sim.dat.tvarying: Simulation Functions for Time-dependent Proportional Hazard...
In kyotil: Utility Functions for Statistical Analysis Report Generation and Monte Carlo Studies

sim.dat.tvarying.two

R Documentation

Simulation Functions for Time-dependent Proportional Hazard Model

Description

sim.dat.tvarying.three simulates from a model with time varing age group variale of three levels, sim.dat.tvarying.two two.

Usage

sim.dat.tvarying.three(n, followup.length, incidence.density, 
    age.sim = c("tvaryinggroup", "baselinegroup", "continuous","bt"),
    random.censoring.rate = 0.05, seed)
    
sim.dat.tvarying.two(n, followup.length, incidence.density, 
    age.sim = c("tvaryinggroup", "baselinegroup", "continuous","bt"), 
    random.censoring.rate = 0.05, seed)

Arguments

`n`	integer. Sample size.
`followup.length`	numeric. Length of followup, in years.
`incidence.density`	numeric. Incidence rate per year.
`age.sim`	string. Choose between one of three possibilities. tvaryinggroup: age group is time-varying covariate; baselinegroup: age group is a baseline covariate; continuous: age is a continuous covariate; bt: age group by treatment interaction uses baseline age group, while age group main effect uses time-dependent age group
`random.censoring.rate`	numeric. Amount of random censoring.
`seed`	integer. Random number generator seed.

Details

In sim.dat.tvarying.three, baseline age is uniformly distributed between 2.0 and 16.0, and divivded into three groups at 6 and 12. In sim.dat.tvarying.two, baseline age is uniformly distributed between 2.0 and 12.0, and divivded into two groups at 6.

Value

Return a data frame with the following columns:

`ptid`	subject identifier
`trt`	treatment indicator 0/1
`for.non.tvarying.ana`	Boolean, used to subset dataset for non-time dependent analysis
`C`	censoring time
`baseline.age`	age years at baseline
`agegrp`	a factor with levels `[0,6)` `[6,12)` `[12,100)`
`baseline.agegrp`	a factor with levels `[0,6)` `[6,12)` `[12,100)`
`tstart`	left bound of time interval
`tstop`	right bound of time interval
`d`	event indicator
`X`	followup time, in years

Author(s)

Youyi Fong

Examples


library(survival)

dat=sim.dat.tvarying.three(n=6000,followup.length=3, incidence.density=0.05, 
    age.sim="tvaryinggroup", seed=1)
f.tvarying = Surv(tstart,tstop,d) ~ trt*agegrp 
f =          Surv(X,d)            ~ trt*baseline.agegrp 
fits=list()
fits[["tvarying"]]=coxph(f.tvarying, dat)
fits[["baseline"]]=coxph(f, subset(dat, for.non.tvarying.ana))
fits

kyotil documentation built on Nov. 28, 2023, 1:09 a.m.