title: Modeling treatment effects on binary outcomes with grouped-treatment variables and individual covariates.
abstract: 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.
authors.affiliation: Department of Neurology, University of California, San Francisco, 94143, USA. clay.johnston@ucsfmedctr.org
authors.forename: S Claiborne
authors.initials: SC
authors.lastname: Johnston
authors.forename: Tanya
authors.initials: T
authors.lastname: Henneman
authors.forename: Charles E
authors.initials: CE
authors.lastname: McCulloch
authors.forename: Mark
authors.initials: M
authors.lastname: van der Laan
mesh_terms: D015986:Confounding Factors, Epidemiologic; D004463:Ecology; D016021:Epidemiologic Studies; D006801:Humans; D015233:Models, Statistical; D016017:Odds Ratio; D015203:Reproducibility of Results; D012107:Research Design