A meta-analytic summary effect estimate often is calculated as an inverse-variance-weighted average of study-specific estimates of association. The variances of published estimates of association often are derived from their associated confidence intervals under assumptions typical of Wald-type statistics, such as normality of the parameter. However, in some research areas, such as radiation epidemiology, epidemiological results typically are obtained by fitting linear relative risk models, and associated likelihood-based confidence intervals are often asymmetric; consequently, reasonable estimates of variances associated with study-specific estimates of association may be difficult to infer from the standard approach based on the assumption of a Wald-type interval. Here, a novel method is described for meta-analysis of published results from linear relative risk models that uses a parametric transformation of published results to improve on the normal approximation used to assess confidence intervals. Using simulations, it is illustrated that the meta-analytic summary obtained using the proposed approach yields less biased summary estimates, with better confidence interval coverage, than the summary obtained using the more classical approach to meta-analysis. The proposed approach is illustrated using a previously published example of meta-analysis of epidemiological findings regarding circulatory disease following exposure to low-level ionizing radiation.

荟萃分析总结效应估计通常计算为特定于研究的关联估计的逆方差加权平均值。已发表的关联估计的方差通常是根据 Wald 型统计的典型假设（例如参数的正态性）下的相关置信区间得出的。然而，在一些研究领域，例如辐射流行病学，流行病学结果通常是通过拟合线性相对风险模型获得的，并且相关的基于可能性的置信区间往往是不对称的；因此，与研究特定的关联估计相关的合理方差估计可能很难从基于 Wald 型区间假设的标准方法中推断出来。在这里，描述了一种新方法，用于对线性相对风险模型的已发表结果进行荟萃分析，该方法使用已发表结果的参数转换来改进用于评估置信区间的正态近似。通过模拟，结果表明，与使用更经典的荟萃分析方法获得的摘要相比，使用所提出的方法获得的荟萃分析摘要产生的摘要估计偏差较小，具有更好的置信区间覆盖范围。使用先前发表的关于暴露于低水平电离辐射后循环系统疾病的流行病学发现的荟萃分析的示例来说明所提出的方法。