Estimating hypothetical estimands with causal inference and missing data estimators in a diabetes trial

TL;DR

This paper compares various causal inference methods for estimating treatment effects in a diabetes trial using the hypothetical estimand strategy, addressing missing data and intercurrent events.

Contribution

It demonstrates how multiple estimation techniques can be applied under the ICH E9 addendum framework, providing practical guidance and implementation details in R.

Findings

Estimates were broadly similar across methods

Different methods have distinct assumptions and practical considerations

Handling missing data and intercurrent events is crucial for accurate estimation

Abstract

The recently published ICH E9 addendum on estimands in clinical trials provides a framework for precisely defining the treatment effect that is to be estimated, but says little about estimation methods. Here we report analyses of a clinical trial in type 2 diabetes, targeting the effects of randomised treatment, handling rescue treatment and discontinuation of randomised treatment using the so-called hypothetical strategy. We show how this can be estimated using mixed models for repeated measures, multiple imputation, inverse probability of treatment weighting, G-formula and G-estimation. We describe their assumptions and practical details of their implementation using packages in R. We report the results of these analyses, broadly finding similar estimates and standard errors across the estimators. We discuss various considerations relevant when choosing an estimation approach, including computational time, how to handle missing data, whether to include post intercurrent event data in the analysis, whether and how to adjust for additional time-varying confounders, and whether and how to model different types of ICE separately.