subjectWeights: Estimation of censoring probabilities at subject specific...

In riskRegression: Risk Regression Models and Prediction Scores for Survival Analysis with Competing Risks

Estimation of censoring probabilities at subject specific times

Description

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

Usage

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 [e-mail]

Examples

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 Sept. 8, 2023, 6:12 p.m.