marginalized.risk.threshold: Compute Maringalized Risk as a Function of S>=s

In marginalizedRisk: Estimating Marginalized Risk

Description

Computes risk of disease conditional on S>=s by marginalizedizing over a covariate vector Z.

Usage

marginalized.risk.threshold(formula, marker.name, data, weights=rep(1, nrow(data)), 
    t, ss=NULL, verbose=FALSE) 

Arguments

formula	A formula for coxph
marker.name	string
data	A data frame containing the phase 2 data
ss	A vector of marker values
weights	Inverse prob sampling weight, optional
t	t is the time at which survival will be assessed
verbose	Boolean

Details

See the vignette file for more details.

Value

If ss is not NULL, a vector of probabilities are returned. If ss is NULL, a matrix of two columns are returned, where the first column is the marker value and the second column is the probabilties.

Examples

#### suppose wt.loss is the marker of interest

if(requireNamespace("survival")) {

library(survival)

dat=subset(lung, !is.na(wt.loss) & !is.na(ph.ecog))
f1=Surv(time, status) ~ ph.ecog + age + sex
ss=quantile(dat$wt.loss, seq(.05,.95,by=0.01))
t0=1000
prob = marginalized.risk.threshold(f1, "wt.loss", dat, t = t0, ss=ss)

plot(ss, prob, type="l", xlab="Weight loss (S>=s)", 
    ylab=paste0("Probability of survival at day ", t0))

}

marginalizedRisk documentation built on Feb. 16, 2021, 5:07 p.m.