Higgins J P, Whitehead A
Department of Applied Statistics, University of Reading, U.K.
Stat Med. 1996 Dec 30;15(24):2733-49. doi: 10.1002/(SICI)1097-0258(19961230)15:24<2733::AID-SIM562>3.0.CO;2-0.
There exists a variety of situations in which a random effects meta-analysis might be undertaken using a small number of clinical trials. A problem associated with small meta-analyses is estimating the heterogeneity between trials. To overcome this problem, information from other related studies may be incorporated into the meta-analysis. A Bayesian approach to this problem is presented using data from previous meta-analyses in the same therapeutic area to formulate a prior distribution for the heterogeneity. The treatment difference parameters are given non-informative priors. Further, related trials which compare one or other of the treatments of interest with a common third treatment are included in the model to improve inference on both the heterogeneity and the treatment difference. Two approaches to estimating relative efficacy are considered, namely a general parametric approach and a method explicit to binary data. The methodology is illustrated using data from 26 clinical trials which investigate the prevention of cirrhosis using beta-blockers and sclerotherapy. Both sources of external information lead to more precise posterior distributions for all parameters, in particular that representing heterogeneity.
在多种情况下，可能会使用少量临床试验进行随机效应荟萃分析。小型荟萃分析存在的一个问题是估计各试验之间的异质性。为克服这一问题，可将其他相关研究的信息纳入荟萃分析。本文提出一种贝叶斯方法来解决此问题，即利用同一治疗领域先前荟萃分析的数据来制定异质性的先验分布。治疗差异参数采用非信息性先验。此外，将比较一种或其他感兴趣治疗与共同第三种治疗的相关试验纳入模型，以改善对异质性和治疗差异的推断。考虑了两种估计相对疗效的方法，即一般参数方法和二元数据专用方法。使用26项临床试验的数据对该方法进行了说明，这些试验研究了使用β受体阻滞剂和硬化疗法预防肝硬化的情况。两种外部信息来源都能为所有参数，特别是代表异质性的参数，带来更精确的后验分布。
