title: Analysis of Binary Adherence Data in the Setting of Polypharmacy: A Comparison of Different Approaches.
abstract: Older community dwelling adults often take multiple medications for numerous chronic diseases. Non-adherence to these medications can have a large public health impact. Therefore, the measurement and modeling of medication adherence in the setting of polypharmacy is an important area of research. We apply a variety of different modeling techniques (standard linear regression; weighted linear regression; adjusted linear regression; naïve logistic regression; beta-binomial (BB) regression; generalized estimating equations (GEE)) to binary medication adherence data from a study in a North Carolina based population of older adults, where each medication an individual was taking was classified as adherent or non-adherent. In addition, through simulation we compare these different methods based on Type I error rates, bias, power, empirical 95% coverage, and goodness of fit. We find that estimation and inference using GEE is robust to a wide variety of scenarios and we recommend using this in the setting of polypharmacy when adherence is dichotomously measured for multiple medications per person.
authors.affiliation: Department of Medicine, Division of General Medicine and Epidemiology, University of North Carolina at Chapel Hill.
authors.forename: Denise A
authors.initials: DA
authors.lastname: Esserman
authors.forename: Charity G
authors.initials: CG
authors.lastname: Moore
authors.forename: Mary T
authors.initials: MT
authors.lastname: Roth