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随机试验和观察性研究中的效应估计:将苹果与苹果进行比较。

Effect Estimates in Randomized Trials and Observational Studies: Comparing Apples With Apples.

机构信息

Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts.

Institute for Global Health, University College London, United Kingdom.

出版信息

Am J Epidemiol. 2019 Aug 1;188(8):1569-1577. doi: 10.1093/aje/kwz100.

Abstract

Effect estimates from randomized trials and observational studies might not be directly comparable because of differences in study design, other than randomization, and in data analysis. We propose a 3-step procedure to facilitate meaningful comparisons of effect estimates from randomized trials and observational studies: 1) harmonization of the study protocols (eligibility criteria, treatment strategies, outcome, start and end of follow-up, causal contrast) so that the studies target the same causal effect, 2) harmonization of the data analysis to estimate the causal effect, and 3) sensitivity analyses to investigate the impact of discrepancies that could not be accounted for in the harmonization process. To illustrate our approach, we compared estimates of the effect of immediate with deferred initiation of antiretroviral therapy in individuals positive for the human immunodeficiency virus from the Strategic Timing of Antiretroviral Therapy (START) randomized trial and the observational HIV-CAUSAL Collaboration.

摘要

随机试验和观察性研究的效应估计值可能由于研究设计、除随机化以外的其他因素以及数据分析方面的差异而无法直接进行比较。我们提出了一个 3 步程序,以促进对随机试验和观察性研究的效应估计值进行有意义的比较:1)协调研究方案(资格标准、治疗策略、结局、随访开始和结束、因果对比),以便研究针对相同的因果效应,2)协调数据分析以估计因果效应,3)进行敏感性分析,以调查在协调过程中无法考虑的差异的影响。为了说明我们的方法,我们比较了即刻与延迟启动抗逆转录病毒治疗对人类免疫缺陷病毒阳性个体的影响,该比较来自随机试验 Strategic Timing of Antiretroviral Therapy(START)和观察性 HIV-CAUSAL 协作。

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