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A Tutorial on Individualized Treatment Effect Prediction from Randomized Trials with a Binary Endpoint by 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 is a Mathematics article available to read on EtoBox.
What is A Tutorial on Individualized Treatment Effect Prediction from Randomized Trials with a Binary Endpoint about?
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 amalgamati
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It is typically read by researchers, students, and practitioners in Mathematics.
- Author
- 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
- Publisher
- Wiley
- Published
- 2021
- Language
- EN
- Field
- Mathematics (Physical Sciences)