Hypothetical Estimands in Randomised Controlled Trials: Unifying Causal Inference and Semiparametric Theory
Authors
Term
4. term
Education
Publication year
2025
Submitted on
2025-05-24
Pages
95
Abstract
When targeting the hypothetical estimand in a randomised controlled trial, accounting for intercurrent events in the analysis presents significant challenges as intercurrent events have a confounding effect. This project presents the causal inference workflow in the context of randomised clinical trials. In addition, the project presents the theory of semiparametric models in order to present the targeted learning framework. When determining the efficacy of treatments in terms of the hypothetical estimand, common practice is to use a Mixed Model for Repeated Measures (MMRM). This project proposes the use of Longitudinal Targeted Maximum Likelihood Estimation (LTMLE) for estimating the hypothetical estimand. Through simulations and empirical analysis, we assess how these methodologies manage the impact of varying amounts of intercurrent events on treatment outcomes. Our findings suggest that while MMRM provides an easily interpretable solution, LTMLE offers a more robust solution by more accurately reflecting the causal relationships in the presence of rescue medication and treatment discontinuation.
Keywords
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