Electronic monitoring device event modelling on an individual-subject basis using adaptive Poisson regression.
An adaptive approach to Poisson regression modelling is presented for analysing event data from electronic devices monitoring medication-taking. The emphasis is on applying this approach to data for individual subjects although it also applies to data for multiple subjects. This approach provides for visualization of adherence patterns as well as for objective comparison of actual device use with prescribed medication-taking. Example analyses are presented using data on openings of electronic pill bottle caps monitoring adherence of subjects with HIV undergoing highly active antiretroviral therapies. The modelling approach consists of partitioning the observation period, computing grouped event counts/rates for intervals in this partition, and modelling these event counts/rates in terms of elapsed time after entry into the study using Poisson regression. These models are based on adaptively selected sets of power transforms of elapsed time determined by rule-based heuristic search through arbitrary sets of parametric models, thereby effectively generating a smooth non-parametric regression fit to the data. Models are compared using k-fold likelihood cross-validation.
School of Nursing, Yale University, New Haven, CT, USA. george.knafl@yale.edu
George J
GJ
Knafl
Kristopher P
KP
Fennie
Carol
C
Bova
Kevin
K
Dieckhaus
Ann B
AB
Williams
D023241:Antiretroviral Therapy, Highly Active; D015658:HIV Infections / Q000188:drug therapy*; D006801:Humans; D016013:Likelihood Functions; D008991:Monitoring, Physiologic / Q000295:instrumentation*; D010349:Patient Compliance; D016012:Poisson Distribution