Trial Sequential Analysis in systematic reviews with meta-analysis.
Trial Sequential Analysis in systematic reviews with meta-analysis.
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DOI：
10.1186/s12874-017-0315-7
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发表时间：
2017-03-06
影响因子：
4
通讯作者：
Gluud C
中科院分区：
文献类型：
--
作者：
Wetterslev J;Jakobsen JC;Gluud C
大多数系统评价的荟萃分析，包括Cochrane的荟萃分析，都没有足够的统计能力来发现或反驳甚至较大的干预效应。这就是为什么荟萃分析应该被视为一种过渡分析，以达到所需的信息规模。荟萃分析的结果应将随机参与者的总数与考虑统计多样性的估计所需的荟萃分析信息大小联系起来。当荟萃分析的参与者数量和相应的试验数量不足时，使用传统的95%置信区间或5%的统计显著性阈值会导致太多的假阳性结论（第一类错误）和太多的假阴性结论（第二类错误）。我们开发了一种解释荟萃分析结果的方法，使用普遍接受的有效证据，说明如何在未达到所需样本量的随机临床试验中调整显著性阈值。试验序列分析中的Lan-DeMets试验序列监测边界为统计显著性提供了调整的置信区间和限制阈值，当未达到多样性调整的meta分析所需的信息大小和相应的所需试验数量时。试验序列分析提供了一种控制I型和II型错误的频率方法。我们在荟萃分析中定义了所需的信息大小和相应的所需试验数量，以及异质性的多样性（D2）测量。我们解释了在实际信息量达不到所需信息量时使用meta分析的试验序列分析的原因。我们使用未调整的naïve 95%置信区间和5%阈值对统计显著性进行传统荟萃分析。采用传统荟萃分析的系统评价中的错误结论可以使用试验序列分析来减少。几项实证研究表明，与传统的naïve元分析相比，试验序列分析可以更好地控制I型错误和II型错误。试验序贯分析是对元分析数据的分析，具有透明的假设，并且比使用naïve未调整置信区间的传统元分析更好地控制I型和II型错误。
Most meta-analyses in systematic reviews, including Cochrane ones, do not have sufficient statistical power to detect or refute even large intervention effects. This is why a meta-analysis ought to be regarded as an interim analysis on its way towards a required information size. The results of the meta-analyses should relate the total number of randomised participants to the estimated required meta-analytic information size accounting for statistical diversity. When the number of participants and the corresponding number of trials in a meta-analysis are insufficient, the use of the traditional 95% confidence interval or the 5% statistical significance threshold will lead to too many false positive conclusions (type I errors) and too many false negative conclusions (type II errors). We developed a methodology for interpreting meta-analysis results, using generally accepted, valid evidence on how to adjust thresholds for significance in randomised clinical trials when the required sample size has not been reached. The Lan-DeMets trial sequential monitoring boundaries in Trial Sequential Analysis offer adjusted confidence intervals and restricted thresholds for statistical significance when the diversity-adjusted required information size and the corresponding number of required trials for the meta-analysis have not been reached. Trial Sequential Analysis provides a frequentistic approach to control both type I and type II errors. We define the required information size and the corresponding number of required trials in a meta-analysis and the diversity (D2) measure of heterogeneity. We explain the reasons for using Trial Sequential Analysis of meta-analysis when the actual information size fails to reach the required information size. We present examples drawn from traditional meta-analyses using unadjusted naïve 95% confidence intervals and 5% thresholds for statistical significance. Spurious conclusions in systematic reviews with traditional meta-analyses can be reduced using Trial Sequential Analysis. Several empirical studies have demonstrated that the Trial Sequential Analysis provides better control of type I errors and of type II errors than the traditional naïve meta-analysis. Trial Sequential Analysis represents analysis of meta-analytic data, with transparent assumptions, and better control of type I and type II errors than the traditional meta-analysis using naïve unadjusted confidence intervals.