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

## TL;DR

This paper explores whether stabilizing inverse probability of censoring weighting improves clinical trial estimates and how to best implement it.

## Contribution

The study investigates optimal stabilization methods for inverse probability of censoring weighting in clinical trials.

## Key findings

- Stabilization improves estimator efficiency, especially with baseline covariates.
- Stabilization can increase bias if the outcome model is mis-specified.
- Performance varies with intercurrent event prevalence and model specification.

## 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 stabilised weight is a preferred choice and if so how we should specify the numerator. In a simulation study, we assessed the performance of inverse probability of censoring weighting implementations with unstabilised weights and with different forms of stabilisation when the outcome analysis model was correctly specified or mis-specified. Scenarios were designed to vary the prevalence of the intercurrent event in one or both randomised arms, the existence of a deterministic intercurrent event, the indirect effect through baseline covariates and overall treatment effect, the existence and the pattern of time-varying effect and sample size. Results show that compared with unstabilised weights, stabilisation improves the efficiency of the inverse probability of censoring weighting estimator in most cases and the improvement is obvious when we stabilise for the baseline covariates. However, stabilisation risks increasing the bias when the outcome analysis model is mis-specified.