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评估随机试验中的基线不平衡:对 Cochrane 偏倚风险工具的影响。

Assessing baseline imbalance in randomised trials: implications for the Cochrane risk of bias tool.

机构信息

Centre for Reviews and Dissemination, University of York, York, YO10 5DD, UK.

出版信息

Res Synth Methods. 2014 Mar;5(1):79-85. doi: 10.1002/jrsm.1090. Epub 2013 Aug 1.

Abstract

A key component of the Cochrane Collaboration's risk of bias tool for critically evaluating randomised trials is the consideration of whether baseline characteristics of the treatment groups being compared are systematically different. Considered under the domain of 'selection bias', this is currently evaluated by looking at the methods of randomisation and specifically at the generation of the randomised allocation sequence and the concealment of this sequence during the process of randomisation. Assessment of the actual similarity of baseline variables across groups in demographic and clinical characteristics is seldom performed. Even when performed, the link with selection bias is sometimes not considered. Methods of randomisation and allocation concealment are often poorly reported in published trials, yet baseline data tables are presented in a large majority of trial reports. In this article, we propose that assessment of trial baseline data should form a key and prominent part of selection bias judgements when using the risk of bias tool. We outline the possible benefits from using this approach, including reduced uncertainty in systematic review conclusions, reduced risk of chance findings being ascribed to treatment effects and better use of available evidence by a more considered approach to evaluating studies using imperfect randomisation and allocation methods.

摘要

Cochrane 协作组用于批判性评估随机试验的偏倚风险工具的一个关键组成部分是考虑正在比较的治疗组的基线特征是否存在系统差异。在“选择偏倚”这一领域进行考虑,目前通过查看随机化方法,特别是随机分配序列的生成以及在随机化过程中对该序列的隐藏情况来进行评估。很少对群组在人口统计学和临床特征方面的基线变量的实际相似性进行评估。即使进行了评估,也有时不会考虑与选择偏倚的联系。在已发表的试验中,随机化和分配隐藏方法的报告往往很差,而基线数据表在大多数试验报告中都有呈现。在本文中,我们提出在使用偏倚风险工具时,评估试验基线数据应成为选择偏倚判断的关键和突出部分。我们概述了使用这种方法的可能好处,包括系统评价结论的不确定性降低、机会发现被归因于治疗效果的风险降低以及通过更仔细地评估使用不完美随机化和分配方法的研究,更好地利用现有证据。

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