Journal Article

Modeling Treatment Effects on Binary Outcomes with Grouped-Treatment Variables and Individual Covariates

S. Claiborne Johnston, Tanya Henneman, Charles E. McCulloch and Mark van der Laan

in American Journal of Epidemiology

Published on behalf of Johns Hopkins Bloomberg School of Public Health

Volume 156, issue 8, pages 753-760
Published in print October 2002 | ISSN: 0002-9262
Published online October 2002 | e-ISSN: 1476-6256 | DOI: http://dx.doi.org/10.1093/aje/kwf095
Modeling Treatment Effects on Binary Outcomes with Grouped-Treatment Variables and Individual Covariates

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During evaluation of treatment effects in observational studies, confounding is a constant threat because it is always possible that patients with a better prognosis, not adequately characterized by measured covariates, are chosen for a specific therapy. Ecologic analyses may avoid confounding that would be present in analysis at the individual level because variations in regional or hospital practice may be unrelated to prognosis. The authors used simulated data with an excluded confounder to evaluate the reliability and limitations of the grouped-treatment approach, a method of incorporating an ecologic measure of treatment assignment into an individual-level multivariable model, similar to the instrumental variable approach. Estimates based on the grouped-treatment approach were closer to the true value than those of standard individual-level multivariable analysis in every simulation. Furthermore, confidence intervals based on the grouped-treatment approach achieved approximately their nominal coverage, whereas those based on individual-level analyses did not. The grouped-treatment approach appears to be more reliable than standard individual-level analysis in situations where the grouped-treatment variable is unassociated with the outcome except via the actual treatment assignment and measured covariates.

Keywords: confounding factors (epidemiology); ecology; instrumental variable; selection bias; simulation; Abbreviation: OR, odds ratio.

Journal Article.  5694 words. 

Subjects: Public Health and Epidemiology

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