# subjectWeights: Estimation of censoring probabilities at subject specific... In riskRegression: Risk Regression Models and Prediction Scores for Survival Analysis with Competing Risks

## Description

This function is used internally to contruct pseudo values by inverse of the probability of censoring weights.

## Usage

 ```1 2 3 4 5 6 7``` ```subjectWeights( formula, data, method = c("cox", "marginal", "km", "nonpar", "forest", "none"), args, lag = 1 ) ```

## Arguments

 `formula` A survival formula like, Surv(time,status)~1 or Hist(time,status)~1 where status=0 means censored. The status variable is internally reversed for estimation of censoring rather than survival probabilities. Some of the available models, see argument `model`, will use predictors on the right hand side of the formula. `data` The data used for fitting the censoring model `method` Censoring model used for estimation of the (conditional) censoring distribution. `args` Arguments passed to the fitter of the method. `lag` If equal to `1` then obtain `G(T_i-|X_i)`, if equal to `0` estimate the conditional censoring distribution at the subject.times, i.e. (`G(T_i|X_i)`).

## Details

Inverse of the probability of censoring weights usually refer to the probabilities of not being censored at certain time points. These probabilities are also the values of the conditional survival function of the censoring time given covariates. The function subjectWeights estimates the conditional survival function of the censoring times and derives the weights.

IMPORTANT: the data set should be ordered, `order(time,-status)` in order to get the `weights` in the right order for some choices of `method`.

## Value

 `times` The times at which weights are estimated `weights` Estimated weights at individual time values `subject.times` `lag` The time lag. `fit` The fitted censoring model `method` The method for modelling the censoring distribution `call` The call

## Author(s)

Thomas A. Gerds tag@biostat.ku.dk

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23``` ```library(prodlim) library(survival) dat=SimSurv(300) dat <- dat[order(dat\$time,-dat\$status),] # using the marginal Kaplan-Meier for the censoring times WKM=subjectWeights(Hist(time,status)~X2,data=dat,method="marginal") plot(WKM\$fit) WKM\$fit WKM\$weights # using the Cox model for the censoring times given X2 WCox=subjectWeights(Surv(time,status)~X2,data=dat,method="cox") WCox plot(WCox\$weights,WKM\$weights) # using the stratified Kaplan-Meier for the censoring times given X2 WKM2 <- subjectWeights(Surv(time,status)~X2,data=dat,method="nonpar") plot(WKM2\$fit,add=FALSE) ```

riskRegression documentation built on Jan. 13, 2021, 11:12 a.m.