Meta-analysis of continuous outcome data from individual patients
Meta-analysis of continuous outcome data from individual patients
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DOI：
10.1002/sim.918
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发表时间：
2001-08-15
影响因子：
2
通讯作者：
Thompson, SG
中科院分区：
文献类型：
--
作者：
Higgins, JPT;Whitehead, A;Thompson, SG
使用个体患者数据的荟萃分析越来越常见，与汇总统计量的荟萃分析相比有几个优势。我们探索使用多层次或分层模型的荟萃分析连续的个体患者的临床试验结果数据。一个通用的框架，其中包括传统的荟萃分析，以及荟萃回归和纳入患者水平的协变量的异质性调查。试验间治疗差异的不明原因变化被视为随机变化。我们专注于固定试验效应的模型，虽然扩展到随机效应的试验。在阿尔茨海默病的一个例子中，在一个经典的框架，使用SAS PROC MIXED和MLwiN的方法进行说明，并在贝叶斯框架，使用错误。三个软件包的相对优点进行了讨论，这样的荟萃分析，作为评估模型的假设和扩展，将两个以上的治疗。版权所有（C）2001约翰威利父子有限公司
Meta-analyses using individual patient data are becoming increasingly common and have several advantages over meta-analyses of summary statistics. We explore the use of multilevel or hierarchical models for the meta-analysis of continuous individual patient outcome data from clinical trials. A general framework is developed which encompasses traditional meta-analysis, as well as meta-regression and the inclusion of patient-level covariates for investigation of heterogeneity. Unexplained variation in treatment differences between trials is considered as random. We focus on models with fixed trial effects, although an extension to a random effect for trial is described. The methods are illustrated on an example in Alzheimer's disease in a classical framework using SAS PROC MIXED and MLwiN, and in a Bayesian framework using BUGS. Relative merits of the three software packages for such meta-analyses are discussed, as are the assessment of model assumptions and extensions to incorporate more than two treatments. Copyright (C) 2001 John Wiley & Sons, Ltd.