data.gen: data.gen

In SubgrpID: Patient Subgroup Identification for Clinical Drug Development

data.gen

Description

Function for simulated data generation

Usage

data.gen(
  n,
  k,
  prevalence = sqrt(0.5),
  prog.eff = 1,
  sig2,
  y.sig2,
  rho,
  rhos.bt.real,
  a.constent
)

Arguments

n	Total sample size
k	Number of markers
prevalence	prevalence of predictive biomarkers with values above the cutoff
prog.eff	effect size beta for prognostic biomarker
sig2	standard deviation of each marker
y.sig2	Standard Deviation of the error term in the linear component
rho	rho*sig2 is the entries for covariance matrix between pairs of different k markers
rhos.bt.real	correlation between each prognostic and predictive markers
a.constent	a constant is set such that there is no overall treatment effect

Details

Function for simulated data generation

Value

A list of simulated clinical trial data with heterogeneous prognostic and predictive biomarkers

Examples

n <- 500
k <- 10
prevalence <- sqrt(0.5)
rho<-0.2
sig2 <- 2
rhos.bt.real <- c(0, rep(0.1, (k-3)))*sig2
y.sig2 <- 1
prog.eff <- 0.5
effect.size <- 1
a.constent <- effect.size/(2*(1-prevalence))
ObsData <- data.gen(n=n, k=k, prevalence=prevalence, prog.eff=prog.eff,
                    sig2=sig2, y.sig2=y.sig2, rho=rho,
                    rhos.bt.real=rhos.bt.real, a.constent=a.constent)

SubgrpID documentation built on May 29, 2024, 1:33 a.m.