Bayesian hierarchical dose-response meta-analysis of epidemiological studies: Modeling and target population prediction methods
| Autor: | J. Allen Davis, Bruce C. Allen, William Mendez, Kan Shao, Ingrid L. Druwe, Kevin Hobbie, Ila Cote, Jeffrey S. Gift, Janice S. Lee |
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| Rok vydání: | 2019 |
| Předmět: |
010504 meteorology & atmospheric sciences
Computer science Population Bayesian probability 010501 environmental sciences Logistic regression 01 natural sciences Hierarchical database model Arsenicals Article Cohort Studies Econometrics Humans education lcsh:Environmental sciences 0105 earth and related environmental sciences General Environmental Science Estimation lcsh:GE1-350 education.field_of_study Incidence Bayesian dose-response Bayes Theorem Random effects model Lifetable analysis United States Meta-analysis Epidemiologic Studies Hierarchical model Cohort study |
| Zdroj: | Environ Int Environment International, Vol 145, Iss, Pp 106111-(2020) |
| ISSN: | 1873-6750 |
| Popis: | When assessing the human risks due to exposure to environmental chemicals, traditional dose-response analyses are not straightforward when there are numerous high-quality epidemiological studies of priority cancer and non-cancer health outcomes. Given this wealth of information, selecting a single “best” study on which to base dose-response analyses is difficult and would potentially ignore much of the available data. Therefore, systematic approaches are necessary for the analysis of these rich databases. Examples are meta-analysis (and further, meta-regression), which are well established methods that consider and incorporate information from multiple studies into the estimation of risks due to exposure to environmental contaminants. In this paper, we propose a hierarchical, Bayesian meta-analysis approach for the dose-response analysis of multiple epidemiological studies. This paper is the second of two papers detailing this approach; the first covered “pre-analysis” steps necessary to prepare the data for dose-response modeling. This paper focuses on the hierarchical Bayesian approach to dose-response modeling and extrapolation of risk to populations of interest using the association between bladder cancer and oral inorganic arsenic (iAs) exposure as an illustrative case study. In particular, this paper addresses the modeling of both case-control and cohort studies with a flexible, logistic model in a hierarchical Bayesian framework that estimates study-specific slopes, as well as a pooled slope across all studies. This approach is akin to a random effects model in which no assumption is made a priori that there is a single, common slope for all included studies. Further, this paper also details extrapolation of the estimates of logistic slope to extra risk in a target population using a lifetable analysis and basic assumptions about background iAs exposure levels. In this case, the target population was the general United States population and information on all-cause mortality and incidence and mortality from bladder cancer was used to perform the lifetable analysis. The methods herein were developed for general use in investigating the association between any pollutant and observed health-effects in epidemiological studies. In order to demonstrate these methods, inorganic arsenic was chosen as a case study given the large epidemiological database that exists for this contaminant. |
| Databáze: | OpenAIRE |
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