A further critique of the analytic strategy of adjusting for covariates to identify biologic mediation
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Kaufman, Jay S, Richard F Mac Lehose, and Sol Kaufman. A Further Critique of the Analytic Strategy of Adjusting for Covariates to Identify Biologic Mediation. BioMed Central Ltd, 2004. https://doi.org/10.17615/q6ng-db08APA
Kaufman, J., Mac Lehose, R., & Kaufman, S. (2004). A further critique of the analytic strategy of adjusting for covariates to identify biologic mediation. BioMed Central Ltd. https://doi.org/10.17615/q6ng-db08Chicago
Kaufman, Jay S., Richard F Mac Lehose, and Sol Kaufman. 2004. A Further Critique of the Analytic Strategy of Adjusting for Covariates to Identify Biologic Mediation. BioMed Central Ltd. https://doi.org/10.17615/q6ng-db08- Creator
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Kaufman, Jay S.
- Affiliation: Gillings School of Global Public Health, Department of Epidemiology
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MacLehose, Richard F.
- Affiliation: Gillings School of Global Public Health, Department of Epidemiology
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Kaufman, Sol
- Other Affiliation: Department of Otolaryngology, University at Buffalo
- Abstract
- Abstract Background Epidemiologic research is often devoted to etiologic investigation, and so techniques that may facilitate mechanistic inferences are attractive. Some of these techniques rely on rigid and/or unrealistic assumptions, making the biologic inferences tenuous. The methodology investigated here is effect decomposition: the contrast between effect measures estimated with and without adjustment for one or more variables hypothesized to lie on the pathway through which the exposure exerts its effect. This contrast is typically used to distinguish the exposure's indirect effect, through the specified intermediate variables, from its direct effect, transmitted via pathways that do not involve the specified intermediates. Methods We apply a causal framework based on latent potential response types to describe the limitations inherent in effect decomposition analysis. For simplicity, we assume three measured binary variables with monotonic effects and randomized exposure, and use difference contrasts as measures of causal effect. Previous authors showed that confounding between intermediate and the outcome threatens the validity of the decomposition strategy, even if exposure is randomized. We define exchangeability conditions for absence of confounding of causal effects of exposure and intermediate, and generate two example populations in which the no-confounding conditions are satisfied. In one population we impose an additional prohibition against unit-level interaction (synergism). We evaluate the performance of the decomposition strategy against true values of the causal effects, as defined by the proportions of latent potential response types in the two populations. Results We demonstrate that even when there is no confounding, partition of the total effect into direct and indirect effects is not reliably valid. Decomposition is valid only with the additional restriction that the population contain no units in which exposure and intermediate interact to cause the outcome. This restriction implies homogeneity of causal effects across strata of the intermediate. Conclusions Reliable effect decomposition requires not only absence of confounding, but also absence of unit-level interaction and use of linear contrasts as measures of causal effect. Epidemiologists should be wary of etiologic inference based on adjusting for intermediates, especially when using ratio effect measures or when absence of interacting potential response types cannot be confidently asserted.
- Date of publication
- October 8, 2004
- DOI
- Identifier
- Resource type
- Article
- Rights statement
- In Copyright
- Rights holder
- Jay S Kaufman et al.; licensee BioMed Central Ltd.
- License
- Journal title
- Epidemiologic Perspectives & Innovations
- Journal volume
- 1
- Journal issue
- 1
- Page start
- 4
- Language
- English
- Is the article or chapter peer-reviewed?
- Yes
- ISSN
- 1742-5573
- Bibliographic citation
- Epidemiologic Perspectives & Innovations. 2004 Oct 08;1(1):4
- Publisher
- BioMed Central Ltd
- Access right
- Open Access
- Date uploaded
- September 5, 2012
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