Evaluating individualized treatment effect predictions: a model-based perspective on discrimination and calibration assessment
个体化处理效应预测的评价：区分度与校准的模型视角AI 解读 ↓
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个体化处理效应预测的评价：区分度与校准的模型视角
- 研究问题
- 对于个体化处理效应预测，如何定义并评价获益区分度与校准表现，尤其是在具有二元结局的随机试验和模型验证中？
- 方法与思路
- 作者在潜在结果框架中定义评价目标，考察已有获益区分度指标，并提出模型化的区分度与校准扩展，以模拟和卒中试验说明。
- 材料陈述的贡献
- 摘要称所提指标在模拟中的偏差与准确性表现最好；重抽样虽能调整乐观偏差但方差较高，作者据此建议使用独立数据验证。
- 阅读关注点
- 应核对指标是否依赖潜在结果预测模型、模型错设时的表现、独立验证样本的使用方式，以及模拟比较和 R 实现的细节。
- 阅读边界
- 所给版本为 arXiv 预印本，同行评审及接收状态未核验；摘要未给具体指标公式及数值，不能把模拟比较外推为普遍优势。
原文摘要
In recent years, there has been a growing interest in the prediction of individualized treatment effects. While there is a rapidly growing literature on the development of such models, there is little literature on the evaluation of their performance. In this paper, we aim to facilitate the validation of prediction models for individualized treatment effects. The estimands of interest are defined as based on the potential outcomes framework, which facilitates a comparison of existing and novel measures. In particular, we examine existing measures of measures of discrimination for benefit (variations of the c-for-benefit), and propose model-based extensions to the treatment effect setting for discrimination and calibration metrics that have a strong basis in outcome risk prediction. The main focus is on randomized trial data with binary endpoints and on models that provide individualized treatment effect predictions and potential outcome predictions. We use simulated data to provide insight into the characteristics of the examined discrimination and calibration statistics under consideration, and further illustrate all methods in a trial of acute ischemic stroke treatment. The results show that the proposed model-based statistics had the best characteristics in terms of bias and accuracy. While resampling methods adjusted for the optimism of performance estimates in the development data, they had a high variance across replications that limited their accuracy. Therefore, individualized treatment effect models are best validated in independent data. To aid implementation, a software implementation of the proposed methods was made available in R.