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.
翻译:背景 : 分组分析经常在随机临床试验中进行,以评估病人亚群群之间不同治疗效果的证据。 虽然随机化平衡在分组内部会发生共变, 但机会不平衡可能会在小分组中扩大, 并损害精确性。 经常通过ANCOVA或偏差评分加权法对RCT总体分析进行共变调整, 但很少讨论分组分析。 在本条中, 我们为共变加权调整制定了调分方法, 以提高RCTs分组分析的精确度和功率。 方法 : 我们通过安装一个物流偏差模型, 将偏差加权法扩大到分组分析, 在小分组中, 在小分组中, 我们通过预设的 复变差- 亚群互动法, 在每个分组中, 对共变差进行完全的调整 。 标准比值的共变差法, 在计算精度结果时, RCO 的分数模型模型/ 的比值比值小分数 。 我们将这些方法应用到高频- 基体试验来评价练习训练对6分钟的比 的比对精度测试重量的影响 。 精度 。 精度 精度 精度 精度 精度 精度 度 度 度 精度 度 精度 度 精度 精度 度 度 度 度 度 度 精度 度 度 度 度 度 度 度 度 度 度 度 度 度 度 度 度 度 度 度 度 度 度 度 度 度 度 度 度 度 度 度 度 度 度 度 度 度 度 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值 值