Missing Data Imputation With Baseline Information in Longitudinal Clinical Trials

Abstract

In longitudinal clinical trials, missing data are inevitable despite every effort made to retain patients in the trial. Missing data cause difficulty in the estimation and interpretation of the treatment effect. When the primary objective is to assess the treatment effect in a realistic setting, it is necessary to take into consideration the impact of noncompliance to the treatment regimen. For estimation following the intention-to-treat principle, a return-to-baseline (RTB) approach may be used for continuous endpoints in some longitudinal clinical trials. The RTB approach is based on the assumption that the unobserved outcomes at the end of the trial represent a return to the baseline value, that is, any change observed while on treatment can be expected to wash out after patients drop out. This article describes a statistical approach for RTB analyses. The method for calculating the sample size using RTB approach is presented. A detailed illustration using this RTB approach based on publicly available longitudinal antidepressant clinical trial data is provided. Extensive simulations are presented to evaluate the performance of this RTB approach under various missing data mechanisms. Important limitations regarding the appropriateness of the underlying assumptions of RTB are discussed.

Keywords:

• Baseline observation carried forward
• Longitudinal data
• Multiple imputation
• Return to baseline

Supplementary Materials

The supplementary materials provide the proof of Proposition 1 and additional simulation results.