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Poor adherence is a particular problem for patients with chronic diseases requiring long\u2010term 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\u2010intensive or do not successfully detect low\u2010adherers 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\u2010varying adherence behaviors. Our framework permits the inclusion of baseline health characteristics and socio\u2010demographic data. We employ a modular inferential approach. First, we fit a two\u2010component 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\u2010demographic 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\u2010varying 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\u2010demographic measures, and blood pressure measured over time to infer patients' adherence to antihypertensive medication.<\/jats:p>","DOI":"10.1002\/sim.9351","type":"journal-article","created":{"date-parts":[[2022,2,9]],"date-time":"2022-02-09T04:29:29Z","timestamp":1644380969000},"page":"2205-2226","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Inferring medication adherence from time\u2010varying health measures"],"prefix":"10.1002","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5678-4620","authenticated-orcid":false,"given":"Kristen B.","family":"Hunter","sequence":"first","affiliation":[{"name":"Department of Statistics Harvard University Cambridge Massachusetts USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mark E.","family":"Glickman","sequence":"additional","affiliation":[{"name":"Department of Statistics Harvard University Cambridge Massachusetts USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luis F.","family":"Campos","sequence":"additional","affiliation":[{"name":"Etsy, Inc. 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