Using inverse probability of censoring weighting to estimate hypothetical estimands in clinical trials: Should we implement stabilisation, and if so how?

ABSTRACT: Inverse probability of censoring weighting is an approach used to estimate the hypothetical treatment effect that would have been observed in a clinical trial if certain intercurrent events had not occurred. Despite the unbiased estimates obtained by inverse probability of censoring weighting when its key assumptions are satisfied, large standard errors and wide confidence intervals can be potential concerns. Inverse probability of censoring weighting with unstabilised weights can be simply implemented by calculating the reciprocal of the probability of being uncensored by the intercurrent events. To improve precision, stabilisation can be realised by replacing the numerator in the unstabilised weights with functions of the time and baseline covariates. Here, we aim to investigate whether

SUBMITTER: Xuan J 

PROVIDER: S-EPMC12783383 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature