A Systematic Approach for Analyzing Electronically Monitored Adherence Data pp. 1-66
Authors: (G. J. Knafl, K. L. Delucchi, C. A. Bova, K. P. Fennie, K. Ding, A. B. Williams, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA, and others)
Abstract: We propose a 4-step process for analyzing medication adherence data generated by MEMS and similar electronic monitoring devices. SAS macros developed to support this analysis process are available on the Internet. An overview of these methods and macros is provided. Example analyses are presented to demonstrate these methods using MEMS data on HIV positive subjects' adherence to antiretroviral medications. The four analysis steps are formulated including new extensions for adaptive modeling of the dispersion as well as of the expected value, i.e., variability in adherence as well as its mean. How to use the macros to conduct the example analyses is also described. The steps of the analysis process are: 1. Group MEMS opening events for each subject into opening counts and rates over disjoint intervals within that subject’s MEMS usage period. 2. Model grouped counts/rates for each subject using adaptive Poisson regression methods, fitting non-linear curves in time to the expected value and dispersion. 3. Cluster estimates of the expected value and dispersion at proportional times (e.g., every 5%) within subjects’ MEMS usage periods into adherence pattern types (e.g., high, moderate, low, improving, deteriorating). 4. Model membership in adherence pattern types in terms of available predictors.
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