摘要：Highlights•Often summary epidemiologic data needs pre-analysis before dose-response.•Estimated effective counts retain the control for confounders in individual studies.•Estimating dose metrics via probabilistic methods address interindividual variation.•The methods in this paper are suitable prior to individual study or meta-analyses.AbstractMeta-analysis approaches can be used to assess the human risks due to exposure to environmental chemicals when there are numerous high-quality epidemiologic studies of priority outcomes in a database. However, methodological issues related to how different studies report effect measures and incorporate exposure into their analyses arise that complicate the pooled analysis of multiple studies. As such, there are “pre-analysis” steps that are often necessary to prepare summary data reported in epidemiologic studies for dose-response analysis. This paper uses epidemiologic studies of arsenic-induced health effects as a case example and addresses the issues surrounding the estimation of mean doses from censored dose- or exposure-intervals reported in the literature (e.g., estimation of mean doses from high exposures that are only reported as an open-ended interval), calculation of a common dose metric for use in a dose-response meta-analysis (one that takes into consideration inter-individual variability), and calculation of response “effective counts” that inherently account for confounders. The methods herein may be generalizable to 1) the analysis of other environmental contaminants with a suitable database of epidemiologic studies, and 2) any meta-analytic approach used to pool information across studies. A second companion paper detailing the use of “pre-analyzed” data in a hierarchical Bayesian dose-response model and techniques for extrapolating risks to target populations follows.
