Incorporating data from various trial designs into a mixed treatment comparison model
Incorporating data from various trial designs into a mixed treatment comparison model
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DOI：
10.1002/sim.5764
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发表时间：
2013-07-30
影响因子：
2
通讯作者：
Walsh, Cathal
中科院分区：
文献类型：
--
作者：
Schmitz, Susanne;Adams, Roisin;Walsh, Cathal
估计替代治疗之间的相对疗效是至关重要的医疗保健决策。贝叶斯混合治疗比较模型提供了一个强大的方法来获得这样的估计时，头对头的证据是不可用或不足。近年来，这种方法已被广泛接受，并应用于医疗干预的经济建模。大多数评价只考虑随机对照试验的证据，而忽略了其他试验设计的信息。在本文中，我们提出了三种替代方法，结合数据从不同的试验设计中的混合治疗比较模型。朴素合并是最简单的方法，不区分试验间设计。利用观察性数据作为先验信息，可以调整试验设计引起的偏倚。最灵活的技术是三层分层模型。这样的模型允许偏倚调整，同时也考虑了试验间设计的异质性。这些技术说明使用类风湿性关节炎的应用程序。版权所有（c）2013约翰威利父子有限公司
Estimates of relative efficacy between alternative treatments are crucial for decision making in health care. Bayesian mixed treatment comparison models provide a powerful methodology to obtain such estimates when head-to-head evidence is not available or insufficient. In recent years, this methodology has become widely accepted and applied in economic modelling of healthcare interventions. Most evaluations only consider evidence from randomized controlled trials, while information from other trial designs is ignored. In this paper, we propose three alternative methods of combining data from different trial designs in a mixed treatment comparison model. Naive pooling is the simplest approach and does not differentiate between-trial designs. Utilizing observational data as prior information allows adjusting for bias due to trial design. The most flexible technique is a three-level hierarchical model. Such a model allows for bias adjustment while also accounting for heterogeneity between-trial designs. These techniques are illustrated using an application in rheumatoid arthritis. Copyright (c) 2013 John Wiley & Sons, Ltd.