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.
|Original language||English (US)|
|Number of pages||13|
|State||Published - May 1 2022|
Bibliographical noteFunding Information:
We would like to thank Ilya Lipkovich and Govinda Weerakkody for their useful comments and their careful review of this manuscript, and Dana Schamberger for an editorial review of the manuscript.
© 2022 John Wiley & Sons Ltd.
- baseline observation carried forward
- direct maximum likelihood estimation
- ignorable missingness
PubMed: MeSH publication types
- Journal Article