R/sim.subject.data.R

In agaye/ESPRESSO.E: Power Analysis and Sample Size Calculation for a model with a main effect environmental exposure

#' 
#' @title Simulates the individual effect related to heterogeneity in baseline disease risk
#' @description The variation in baseline disease risk is assumed to be normally distributed 
#' on a logistic scale. If this parameter is set to 10, the implication is that a 'high risk' 
#' subject (someone at the upper 95 percent entile of population risk) is, all else being equal, 
#' at 10 times the offs of developing disease compared to someone else who is at 'low risk' (at 
#' the lower 5 percent centile of population risk).
#' @param num.obs number of observations to simulate.
#' @param baseline.OR baseline odds ratio for subject on 95 percent population centile versus 5 
#' percentile. This parameter reflects the heterogeneity in disease risk arising from determinantes 
#' that have not been measured or have not been included in the model.
#' @return a numerical vector.
#' @keywords internal
#' @author Gaye A.
#'
sim.subject.data <- function (num.obs = 10000, baseline.OR = 12.36) 
{
    numobs <- num.obs
    baseline.odds <- baseline.OR
    baseline.variance <- (log(baseline.odds)/(2 * qnorm(0.95)))^2
    subject.effect <- rnorm(numobs, 0, sqrt(baseline.variance))
    output <- subject.effect
}