Electronic monitoring device event modelling on an individual‐subject basis using adaptive Poisson regression

Abstract

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

Home Visits to Improve Adherence to Highly Active Antiretroviral Therapy

“…Preliminary, exploratory studies of data analysis strategies suggest that it is possible to characterize categories of adherence behavior patterns. 40 If successful, these new approaches to analyzing electronic adherence data may improve our understanding of which categories of patients most benefit from specific interventions.…”

Analysis of HIV Medication Adherence in Relation to Person and Treatment Characteristics Using Hierarchical Linear Modeling

“…Moreover, studies investigating adherence to ART tend to be cross-sectional or retrospective, and studies that do utilize prospective longitudinal methodologies tend to use simple pairwise comparisons and repeated-measures analysis of variance, but rarely use more advanced multilevel modeling. 46,54 Because of such methodological confounds, we designed and conducted a larger-scale investigation over a more extended period of time in order to gain a better understanding of ART adherence and the predictors, which have been previously studied cross-sectionally. This design, in combination with MEMS technology and more advanced, multilevel statistical methodology, leads to a more powerful understanding of adherence across a time span.…”

Modeling Virologic Response in HIV-1 Infected Patients to Assess Medication Adherence

“…Several studies investigated the association between virologic responses and adherence assessed by MEMS data only without considering other confounding factors such as drug resistance using standard modeling methods including Poisson regression (Knafl et al, 2004), logistic regression (Vrijens et al, 2005) and linear mixed-effects model (Liu et al, 2007). In this article, we developed a mechanism-based nonlinear time-varying differential equation model for long-term dynamics to (i) establish the relationship of virologic response (viral load trajectory) with drug adherence and drug resistance, (ii) to describe both suppression and resurgence of virus, (iii) to directly incorporate observed drug adherence and susceptibility into a function of treatment efficacy and (iv) to use a hierarchical Bayesian mixed-effects modeling approach that can not only combine prior information with current clinical data for estimating dynamic parameters, but also characterize inter-subject variability.…”