Bayesian Approaches for Handling Hypothetical Estimands in Longitudinal Clinical Trials With Gaussian Outcomes

Abstract

The International Council for Harmonisation (ICH) recently published an E9(R1) addendum that requires the estimand associated with the study objective in clinical trials to be clearly defined. One of the challenges in defining an estimand is the estimand’s handling of intercurrent events (ICEs) that affect the collection or interpretation of the data for the study. Among the strategies for handling ICEs, sponsors may prefer to examine hypothetical strategies that assess the theoretical or attributable efficacy of test drugs or biologic products. We first look at several estimands under different hypothetical treatment conditions of interest. For these estimands, the data after ICEs are ignored and treated as missing. Analyses are carried out with missing data assumptions under missing at random, control-based imputation, and return-to-baseline imputation. With the explicit forms of these hypothetical estimands derived, we investigate Bayesian approaches to obtain corresponding point and interval estimates and propose a Bayesian sensitivity analysis which avoids the information positive problem. The methods are illustrated with applications to three clinical trials.

Keywords:

• Bayesian
• Control-based imputation
• Hypothetical estimand
• Missing data
• Return-to-baseline

Supplementary Materials

1. Sample SAS codes and SAS macros;

2. Sample R-stan codes.