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A tutorial on individualized treatment effect prediction from randomized trials with a binary endpoint

Hoogland, Jeroen; IntHout, Joanna; Belias, Michail; Rovers, Maroeska M.; Riley, Richard D.; Harrell Jr, Frank E.; Moons, Karel G. M.; Debray, Thomas P. A.; Reitsma, Johannes B.

A tutorial on individualized treatment effect prediction from randomized trials with a binary endpoint Thumbnail


Jeroen Hoogland

Joanna IntHout

Michail Belias

Maroeska M. Rovers

Richard D. Riley

Frank E. Harrell Jr

Karel G. M. Moons

Thomas P. A. Debray

Johannes B. Reitsma


Randomized trials typically estimate average relative treatment effects, but decisions on the benefit of a treatment are possibly better informed by more individualized predictions of the absolute treatment effect. In case of a binary outcome, these predictions of absolute individualized treatment effect require knowledge of the individual's risk without treatment and incorporation of a possibly differential treatment effect (ie, varying with patient characteristics). In this article, we lay out the causal structure of individualized treatment effect in terms of potential outcomes and describe the required assumptions that underlie a causal interpretation of its prediction. Subsequently, we describe regression models and model estimation techniques that can be used to move from average to more individualized treatment effect predictions. We focus mainly on logistic regression-based methods that are both well-known and naturally provide the required probabilistic estimates. We incorporate key components from both causal inference and prediction research to arrive at individualized treatment effect predictions. While the separate components are well known, their successful amalgamation is very much an ongoing field of research. We cut the problem down to its essentials in the setting of a randomized trial, discuss the importance of a clear definition of the estimand of interest, provide insight into the required assumptions, and give guidance with respect to modeling and estimation options. Simulated data illustrate the potential of different modeling options across scenarios that vary both average treatment effect and treatment effect heterogeneity. Two applied examples illustrate individualized treatment effect prediction in randomized trial data.

Journal Article Type Article
Acceptance Date Jul 19, 2021
Online Publication Date Aug 16, 2021
Publication Date Nov 20, 2021
Publicly Available Date May 30, 2023
Journal Statistics in Medicine
Print ISSN 0277-6715
Publisher Wiley
Volume 40
Issue 26
Pages 5961-5981
Keywords causal inference, personalized medicine, prediction, regression, treatment effect
Publisher URL