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. In this paper, 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. Expressions of the asymptotic variances are given in each case and the expressions are compared to 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. Expressions of the limit of the standard sandwich variance estimator in the three cases are also provided, revealing an overestimation under the implied assumptions.

翻译：逆删失概率加权是处理回归分析中因右删失导致结果缺失的一种方法。本文比较了在采用Kaplan-Meier估计器估计删失概率的设定下，基于该思想的三种不同方法。具体而言，这三种方法包括：加权回归、加权结果回归，以及基于加权估计器的刀切法伪观测值回归。我们给出了每种方法的渐近方差表达式，并将这些表达式相互比较，同时与未删失情形进行对比。就低渐近方差而言，未能发现明显优势方法。何种方法具有最低渐近方差取决于删失分布。本文还提供了三种情形下标准三明治方差估计量极限的表达式，揭示了在隐含假设下存在高估现象。