Meta-analysis of published excess relative risk estimates
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ORIGINAL ARTICLE
Meta‑analysis of published excess relative risk estimates David B. Richardson1 · Kossi Abalo2 · Marie‑Odile Bernier2 · Estelle Rage2 · Klervi Leuraud2 · Dominique Laurier2 · Alexander P. Keil1 · Mark P. Little3 Received: 19 April 2020 / Accepted: 11 July 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract 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. Keywords Meta-analysis · Cohort studies · Excess relative risk · Cancer Abbreviations RR Relative rate CI Confidence interval ERR Excess relative risk Sv Sievert
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00411-020-00863-w) contains supplementary material, which is available to authorized users. * David B. Richardson [email protected] 1
Department of Epidemiology, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
2
Institut de Radioprotection et de Sûreté Nucléaire, Paris, France
3
Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
Introduction In a meta-analysis of epidemiological study results, a summary effect estimate is obtained by combining information from a set of study-specific estimates. A common approach is to calculate an inverse-variance-weighted average of the study-specific estimates of association [e.g., Sutton et al. (2000), United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR) (2018)]. This approach assigns more weight to studies with more precise studyspecific estimates of a
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