Covariate adjustment in subgroup analyses of randomized clinical trials: A propensity score approach
随机临床试验亚组分析中的协变量调整：倾向得分方法AI 解读 ↓
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随机临床试验亚组分析中的协变量调整：倾向得分方法
- 研究问题
- 随机临床试验的小亚组中，偶然的协变量不平衡可能降低精度；如何通过倾向得分加权改善异质处理效应分析的精度和功效？
- 方法与思路
- 作者在逻辑倾向得分模型中加入预先指定的协变量与亚组交互项，采用重叠加权实现亚组内相应协变量的精确平衡。
- 材料陈述的贡献
- 摘要称调整降低标准误，完整交互加权在所述模拟中优于主效应模型，并在部分小亚组或结果模型误设情形下比 ANCOVA 更有效率。
- 阅读关注点
- 应核对亚组效应的目标总体、精确平衡涉及的变量、交互项预先指定方式，以及方差估计和模拟中效率比较的条件。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或期刊接收；摘要未给出完整推断条件，所述效率优势不能推广至所有试验与亚组。
原文摘要
Background: Subgroup analyses are frequently conducted in randomized clinical trials to assess evidence of heterogeneous treatment effect across patient subpopulations. Although randomization balances covariates within subgroups in expectation, chance imbalance may be amplified in small subgroups and harm the precision. Covariate adjustment in overall analysis of RCT is often conducted, via either ANCOVA or propensity score weighting, but for subgroup analysis has been rarely discussed. In this article, we develop propensity score weighting methodology for covariate adjustment to improve the precision and power of subgroup analyses in RCTs. Methods: We extend the propensity score weighting methodology to subgroup analyses by fitting a logistic propensity model with pre-specified covariate-subgroup interactions. We show that, by construction, overlap weighting exactly balances the covariates with interaction terms in each subgroup. Extensive simulations were performed to compare the operating characteristics of unadjusted, different propensity score weighting and the ANCOVA estimator. We apply these methods to the HF-ACTION trial to evaluate the effect of exercise training on 6-minute walk test in pre-specified subgroups. Results: Standard errors of the adjusted estimators are smaller than those of the unadjusted estimator. The propensity score weighting estimator is as efficient as ANCOVA, and is often more efficient when subgroup sample size is small (e.g.<125), and/or when outcome model is misspecified. The weighting estimators with full-interaction propensity model consistently outperform the standard main-effect propensity model. Conclusion: Propensity score weighting is a transparent and objective method to adjust chance imbalance of important covariates in subgroup analyses of RCTs. It is crucial to include the full covariate-subgroup interactions in the propensity score model.