Bias modelling in evidence synthesis.
Bias modelling in evidence synthesis.
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DOI：
10.1111/j.1467-985x.2008.00547.x
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发表时间：
2009-01
期刊：
影响因子：
--
通讯作者：
Thompson SG
中科院分区：
文献类型：
--
作者：
Turner RM;Spiegelhalter DJ;Smith GC;Thompson SG
政策决定往往需要综合多个来源的证据，而来源研究的严谨性和与目标问题的相关性通常各不相同。我们提出了简单的方法，允许在证据合成的严谨性（或缺乏内部偏见）和相关性（或缺乏外部偏见）的差异。这些方法是在重新分析英国国家临床卓越研究所产前护理技术评估的背景下开发的，其中包括八项比较研究。许多是历史对照，只有一个是随机试验，剂量，人群和结果在研究之间存在差异，并且与目标英国设置不同。使用引出的意见，我们构建先验分布来表示每个研究中的偏差，并进行偏差调整后的荟萃分析。调整的效果是将合并估计值从零值偏移约10%，合并估计值的方差几乎增加了两倍。我们的通用偏倚建模方法允许基于所有可用证据做出决策，通过使用计算简单的方法降低不太严格或不太相关的研究。
Policy decisions often require synthesis of evidence from multiple sources, and the source studies typically vary in rigour and in relevance to the target question. We present simple methods of allowing for differences in rigour (or lack of internal bias) and relevance (or lack of external bias) in evidence synthesis. The methods are developed in the context of reanalysing a UK National Institute for Clinical Excellence technology appraisal in antenatal care, which includes eight comparative studies. Many were historically controlled, only one was a randomized trial and doses, populations and outcomes varied between studies and differed from the target UK setting. Using elicited opinion, we construct prior distributions to represent the biases in each study and perform a bias-adjusted meta-analysis. Adjustment had the effect of shifting the combined estimate away from the null by approximately 10%, and the variance of the combined estimate was almost tripled. Our generic bias modelling approach allows decisions to be based on all available evidence, with less rigorous or less relevant studies downweighted by using computationally simple methods.