估计随机试验中的异质性治疗效果：风险建模与治疗效果建模方法的比较

中文摘要 AI 辅助

引言 风险建模（RM）和治疗效果建模（EM）是两种从随机对照试验（RCT）中构建模型以估计异质性治疗效果的方法。RM是一种两阶段方法；它在第一阶段估计预测的基线风险，然后在第二阶段将其作为唯一的效应修饰因子纳入回归模型。EM是一个完整的治疗交互多变量模型。两种方法都有理论上的优势和局限性，但缺乏包括模拟研究在内的全面比较。方法 在对这两种方法的理论回顾中，我们介绍了它们的基本假设以及理论上的优缺点。基于我们的理论回顾，我们设计了针对具有二分结果的RCT的模拟场景，并评估了两种方法在预测风险差异的均方根误差和偏差方面的表现。结果 在理论部分，我们认为在许多临床情况下，基线风险是治疗效果的修饰因子。RM是一种降维方法，但对预后因素在修饰治疗效果中的作用做出了强有力的假设。EM的更大灵活性在样本量较大时可能是一个优势。在大多数模拟场景中，RM的表现优于EM，即使RM的基本假设未完全满足时也是如此。RM的优势随着样本量的增加而减弱。结论 在RM和EM之间进行选择时，应考虑可用的样本量及其基本假设的合理性。

英文摘要

Introduction Risk modelling (RM) and treatment effect modelling (EM) are two approaches to build models to estimate heterogeneous treatment effects from randomized control trials (RCT). RM is a two-stage approach; it estimates a predicted baseline risk at the first stage and then includes it as the only effect modifier in a regression model in the second stage. EM is a full treatment interaction multivariable model. Both approaches have theoretical advantages and limitations, but a thorough comparison including a simulation study is missing. Methods In a theoretical review of the two approaches, we present their underlying assumptions and theoretical advantages and disadvantages. Based on our theoretical review, we design simulation scenarios for RCTs with a dichotomous outcome and evaluate the performance of both approaches with respect to the root mean square error and the bias in the predicted risk difference. Results In the theoretical part we argue that baseline risk is a treatment effect modifier in many clinical situations. RM is a dimensionality reduction approach which, however, makes strong assumptions about the role of prognostic factors modifying the treatment effect. EM's greater flexibility is a possible advantage when the sample size is large. In most simulated scenarios RM performs better than EM, even when the assumptions underlying RM are not fully met. The advantage of RM diminishes as sample size increases. Conclusion When choosing between RM and EM the available sample size and the plausibility of their underlying assumptions should be considered.

发表机构

• University of Bern（伯尔尼大学）

机构由 AI 辅助整理，请以论文原文为准。