Key result
A novel state-space model incorporating time-varying blood pressure and baseline covariates successfully inferred unobserved medication adherence, with 80% posterior intervals achieving 90% actual coverage.
Why the study?
Existing methods to collect information about patient medication adherence are resource-intensive or fail to detect low-adherers with high accuracy.
A novel statistical framework using state-space models and Sequential Monte Carlo can successfully infer medication adherence from routinely collected time-varying health measures such as blood pressure.
May support remote adherence monitoring via routine BP data; leaves open prospective validation before clinical use.
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.
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Hunter et al. (2022) studied Hypertension (n=503). Statistical model for inferring medication adherence was evaluated on Actual coverage of 80% posterior intervals for estimated average adherence. A novel state-space model incorporating time-varying blood pressure and baseline covariates successfully inferred unobserved medication adherence, with 80% posterior intervals achieving 90% actual coverage.
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