr, pat_year_cost, pat_year_cost / 2)
pat_year_cost_p2 <- ifelse(pat_year_cost_p2 < 0, 0, pat_year_cost_p2)
output <- bind_cols(
sim_evt %>% select(id, year),
cost = (pat_year_cost_p1 * pat_year_cost_p2) %>% as.vector()
)
return(output)
}
pat_cost <- f_simulate_cost(sim_pat = pat_base %>% select(id, age, male),
sim_evt = pat_evt,
input_p1 = input$cost_p1,
input_p2 = input$cost_p2)
output <- left_join(pat_base, pat_evt, by = "id") %>%
left_join(pat_cost, by = c("id", "year")) %>%
relocate(id, year) %>%
mutate(cur_age = age + year - 1)
saveRDS(output, file.path(path_output, "step0_ana.rds"))