随机效应meta回归模型中研究间残差方差的置信区间和可信区间的计算方法。
Methods for calculating confidence and credible intervals for the residual between-study variance in random effects meta-regression models.
作者信息
Jackson Dan, Turner Rebecca, Rhodes Kirsty, Viechtbauer Wolfgang
机构信息
MRC Biostatistics Unit, Cambridge, UK.
出版信息
BMC Med Res Methodol. 2014 Sep 6;14:103. doi: 10.1186/1471-2288-14-103.
BACKGROUND
Meta-regression is becoming increasingly used to model study level covariate effects. However this type of statistical analysis presents many difficulties and challenges. Here two methods for calculating confidence intervals for the magnitude of the residual between-study variance in random effects meta-regression models are developed. A further suggestion for calculating credible intervals using informative prior distributions for the residual between-study variance is presented.
METHODS
Two recently proposed and, under the assumptions of the random effects model, exact methods for constructing confidence intervals for the between-study variance in random effects meta-analyses are extended to the meta-regression setting. The use of Generalised Cochran heterogeneity statistics is extended to the meta-regression setting and a Newton-Raphson procedure is developed to implement the Q profile method for meta-analysis and meta-regression. WinBUGS is used to implement informative priors for the residual between-study variance in the context of Bayesian meta-regressions.
RESULTS
Results are obtained for two contrasting examples, where the first example involves a binary covariate and the second involves a continuous covariate. Intervals for the residual between-study variance are wide for both examples.
CONCLUSIONS
Statistical methods, and R computer software, are available to compute exact confidence intervals for the residual between-study variance under the random effects model for meta-regression. These frequentist methods are almost as easily implemented as their established counterparts for meta-analysis. Bayesian meta-regressions are also easily performed by analysts who are comfortable using WinBUGS. Estimates of the residual between-study variance in random effects meta-regressions should be routinely reported and accompanied by some measure of their uncertainty. Confidence and/or credible intervals are well-suited to this purpose.
背景
Meta回归越来越多地用于对研究水平协变量效应进行建模。然而，这种类型的统计分析存在许多困难和挑战。本文开发了两种方法来计算随机效应Meta回归模型中研究间方差残差大小的置信区间。还提出了一种使用信息先验分布来计算研究间方差残差可信区间的建议。
方法
将最近提出的两种在随机效应模型假设下用于构建随机效应Meta分析中研究间方差置信区间的精确方法扩展到Meta回归设置。广义 Cochr an异质性统计量的使用扩展到Meta回归设置，并开发了一种牛顿-拉夫逊程序来实现Meta分析和Meta回归的Q轮廓法。在贝叶斯Meta回归的背景下，使用WinBUGS为研究间方差残差实现信息先验。
结果
针对两个对比示例获得了结果，第一个示例涉及二元协变量，第二个示例涉及连续协变量。两个示例中研究间方差残差的区间都很宽。
结论
有统计方法和R计算机软件可用于计算Meta回归随机效应模型下研究间方差残差的精确置信区间。这些频率论方法几乎与已有的Meta分析方法一样易于实施。熟悉使用WinBUGS的分析师也很容易进行贝叶斯Meta回归。随机效应Meta回归中研究间方差残差的估计应常规报告，并伴有其不确定性的某种度量。置信区间和/或可信区间非常适合此目的。
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