# 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

 ```1 2``` ```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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16``` ```#### 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.