Combining Non-randomized and Randomized Data in Clinical Trials Using Commensurate Priors.
Combining Non-randomized and Randomized Data in Clinical Trials Using Commensurate Priors.
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使用相应的先验在临床试验中结合非随机和随机数据。
DOI：
10.1007/s10742-016-0155-7
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发表时间：
2016
影响因子：
1.5
通讯作者：
Carlin,BradleyP
中科院分区：
文献类型：
--
作者：
Zhao,Hong;Hobbs,BrianP;Ma,Haijun;Jiang,Qi;Carlin,BradleyP
随机化消除了选择偏倚，并减弱了研究组之间在已知和未知预后因素方面的不平衡。因此，来自随机临床试验（RCT）的信息通常被认为是验证性研究中比较治疗干预措施的金标准。然而，随机对照试验在愿意接受随机治疗分配的患者仅代表患者人群的一个子集的情况下受到限制。相比之下，观察性研究（OS）通常招募更能反映更广泛患者人群的患者队列。然而，OS通常存在选择偏倚，即使在调整已知混杂因素后，也可能产生无效的治疗比较。因此，由于操作系统的局限性，将操作系统获得的信息与随机对照试验的数据相结合进行研究综合常常受到批评。在这篇文章中，我们结合联合收割机随机和非随机子研究数据，从第一，最近的艾滋病毒/艾滋病药物试验。我们开发了分层贝叶斯方法，旨在联合收割机同时从所有来源的数据，同时明确占潜在的差异来源的设计。具体来说，我们描述了一个两步的方法相结合的倾向评分匹配和贝叶斯分层模型，从非随机研究的数据与RCT的信息整合，在一定程度上，这取决于估计的可重复性的数据源。我们调查我们的程序的操作特性，通过模拟。我们的研究结果对HIV/AIDS研究有一定的意义，同时也阐明了精心设计的非随机研究在多大程度上可以补充RCT。
Randomization eliminates selection bias, and attenuates imbalance among study arms with respect to prognostic factors, both known and unknown. Thus, information arising from randomized clinical trials (RCTs) is typically considered the gold standard for comparing therapeutic interventions in confirmatory studies. However, RCTs are limited in contexts wherein patients who are willing to accept a random treatment assignment represent only a subset of the patient population. By contrast, observational studies (OSs) often enroll patient cohorts that better reflect the broader patient population. However, OSs often suffer from selection bias, and may yield invalid treatment comparisons even after adjusting for known confounders. Therefore, combining information acquired from OSs with data from RCTs in research synthesis is often criticized due to the limitations of OSs. In this article, we combine randomized and non-randomized substudy data from FIRST, a recent HIV/AIDS drug trial. We develop hierarchical Bayesian approaches devised to combine data from all sources simultaneously while explicitly accounting for potential discrepancies in the sources’ designs. Specifically, we describe a two-step approach combining propensity score matching and Bayesian hierarchical modeling to integrate information from non-randomized studies with data from RCTs, to an extent that depends on the estimated commensurability of the data sources. We investigate our procedure’s operating characteristics via simulation. Our findings have implications for HIV/AIDS research, as well as elucidate the extent to which well-designed non-randomized studies can complement RCTs.