Standard and reference-based conditional mean imputation

TL;DR

This paper introduces a deterministic conditional mean imputation method combined with jackknife inference for handling missing data in clinical trials, providing consistent treatment effect estimates and reliable inference without reliance on random sampling.

Contribution

It proposes a novel deterministic imputation approach that aligns with Bayesian and reference-based methods, offering improved reproducibility and accurate inference in missing data scenarios.

Findings

Provides consistent treatment effect estimates comparable to Bayesian methods

Achieves reliable frequentist inference with accurate standard errors

Eliminates Monte Carlo error and enhances reproducibility

Abstract

Clinical trials with longitudinal outcomes typically include missing data due to missed assessments or structural missingness of outcomes after intercurrent events handled with a hypothetical strategy. Approaches based on Bayesian random multiple imputation and Rubin's rule for pooling results across multiple imputed datasets are increasingly used in order to align the analysis of these trials with the targeted estimand. We propose and justify deterministic conditional mean imputation combined with the jackknife for inference as an alternative approach. The method is applicable to imputations under a missing-at-random assumption as well as for reference-based imputation approaches. In an application and a simulation study, we demonstrate that it provides consistent treatment effect estimates with the Bayesian approach and reliable frequentist inference with accurate standard error estimation and type I error control. A further advantage of the method is that it does not rely on random sampling and is therefore replicable and unaffected by Monte Carlo error.