MRC Biostatistics Unit, School of Clinical Medicine, Cambridge Institute of Public Health, University of Cambridge, Forvie Site, Robinson Way, Cambridge Biomedical Campus, Cambridge CB2 0SR, UK.
MRC Biostatistics Unit, School of Clinical Medicine, Cambridge Institute of Public Health, University of Cambridge, Forvie Site, Robinson Way, Cambridge Biomedical Campus, Cambridge CB2 0SR, UK; MRC Clinical Trials Unit, Institute of Clinical Trials and Methodology, University College London, 90 High Holborn, 2nd Floor, London WC1V 6LJ, UK.
J Clin Epidemiol. 2018 Mar;95:45-54. doi: 10.1016/j.jclinepi.2017.11.025. Epub 2017 Dec 5.
We investigated the associations between risk of bias judgments from Cochrane reviews for sequence generation, allocation concealment and blinding, and between-trial heterogeneity.
Bayesian hierarchical models were fitted to binary data from 117 meta-analyses, to estimate the ratio λ by which heterogeneity changes for trials at high/unclear risk of bias compared with trials at low risk of bias. We estimated the proportion of between-trial heterogeneity in each meta-analysis that could be explained by the bias associated with specific design characteristics.
Univariable analyses showed that heterogeneity variances were, on average, increased among trials at high/unclear risk of bias for sequence generation (λˆ 1.14, 95% interval: 0.57-2.30) and blinding (λˆ 1.74, 95% interval: 0.85-3.47). Trials at high/unclear risk of bias for allocation concealment were on average less heterogeneous (λˆ 0.75, 95% interval: 0.35-1.61). Multivariable analyses showed that a median of 37% (95% interval: 0-71%) heterogeneity variance could be explained by trials at high/unclear risk of bias for sequence generation, allocation concealment, and/or blinding. All 95% intervals for changes in heterogeneity were wide and included the null of no difference.
Our interpretation of the results is limited by imprecise estimates. There is some indication that between-trial heterogeneity could be partially explained by reported design characteristics, and hence adjustment for bias could potentially improve accuracy of meta-analysis results.
我们调查了 Cochrane 评价中关于随机序列生成、分配隐藏和盲法的偏倚风险判断之间的关联，以及试验间异质性之间的关联。
贝叶斯层次模型被拟合到 117 项荟萃分析的二分类数据中，以估计高/不确定偏倚风险与低偏倚风险相比，试验间异质性变化的比例λ。我们估计了每个荟萃分析中可以用与特定设计特征相关的偏倚来解释的试验间异质性的比例。
单变量分析表明，在高/不确定偏倚风险的试验中，随机序列生成（λˆ1.14，95%区间：0.57-2.30）和盲法（λˆ1.74，95%区间：0.85-3.47）的异质性方差平均增加。高/不确定偏倚风险的分配隐藏试验平均异质性较小（λˆ0.75，95%区间：0.35-1.61）。多变量分析表明，37%（95%区间：0-71%）的中位数异质性方差可以用随机序列生成、分配隐藏和/或盲法的高/不确定偏倚风险的试验来解释。异质性变化的所有 95%区间都很宽，包括无差异的零假设。
我们对结果的解释受到不精确估计的限制。有一些迹象表明，试验间异质性可能部分可以用报告的设计特征来解释，因此调整偏倚可能会潜在地提高荟萃分析结果的准确性。
