A tutorial on individualized treatment effect prediction from randomized trials with a binary endpoint
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随机试验中二元结局个体化处理效应预测教程
- 研究问题
- 随机试验通常报告平均相对效应，如何结合患者未处理风险与效应异质性，形成具有明确因果含义的个体化绝对处理效应预测？
- 方法与思路
- 作者用潜在结果表述目标与因果解释条件，主要介绍基于逻辑回归的建模和估计选择，并通过不同异质性情景及两个试验实例说明。
- 材料陈述的贡献
- 摘要称，该教程整合因果推断与预测研究的关键组成，明确目标量和所需假设，为从平均效应转向个体化预测提供方法指导。
- 阅读关注点
- 应核对个体化目标量是否指协变量条件平均效应、基线风险与交互项如何建模，以及预测验证和因果解释分别依赖哪些条件。
- 阅读边界
- 这是预印本，同行评审或期刊接收未经核实。摘要未列出具体假设与性能结果，个体化预测也不能据此理解为识别每个人的真实效应。
原文摘要
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 (i.e. varying with patient characteristics). In this paper 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 illustrates 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.