sim.fong: Data Simulation as in Fong and Gilbert (2014)

In sptm: SemiParametric Transformation Model Methods

Description

Simulate data as in Fong and Gilbert (2014).

Usage

sim.fong (n, family=c("PH","PO","P2"), beta, 
    random.censoring=c("0%","20%","60%"), prevalence=0.1, non.adherence.ratio=0,
    design=c("FULL","CC"), auxiliary=c("weak","good","excellent","none"), 
    seed=NULL, var.S=1, var.W=1) 

Arguments

n	integer. Sample size
family	string. Link functions in the semiparametric transformation model
beta	numerical vector. Coefficients of the linear model
random.censoring	string. Random censoring in addition to administrative censoring
prevalence	numerical. Proportion of cases among z==0 when there is no random censoring and non-adherence ratio is 0
design	string. Full cohort or case-cohort (finite population sampling)
auxiliary	string.
seed	integer. Random generator seed
var.S	numeric. Variance of the phase II covariate s
var.W	numeric. Variance of the baseline covariate w
non.adherence.ratio	ratio of non-adherent

Details

The number of rows is the size of the full cohort. Adherence ratio works as a Bernoulli variable. Prevalence is used to compute baseline hazard function based on some empirical evidence.

Value

If design is FULL, returns a data frame of:

ft	failure time
C	censoring time
X	smaller of the ft and C
d	event indicator
z	baseline covariate z
s	phase II covariate s

If design is CC, returns a data frame of:

ft	failure time
C	censoring time
X	smaller of the ft and C
d	event indicator
z	baseline covariate z
s	phase II covariate s
w	baseline auxiliary covariate w

Examples

dat = sim.fong(n=10000, family="PH", beta=c(log(.5), log(.7), log(1.2)), design="CC", 
    auxiliary="weak", seed=1, prevalence=0.1, non.adherence.ratio=0, random.censoring="0")
mean(dat$d[dat$z==0])

dat = sim.fong(n=10000, family="PH", beta=c(log(.5), log(.7), log(1.2)), design="CC", 
    auxiliary="weak", seed=1, prevalence=0.1, non.adherence.ratio=0.15, random.censoring="0")
sum(dat$d & !is.na(dat$s))
sum(!dat$d & !is.na(dat$s)) / sum(dat$d & !is.na(dat$s))

dat = sim.fong(n=10000, family="PH", beta=c(log(.5), log(.7), log(1.2)), design="CC", 
    auxiliary="weak", seed=1, prevalence=0.1, non.adherence.ratio=0.15, random.censoring="20")
sum(dat$d & !is.na(dat$s))
sum(!dat$d & !is.na(dat$s)) / sum(dat$d & !is.na(dat$s))

sptm documentation built on Dec. 1, 2019, 1:08 a.m.