Meta-analysis of individual patient data from randomized trials: a review of methods used in practice
Meta-analysis of individual patient data from randomized trials: a review of methods used in practice
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DOI：
10.1191/1740774505cn087oa
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发表时间：
2005-01-01
期刊：
影响因子：
2.7
通讯作者：
Thompson, SG
中科院分区：
文献类型：
--
作者：
Simmonds, MC;Higgins, JPT;Thompson, SG
基于个体患者数据（IPD）的荟萃分析被认为是系统评价的金标准。然而，用于分析和呈现IPD荟萃分析结果的方法却很少得到讨论。方法我们回顾了1999-2001年期间发表的44篇IPD荟萃分析。我们总结了他们是否获得了所有的数据，他们寻求，什么类型的方法被用于分析，包括假设的共同或随机效应，以及他们如何检查的影响的协variates.Results二十四的44个分析集中在时间到事件的结果，和大多数分析（28）估计治疗效果在每个试验，然后结合的结果，假设一个共同的治疗效果跨试验。三项分析未能按试验进行分层，分析数据是否来自单一的大型试验。只有9项分析使用了随机效应方法。通常通过亚组患者研究协变量-治疗相互作用。七个荟萃分析包括数据不到80%的随机患者寻求，但没有解决由此产生的潜在bias.Conclusions虽然IPD荟萃分析有许多优势，在评估医疗保健的影响，有几个方面，可以进一步发展，以充分利用这些耗时的项目的潜力。特别是，IPD可用于更全面地研究协变量对试验内和试验间治疗效应异质性的影响。异质性的影响，或使用随机效应，很少讨论。因此，有相当大的空间，以加强分析和介绍IPD荟萃分析的方法。
Background Meta-analyses based on individual patient data (IPD) are regarded as the gold standard for systematic reviews. However, the methods used for analysing and presenting results from IPD meta-analyses have received little discussion.Methods We review 44 IPD meta-analyses published during the years 1999-2001. We summarize whether they obtained all the data they sought, what types of approaches were used in the analysis, including assumptions of common or random effects, and how they examined the effects of covariates.Results Twenty-four out of 44 analyses focused on time-to-event outcomes, and most analyses (28) estimated treatment effects within each trial and then combined the results assuming a common treatment effect across trials. Three analyses failed to stratify by trial, analysing the data is if they came from a single mega-trial. Only nine analyses used random effects methods. Covariate-treatment interactions were generally investigated by subgrouping patients. Seven of the meta-analyses included data from less than 80% of the randomized patients sought, but did not address the resulting potential biases.Conclusions Although IPD meta-analyses have many advantages in assessing the effects of health care, there are several aspects that could be further developed to make fuller use of the potential of these time-consuming projects. In particular, IPD could be used to more fully investigate the influence of covariates on heterogeneity of treatment effects, both within and between trials. The impact of heterogeneity, or use of random effects, are seldom discussed. There is thus considerable scope for enhancing the methods of analysis and presentation of IPD meta-analysis.