Return-to-baseline multiple imputation for missing values in clinical trials

TL;DR

This paper introduces a new return-to-baseline multiple imputation method for missing data in clinical trials, which reduces bias and variance compared to traditional approaches, especially when missingness depends on observed data.

Contribution

The paper proposes a novel return-to-baseline imputation method that satisfies specific criteria and improves bias and variance in the presence of dependent missingness.

Findings

The new method maintains proper data distribution after imputation.

It outperforms traditional methods in bias reduction.

It is easily implementable with existing statistical software.

Abstract

Return-to-baseline is an important method to impute missing values or unobserved potential outcomes when certain hypothetical strategies are used to handle intercurrent events in clinical trials. Current return-to-baseline approaches seen in literature and in practice inflate the variability of the "complete" dataset after imputation and lead to biased mean estimators {when the probability of missingness depends on the observed baseline and/or postbaseline intermediate outcomes}. In this article, we first provide a set of criteria a return-to-baseline imputation method should satisfy. Under this framework, we propose a novel return-to-baseline imputation method. Simulations show the completed data after the new imputation approach have the proper distribution, and the estimators based on the new imputation method outperform the traditional method in terms of both bias and variance, when missingness depends on the observed values. The new method can be implemented easily with the existing multiple imputation procedures in commonly used statistical packages.