| weightsKM {dirttee} | R Documentation |
Inverse probability of censoring weights
Description
Computes inverse probability of censoring weights.
Usage
weightsKM(y, delta)
Arguments
y |
numerical vector with right-censored follow-up times |
delta |
numerical vector, same length as y, 1 indicates an event while 0 indicates censoring |
Details
Inverse probability of censoring weights are calculated by dividing the event indicator by the Kaplan-Meier estimator of the censoring time. This leads to zero weights for censored observations, while every uncensored event receives a weight larger than 1, representing several censored observations. In the redistribute-to-the-right approach, the last observation always receives a positive weight such that no weight will be lost. Further details can be found in Seipp et al. (2021).
Value
A data frame with 2 coloumns. The first column consists of usual inverse probability of censoring weights. For the second column, IPC weights modified in a redistribute-to-the-right approach are given.
References
Seipp, A., Uslar, V., Weyhe, D., Timmer, A., & Otto-Sobotka, F. (2021). Weighted expectile regression for right-censored data. Statistics in Medicine, 40(25), 5501-5520.
Examples
data(colcancer)
kw <- weightsKM(colcancer$logfollowup, colcancer$death)