Counterfactual clinical prediction models could help to infer individualized treatment effects in randomized controlled trials-An illustration with the International Stroke Trial
Counterfactual clinical prediction models could help to infer individualized treatment effects in randomized controlled trials-An illustration with the International Stroke Trial
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DOI：
10.1016/j.jclinepi.2020.05.022
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发表时间：
2020-09-01
影响因子：
7.2
通讯作者：
Le Manach, Yannick
中科院分区：
文献类型：
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作者：
Nguyen, Tri-Long;Collins, Gary S.;Le Manach, Yannick
目的：在随机对照试验中，因果治疗效应是在群体水平上估计的，而在实践中，临床决策往往是在个体水平上做出的。我们的目标是显示如何在反事实框架下使用的临床预测模型可能有助于推断个性化的治疗effects.Study设计和设置：作为一个说明性的例子，我们重新分析国际中风试验。这项大型多中心试验招募了来自36个国家的19，435名疑似急性缺血性卒中的成年患者，并报告了阿司匹林（与无阿司匹林相比）在6个月时死亡或依赖性复合结局方面的中等平均获益。我们推导和验证多变量逻辑回归模型，预测病人的反事实风险的结果与阿司匹林，有条件的23 predictor.Results：反事实预测模型显示良好的性能，在校准和歧视（验证C -统计：0.798和0.794）。比较反事实的预测风险的绝对差异规模，我们表明，阿司匹林-尽管平均效益-可能会增加死亡或依赖的风险在6个月（与对照组相比）在四分之一的中风patients.Conclusions：反事实的预测模型可以帮助研究人员和临床医生（i）推断个性化的治疗效果和（ii）更好的目标患者谁可能受益于治疗。(C)2020爱思唯尔公司All rights reserved.
Objective: Causal treatment effects are estimated at the population level in randomized controlled trials, while clinical decision is often to be made at the individual level in practice. We aim to show how clinical prediction models used under a counterfactual framework may help to infer individualized treatment effects.Study Design and Setting: As an illustrative example, we reanalyze the International Stroke Trial. This large, multicenter trial enrolled 19,435 adult patients with suspected acute ischemic stroke from 36 countries, and reported a modest average benefit of aspirin (vs. no aspirin) on a composite outcome of death or dependency at 6 months. We derive and validate multivariable logistic regression models that predict the patient counterfactual risks of outcome with and without aspirin, conditionally on 23 predictors.Results: The counterfactual prediction models display good performance in terms of calibration and discrimination (validation c -statistics: 0.798 and 0.794). Comparing the counterfactual predicted risks on an absolute difference scale, we show that aspirin -despite an average benefit -may increase the risk of death or dependency at 6 months (compared with the control) in a quarter of stroke patients.Conclusions: Counterfactual prediction models could help researchers and clinicians (i) infer individualized treatment effects and (ii) better target patients who may benefit from treatments. (C) 2020 Elsevier Inc. All rights reserved.