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#' Simulate control limit given false signal probability alpha for RA-CUSUM charts
#'
#' False-signal-probability-simulation of Control Limits h for risk-adjusted CUSUM charts
#' @export
#' @import checkmate
#' @import stats
#' @param patient_risks Double. Vector of patient risk scores (individual risk of adverse event)
#' @param n_simulation Integer. Number of simulation runs
#' @param odds_multiplier Double. Odds multiplier of adverse event under the alternative hypothesis (<1 looks for decreases)
#' @param alpha Double. False signal probability of RA-CUSUM
#' @param seed Integer. Seed for RNG
#' @return Returns the control limit for signalling performance change (double)
#' @examples
#'
#' # Patients risks are usually known from Phase I.
#' # If not, these risk scores can be simulated.
#'
#' # define possible patient risk scores
#' risks <- c(0.001, 0.01, 0.1, 0.002, 0.02, 0.2)
#'
#' # sample risk population of size n = 100
#' set.seed(2046)
#' patient_risks <- sample(x = risks, size = 100, replace = TRUE)
#'
#' # simulate control limits for alpha = 0.05
#' racusum_limit_sim(patient_risks,
#' odds_multiplier = 2,
#' n_simulation = 1000,
#' alpha = 0.05,
#' seed = 2046
#' )
racusum_limit_sim <- function(patient_risks, odds_multiplier, n_simulation, alpha, seed = NULL) {
## Check user input ####
assert_numeric(patient_risks, lower = 0, upper = 1, finite = TRUE, any.missing = FALSE, min.len = 1)
assert_numeric(odds_multiplier, lower = 0, finite = TRUE, any.missing = FALSE, len = 1)
if (odds_multiplier < 1) {
# message("CUSUM detects process improvements (odds_multiplier < 1). ")
}
if (odds_multiplier == 1) {
stop("CUSUM detects no process change (odds_multiplier = 1).")
}
assert_integer(as.integer(n_simulation), lower = 1, any.missing = FALSE, len = 1)
assert_numeric(alpha, lower = 0, upper = 1, finite = TRUE, any.missing = FALSE, len = 1)
assert_integer(as.integer(seed), lower = 0, upper = Inf, any.missing = TRUE, max.len = 1)
## Calculate risk distribution ####
n <- length(patient_risks)
freq_score <- table(patient_risks)
freq_score <- as.data.frame(freq_score)
freq <- round(as.numeric(freq_score$Freq / sum(freq_score$Freq)), digits = 1000)
risk <- sort(unique(patient_risks)) # corresponding risk scores
if (sum(freq) != 1) {
warning("Sum of risk frequence != 1.")
}
## Simulate CUSUM runs ####
cs_sim <- function(i) {
p.0 <- sample(risk, size = n, replace = T, prob = freq)
y <- rbinom(n, 1, p.0)
ws <- log(1 / (1 + (odds_multiplier - 1) * p.0)) # success (non death) case
wf <- log(odds_multiplier / (1 + (odds_multiplier - 1) * p.0)) # failure (death) case
w.t <- ifelse(y == 1, wf, ws)
c.t <- vector(mode = "numeric", length = n)
if (odds_multiplier > 1){
c.t[1] <- max(c(0, c.t[1] + w.t[1]))
for (i in 2:n) c.t[i] <- max(c(0, c.t[i - 1] + w.t[i]))
return(max(c.t))
} else {
c.t[1] <- min(c(0, c.t[1] - w.t[1]))
for (i in 2:n) c.t[i] <- min(c(0, c.t[i - 1] - w.t[i]))
return(min(c.t))
}
}
suppressWarnings(RNGversion("3.5.0"))
set.seed(seed)
rl <- lapply(1:n_simulation, cs_sim)
## Estimate Alpha ####
if (odds_multiplier > 1){
q <- quantile(unlist(rl), 1 - alpha)
} else {
q <- quantile(unlist(rl), alpha)
}
return(as.numeric(q))
}
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