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Register a new userInferring medication adherence from time-varying health measures
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Kristen Hunter
Publication date
2021
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English
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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 patients 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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One of the most significant barriers to medication treatment is patients non-adherence to a prescribed medication regimen. The extent of the impact of poor adherence on resulting health measures is often unknown, and typical analyses ignore the time-varying nature of adherence. This paper develops a modeling framework for longitudinally recorded health measures modeled as a function of time-varying medication adherence or other time-varying covariates. Our framework, which relies on normal Bayesian dynamic linear models (DLMs), accounts for time-varying covariates such as adherence and non-dynamic covariates such as baseline health characteristics. Given the inefficiencies using standard inferential procedures for DLMs associated with infrequent and irregularly recorded response data, we develop an approach that relies on factoring the posterior density into a product of two terms; a marginal posterior density for the non-dynamic parameters, and a multivariate normal posterior density of the dynamic parameters conditional on the non-dynamic ones. This factorization leads to a two-stage process for inference in which the non-dynamic parameters can be inferred separately from the time-varying parameters. We demonstrate the application of this model to the time-varying effect of anti-hypertensive medication on blood pressure levels from a cohort of patients diagnosed with hypertension. Our model results are compared to ones in which adherence is incorporated through non-dynamic summaries.
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