  Medication adherence is a problem of widespread concern in clinical care.
Poor adherence is a particular problem for patients with chronic diseases
requiring long-term medication because poor adherence can result in less
successful treatment outcomes and even preventable deaths. Existing methods to
collect information about patient adherence are resource-intensive or do not
successfully detect low-adherers with high accuracy. Acknowledging that health
measures recorded at clinic visits are more reliably recorded than a patient's
adherence, we have developed an approach to infer medication adherence rates
based on longitudinally recorded health measures that are likely impacted by
time-varying adherence behaviors. Our framework permits the inclusion of
baseline health characteristics and socio-demographic data. We employ a modular
inferential approach. First, we fit a two-component model on a training set of
patients who have detailed adherence data obtained from electronic medication
monitoring. One model component predicts adherence behaviors only from baseline
health and socio-demographic information, and the other predicts longitudinal
health measures given the adherence and baseline health measures. Posterior
draws of relevant model parameters are simulated from this model using Markov
chain Monte Carlo methods. Second, we develop an approach to infer medication
adherence from the time-varying health measures using a Sequential Monte Carlo
algorithm applied to a new set of patients for whom no adherence data are
available. We apply and evaluate the method on a cohort of hypertensive
patients, using baseline health comorbidities, socio-demographic measures, and
blood pressure measured over time to infer patients' adherence to
antihypertensive medication.
