A0528
Title: A comparison of Kaplan-Meier-based inverse probability of censoring weighted regression methods
Authors: Morten Overgaard - Aarhus University (Denmark) [presenting]
Abstract: Weighting with the inverse probability of censoring is an approach to deal with censoring in regression analyses where the outcome may be missing due to right censoring. Three separate approaches involving this idea in a setting where the Kaplan-Meier estimator is used for estimating the censoring probability are compared. In more detail, the three approaches involve weighted regression, regression with a weighted outcome, and regression of a jack-knife pseudo-observation based on a weighted estimator. The asymptotic variance in each case is expressed, allowing for comparisons between each other and to the uncensored case. In terms of low asymptotic variance, a clear winner cannot be found. Which approach will have the lowest asymptotic variance depends on the censoring distribution.
