Meta-analysis of continuous outcomes combining individual patient data and aggregate data
Meta-analysis of continuous outcomes combining individual patient data and aggregate data
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DOI：
10.1002/sim.3165
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发表时间：
2008-05-20
影响因子：
2
通讯作者：
Boutitie, Florent
中科院分区：
文献类型：
--
作者：
Riley, Richard D.;Lambert, Paul C.;Boutitie, Florent
个体患者数据的荟萃分析（IPD）是综合临床研究证据的金标准。然而，对于某些研究，IPD可能不可用，只能获得汇总数据（AD），例如治疗效果估计值及其标准误。在这种情况下，结合IPD和AD的方法对于利用所有可用证据非常重要。在本文中，我们开发和评估了一系列的统计方法，结合IPD和AD的随机对照试验的连续结果的荟萃分析。该方法采取一步或两步的方法。后者很简单，IPD简化为AD，因此可以采用标准的AD荟萃分析技术。一步法更复杂，但提供了一个灵活的框架，包括患者水平和试验水平的参数。它使用一个虚拟变量来区分IPD试验和AD试验，并限制AD试验估计的参数。我们表明，这是很重要的，当评估患者水平的协变量如何修改治疗效果，因为跨试验的总体水平的关系受到生态偏见和混杂。因此，我们开发了分离试验内和试验间治疗协变量相互作用的模型;这确保了只有IPD试验估计前者，而IPD和AD试验除了估计汇总治疗效果和任何研究间异质性外，还估计后者。还考虑扩展到多个相关结果。10项IPD高血压试验，血压是持续关注的结果，用于评估模型并确定与IPD一起使用AD的益处。版权所有（c）2007约翰威利父子有限公司。
Meta-analysis of individual patient data (IPD) is the gold-standard for synthesizing evidence across clinical studies. However, for some studies IPD may not be available and only aggregate data (AD), such as a treatment effect estimate and its standard error, may be obtained. In this situation, methods for combining IPD and AD are important to utilize all the available evidence. In this paper, we develop and assess a range of statistical methods for combining IPD and AD in meta-analysis of continuous outcomes from randomized controlled trials.The methods take either a one-step or a two-step approach. The latter is simple, with IPD reduced to AD so that standard AD meta-analysis techniques can be employed. The one-step approach is more complex but offers a flexible framework to include both patient-level and trial-level parameters. It uses a dummy variable to distinguish IPD trials from AD trials and to constrain which parameters the AD trials estimate. We show that this is important when assessing how patient-level covariates modify treatment effect, as aggregate-level relationships across trials are subject to ecological bias and confounding. We thus develop models to separate within-trial and across-trials treatment-covariate interactions; this ensures that only IPD trials estimate the former, whilst both IPD and AD trials estimate the latter in addition to the pooled treatment effect and any between-study heterogeneity. Extension to multiple correlated outcomes is also considered. Ten IPD trials in hypertension, with blood pressure the continuous outcome of interest, are used to assess the models and identify the benefits of utilizing AD alongside IPD. Copyright (c) 2007 John Wiley & Sons, Ltd.