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Propensity score weighting for covariate adjustment in randomized clinical trials
Statistics in Medicine ( IF 1.8 ) Pub Date : 2020-11-10 , DOI: 10.1002/sim.8805
Shuxi Zeng 1 , Fan Li 2, 3 , Rui Wang 4, 5 , Fan Li 1
Affiliation  

Chance imbalance in baseline characteristics is common in randomized clinical trials. Regression adjustment such as the analysis of covariance (ANCOVA) is often used to account for imbalance and increase precision of the treatment effect estimate. An objective alternative is through inverse probability weighting (IPW) of the propensity scores. Although IPW and ANCOVA are asymptotically equivalent, the former may demonstrate inferior performance in finite samples. In this article, we point out that IPW is a special case of the general class of balancing weights, and advocate to use overlap weighting (OW) for covariate adjustment. The OW method has a unique advantage of completely removing chance imbalance when the propensity score is estimated by logistic regression. We show that the OW estimator attains the same semiparametric variance lower bound as the most efficient ANCOVA estimator and the IPW estimator for a continuous outcome, and derive closed‐form variance estimators for OW when estimating additive and ratio estimands. Through extensive simulations, we demonstrate OW consistently outperforms IPW in finite samples and improves the efficiency over ANCOVA and augmented IPW when the degree of treatment effect heterogeneity is moderate or when the outcome model is incorrectly specified. We apply the proposed OW estimator to the Best Apnea Interventions for Research (BestAIR) randomized trial to evaluate the effect of continuous positive airway pressure on patient health outcomes. All the discussed propensity score weighting methods are implemented in the R package PSweight.

中文翻译:


随机临床试验中协变量调整的倾向评分加权



基线特征的机会不平衡在随机临床试验中很常见。协方差分析 (ANCOVA) 等回归调整通常用于解释不平衡并提高治疗效果估计的精度。一种客观的替代方案是通过倾向得分的逆概率加权 (IPW)。尽管 IPW 和 ANCOVA 渐近等效,但前者在有限样本中可能表现出较差的性能。在本文中,我们指出IPW是一般类平衡权重的特例,并主张使用重叠权重(OW)进行协变量调整。 OW 方法具有独特的优势,可以在通过逻辑回归估计倾向得分时完全消除机会不平衡。我们表明,对于连续结果,OW 估计器达到了与最有效的 ANCOVA 估计器和 IPW 估计器相同的半参数方差下界,并在估计加性和比率估计量时导出 OW 的封闭形式方差估计器。通过广泛的模拟,我们证明 OW 在有限样本中始终优于 IPW,并且当治疗效果异质性程度适中或结果模型指定不正确时,OW 的效率优于 ANCOVA 和增强型 IPW。我们将拟议的 OW 估计器应用于最佳呼吸暂停干预研究 (BestAIR) 随机试验,以评估持续气道正压对患者健康结果的影响。所有讨论的倾向得分加权方法都在 R 包PSweight中实现。
更新日期:2021-01-13
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