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从随机对照试验中获取数据:进行可靠且信息丰富的荟萃分析需要多少数据?

Obtaining data from randomised controlled trials: how much do we need for reliable and informative meta-analyses?

作者信息

Clarke M J, Stewart L A

机构信息

Clinical Trial Service Unit, Radcliffe Infirmary, Oxford.

出版信息

BMJ. 1994 Oct 15;309(6960):1007-10. doi: 10.1136/bmj.309.6960.1007.

Abstract

Many randomised controlled trials compare treatments that will produce only moderate differences in outcome, but these differences can be clinically important. However, they are difficult to assess reliably and require a large amount of randomised evidence. This can be achieved through large prospective randomised trials which will accrue future patients, the meta-analysis of results from randomised trials involving patients from the past, or--ideally--both. The techniques require that all possible biases are minimised, and in meta-analyses this can best be achieved by ensuring that all of the randomised evidence--both trials and participants in those trials--is included. The meta-analysis of individual patient data has been described as the gold standard for this approach. It will remove many of the problems associated with relying solely on published data and some of the problems arising from a reliance on aggregate data, and will also add to the analyses that can be performed. Such projects, however, require considerable time and effort.

摘要

许多随机对照试验比较的治疗方法在结果上只会产生适度差异,但这些差异在临床上可能很重要。然而,它们很难可靠地评估,需要大量随机证据。这可以通过大型前瞻性随机试验来实现,这些试验将招募未来的患者,对过去涉及患者的随机试验结果进行荟萃分析,或者——理想情况下——两者兼而有之。这些技术要求将所有可能的偏倚降至最低,在荟萃分析中,最好通过确保纳入所有随机证据——试验以及这些试验中的参与者——来实现这一点。对个体患者数据的荟萃分析被描述为这种方法的金标准。它将消除许多仅依赖已发表数据相关的问题以及一些因依赖汇总数据而产生的问题,还将增加可进行的分析。然而,此类项目需要大量时间和精力。

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