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Evaluation of a method to indirectly adjust for unmeasured covariates in the association between fine particulate matter and mortality

TitleEvaluation of a method to indirectly adjust for unmeasured covariates in the association between fine particulate matter and mortality
Year of Publication2019
AuthorsErickson, A. C., Brauer M., Christidis T., Pinault L., Crouse D. L., Donkelaar A., Weichenthal S., Pappin A., Tjepkema M., Martin R. V., Brook J. R., Hystad P., and Burnett R. T.
JournalEnvironmental Research
Volume175
Pages108 - 116
Keywordsair pollution, cohort study, confounding, indirect adjustment, survival analysis
Abstract

{Background Indirect adjustment via partitioned regression is a promising technique to control for unmeasured confounding in large epidemiological studies. The method uses a representative ancillary dataset to estimate the association between variables missing in a primary dataset with the complete set of variables of the ancillary dataset to produce an adjusted risk estimate for the variable in question. The objective of this paper is threefold: 1) evaluate the method for non-linear survival models, 2) formalize an empirical process to evaluate the suitability of the required ancillary matching dataset, and 3) test modifications to the method to incorporate time-varying exposure data, and proportional weighting of datasets. Methods We used the association between fine particle air pollution (PM2.5) with mortality in the 2001 Canadian Census Health and Environment Cohort (CanCHEC

URLhttps://www.sciencedirect.com/science/article/abs/pii/S0013935119302622
DOI10.1016/j.envres.2019.05.010
Document URLhttps://www.sciencedirect.com/customer/institutionchoice?targetURL=%2Fscience%2Farticle%2Fpii%2FS0013935119302622