Estimating heterogeneous treatment effects from randomised trials: a comparison of the risk modelling and treatment effect modelling approaches
随机试验异质性处理效应估计：风险建模与效应建模比较AI 解读 ↓
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随机试验异质性处理效应估计：风险建模与效应建模比较
- 研究问题
- 在具有二元结果的随机试验中，两阶段风险建模与完整处理交互建模如何影响异质性风险差预测，样本量和模型假设如何左右选择？
- 方法与思路
- 作者先比较两类方法的理论假设，再设计二元结果随机试验模拟，以预测风险差的偏差和均方根误差评价其表现。
- 材料陈述的贡献
- 摘要称，多数模拟情景中风险建模表现较好，即使部分假设不满足；其优势随样本增大而减弱，效应建模的大样本灵活性可能有利。
- 阅读关注点
- 应核对基线风险作为唯一效应修饰因素的限制、完整交互模型的设定，以及模拟情景、样本量和评价方式能否代表目标试验。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未给出模拟参数或具体误差结果，不能将多数情景的优势视为统一选择规则。
原文摘要
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.