Meta‐analytic approaches to dose‐response relationships, with application in studies of lung cancer and exposure to environmental tobacco smoke
Abstract
This paper outlines several meta‐analytic approaches to the assessment of quantal dose—response relationships; that is, to the evaluation of an increase in the level of exposure to an agent and the associated relative risk of a disease when this is investigated over a number of different studies. Analysis is developed at two levels: first, a consistent method of evaluating the dose—response relationship is applied to each study, and second, an overall picture is obtained by comparing and combining these relationships. At the first stage, for an individual study, dose—response assessment involves choices of model and appropriate tests for trend, which are influenced by such issues as dose measurement and use of the unexposed group. At the second stage, different methods for pooling results across studies must be considered. These depend on the choices made in the first stage of analysis, with additional attention paid to heterogeneity, and possible bias due to studies included in meta‐analysis. We describe these meta‐analytic approaches for three methods of evaluating dose response. The approaches are illustrated by evaluating the relationship between lung cancer and levels of exposure to environmental tobacco smoke (ETS). The strength of this relationship has been a point of debate in recent assessment of evidence for an overall carcinogenic effect of ETS exposure. We find little indication of a consistent dose response, a result explained in terms of recent models for cancer and passive smoking developed by Darby and Pike, the current meta‐analysis results of overall risk‐ratios of current studies in Tweedie and Mengersen, and misclassification models developed by the United States Environmental Protection Agency (EPA).
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Other topics included meta-regression, while few articles focused on more specific statistical issues, such as dose-response analysis, credibility ceilings, bias adjustment, hierarchical models, or regression bias.
“Other topics included meta-regression, while few articles focused on more specific statistical issues, such as dose-response analysis [81, 109, 111, 113], credibility ceilings [110], bias adjustment [112], hierarchical models [117], or regression bias [108].”
From Methods to systematically review and meta-analyse observational studies: a systematic scoping review of recommendations (2018) · cites this paper as [113]
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References 44 · Cited by 35
- Authors
- Tweedie, R. L., & Mengersen, K. L.
- Affiliations
- Colorado State University (US)
- ORCID
- Mengersen, K. L.
- Venue
- Statistics in Medicine
- Publisher
- Wiley
- Published
- 1995-03-15
- Volume
- 14
- Issue
- 5-7
- Pages
- 545-569
- ISSN
- 0277-6715 (ISSN-L)
- 1097-0258
- OpenAlex
- W2148141165
- Cited by
- 35
- References
- 44
- Citations by year
- 2025: 1 · 2020: 1 · 2018: 1 · 2013: 2
- FWCI
- 2.93
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Statistics in Medicine (publishedVersion)
- PubMed (publishedVersion)
- Funders
- U.S. Environmental Protection Agency
- Colorado State University
- Bond University
- Centers for Disease Control and Prevention
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Health Sciences › Medicine › Oncology › Global Cancer Incidence and Screening
- Keywords
- dose-response relationship, meta-analysis, lung cancer, environmental tobacco smoke, passive smoking, relative risk, dose measurement, heterogeneity, cancer risk assessment, EPA risk assessment
- MeSH terms
- Dose-Response Relationship, Drug, Environmental Exposure, Female, Humans, Lung Neoplasms, Male, Maximum Allowable Concentration, Tobacco Smoke Pollution, Meta-Analysis as Topic, Cohort Studies, Linear Models, Least-Squares Analysis, Case-Control Studies, Risk Assessment
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