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

Olarte Parra, Camila, Daniel, Rhian M. ORCID: https://orcid.org/0000-0001-5649-9320, Wright, David and Bartlett, Jonathan W. 2025. Estimating hypothetical estimands with causal inference and missing data estimators in a diabetes trial case study. Biometrics 81 (1) , ujae167. 10.1093/biomtc/ujae167

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Abstract

The 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 randomized treatment, handling rescue treatment and discontinuation of randomized 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 intercurrent event data separately.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Medicine
Publisher:	Oxford University Press
ISSN:	0006-341X
Date of First Compliant Deposit:	10 February 2025
Date of Acceptance:	3 January 2025
Last Modified:	11 Feb 2025 12:00
URI:	https://orca.cardiff.ac.uk/id/eprint/176068

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