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使用生存结局进行个体患者数据荟萃分析的实用方法

Practical methodology of meta-analysis of individual patient data using a survival outcome.

作者信息

Katsahian Sandrine, Latouche Aurélien, Mary Jean-Yves, Chevret Sylvie, Porcher Raphaël

机构信息

Département de Biostatistique et Informatique Médicale, Hôpital Saint-Louis, AP-HP, Université Paris 7, Paris, France; Inserm U717, Paris, France.

出版信息

Contemp Clin Trials. 2008 Mar;29(2):220-30. doi: 10.1016/j.cct.2007.08.002. Epub 2007 Aug 29.

Abstract

Meta-analysis of individual patient data (MIPD) is considered as one of the statistical approaches to provide integrated information on the effect of a treatment or an intervention. Statistical analysis of such meta-analyses should account for the clustered structure of data which is induced by all factors varying across the trials. For survival analysis, several models can handle such clustering under proportional hazards. This comprises models with fixed or random trial effects, stratified models and marginal models. In this paper, we review these models and compare their performances using a numerical simulation study. Results show that frailty models, and particularly those with random treatment by trial interactions, are well suited for meta-analyses on individual patient data. This is further exemplified on a meta-analysis of three trials comparing high-dose therapy to conventional chemotherapy in multiple myeloma.

摘要

个体患者数据的荟萃分析(MIPD)被视为提供有关治疗或干预效果综合信息的统计方法之一。对此类荟萃分析的统计分析应考虑到由各试验中所有不同因素所导致的数据聚类结构。对于生存分析,有几种模型可以在比例风险假设下处理这种聚类情况。这包括具有固定或随机试验效应的模型、分层模型和边际模型。在本文中,我们回顾这些模型,并通过数值模拟研究比较它们的性能。结果表明,脆弱模型,特别是那些具有随机治疗与试验交互作用的模型,非常适合个体患者数据的荟萃分析。这在一项比较大剂量疗法与传统化疗治疗多发性骨髓瘤的三项试验的荟萃分析中得到了进一步例证。

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