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

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

Author: Anders Erickson, Michael Brauer, Tanya Christidis, Lauren Pinault, Daniel L. Crouse, Aaron van Donkelaar, Scott Weichenthal, Amanda Pappin, Michael Tjepkema, Randall V.Martin, Jeffrey R.Brook, Perry Hystad, Richard T.Burnett
Year: 2019
Category: Health Publications

Read the journal article in Science Direct

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.