A tool for empirical equipoise assessment in multigroup comparative effectiveness research.
A tool for empirical equipoise assessment in multigroup comparative effectiveness research.
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多组比较有效性研究中实证平衡评估的工具。
DOI：
10.1002/pds.4767
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发表时间：
2019
影响因子：
2.6
通讯作者：
Glynn,RobertJ
中科院分区：
文献类型：
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作者：
Yoshida,Kazuki;Solomon,DanielH;Haneuse,Sebastien;Kim,SeoyoungC;Patorno,Elisabetta;Tedeschi,SaraK;Lyu,Houchen;Hernández-Díaz,Sonia;Glynn,RobertJ
目的在观察性研究中，平衡关注的是被比较的群体是否足够相似以进行有效的推断。经验平衡先前被提议作为基于倾向评分 (PS) 评估患者相似性的工具。我们将这项工作扩展到多组观察研究。方法我们修改了该工具以允许多项暴露，以便当只有两个组时，所提出的定义可以简化为原始定义。我们说明了如何使用该工具作为评估三组临床实例中的研究设计的方法。然后，我们进行了三组模拟，以评估该工具在 PS 加权后存在残余混杂因素的情况下的表现。结果在一个基于类风湿关节炎的临床示例中，在考虑一线生物制剂时，44.5% 的样本落在经验平衡区域内，而对于二线生物制剂，57.7% 的样本处于经验平衡区域内，这与二线设计带来更好平衡的预期一致。在模拟中，未测量的混杂因素与治疗的关联程度与测量的混杂因素相同，并且与结果的关联程度高出 25%，该工具在比率范围内的残余混杂因素为 20% 时跨越了建议的经验均衡阈值。当未测量的变量与治疗的关联性增加两倍时，该工具的敏感性就会降低，并在 30% 的残余混杂因素下超过阈值。结论我们提出的工具可能有助于指导多组观察性研究中的队列识别，特别是未测量和测量的协变量对治疗和结果的类似影响。
PurposeIn observational research, equipoise concerns whether groups being compared aresimilar enoughfor valid inference.Empirical equipoisewas previously proposed as a tool to assess patient similarity based on propensity scores (PS). We extended this work for multigroup observational studies.MethodsWe modified the tool to allow for multinomial exposures such that the proposed definition reduces to the original when there are only two groups. We illustrated how the tool can be used as a method to assess study design within three‐group clinical examples. We then conducted three‐group simulations to assess how the tool performed in a setting with residual confounding after PS weighting.ResultsIn a clinical example based on rheumatoid arthritis, 44.5% of the sample fell within the region of empirical equipoise when considering first‐line biologics, whereas 57.7% did so for second‐line biologics, consistent with the expectation that a second‐line design results in better equipoise. In a simulation where the unmeasured confounder had the same magnitude of association with the treatment as the measured confounders and a 25% greater association with the outcome, the tool crossed the proposed threshold for empirical equipoise at a residual confounding of 20% on the ratio scale. When the unmeasured variable had a twice larger association with treatment, the tool became less sensitive and crossed the threshold at a residual confounding of 30%.ConclusionOur proposed tool may be useful in guiding cohort identification in multigroup observational studies, particularly with similar effects of unmeasured and measured covariates on treatment and outcome.