R/nwts2ph.generator.R

In katehu/addhazard: Fit Additive Hazards Models for Survival Analysis

###############################################################################
## Author: Kate HU 
## Author: Kate HU 
## Date: March 13th, 2017
###############################################################################
## Author: Kate HU 
#' This file genreate the example dataset nwts2ph
#' importFrom('stats', 'rbinom')
#' @param data the full cohort data
#' @param seed the random seed we use for generating this dataset
#' @importFrom stats rbinom
#' @export nwts2ph.generate
nwts2ph.generate <- function(data, seed = 20) {
  # Create a hypothetical two-phase sampling (stratified sampling) dataset.  In this
  # dataset, only people who have relapse and some of the controls have their samples sent
  # to the central laboratory for histology determination
  set.seed(seed)
  nwts2ph <- data
  
  # Institutional histology is examined in the local hospital thus it is reasonable to
  # assume it is measured for all the samples.  Central histology is more expensive to
  # obtain since the samples have to be sent to the central laboratory and the work
  # requires experienced lab scientists thus it is reasonable to assume only some samples
  # were tested for central histology. Based on the outcomes and institutional histology,
  # we determine who will be selected for central histology measurement, which follows
  # two-phase sampling* framework.
  
  # create strata based on institutional histology and disease status
  nwts2ph$strt <- 1 + nwts2ph$instit  ### add a stratum containing all (relapsed) cases
  nwts2ph$strt[nwts2ph$relaps == 1] <- 3
  
  # phase II samples are obtained by Bernoulli Sampling we oversample unfavorable
  # institutional histology (instit = 1) and cases selection probability Pi = 0.5 for
  # instit = 0, Pi = 0.9 for instit = 1 and relaps = 1 assign phase II subsampling
  # (Bernoulli) probabilities
  nwts2ph$Pi <- 0.5 * (nwts2ph$strt == 1) + 0.9 * (nwts2ph$strt == 2) + 0.9 * (nwts2ph$strt == 
                                                                                 3)
  nwts2ph$wts = 1/nwts2ph$Pi
  # generate phase II sampling indicators
  N <- dim(nwts2ph)[1]
  nwts2ph$in.ph2 <- rbinom(N, 1, nwts2ph$Pi)
  
  # update the central histology variable. Those who were not selected into phase II
  # subsamples are assumed not have central histology measurement
  nwts2ph$histol[nwts2ph$in.ph2 == 0] <- NA
  return(nwts2ph)
}

# generate the data nwts2ph <- nwts2ph.generate(nwtsco) save the data
# devtools::use_data(nwts2ph, overwrite = TRUE)