Adherence Prediction Algorithms to Explain Treatment Heterogeneity and Guide Adherence Improvement [Methods Study], United States, 2014-2019 (ICPSR 39572)
Version Date: Nov 24, 2025 View help for published
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Joshua J. Gagne, Brigham and Women's Hospital
https://doi.org/10.3886/ICPSR39572.v1
Version V1
Summary View help for Summary
If patients don't take medicines as directed, the medicines don't work as well for treating a health problem. It may also lead to more health problems. If doctors knew which patients were less likely to take medicines as directed, they could find ways to help these patients.
In this study, the research team wanted to learn if knowing who took medicines as directed in the past would predict if patients take a new medicine as directed. The team created two statistical models to predict if patients would take a medicine as directed. First, the research team created a model to predict if patients would take medicines to lower cholesterol. Then, they created a second model using data from these patients plus others who were taking medicines to lower blood pressure or strengthen bones.
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Study Purpose View help for Study Purpose
The objectives of this project were to (1) develop and compare algorithms for predicting adherence to various interventions, based on data collected prior to the start of those interventions; (2) assess whether predicted adherence explains heterogeneity of treatment effects in Patient-Centered Outcomes Research (PCOR) studies; and (3) develop and compare different algorithms for predicting adherence during treatment
Study Design View help for Study Design
Poor medication adherence leads to adverse clinical outcomes and substantial avoidable medical spending. Identifying patients who are likely to have low adherence may help develop effective interventions. This study developed algorithms to predict full adherence to a newly initiated medication based on patients' prior medication adherence. The research team defined full adherence as at least 80% proportion of days covered (PDC), which is the proportion of days on which a patient has medication available during a year.
The study included data for 89,490 patients who had a statin dispensed during the study period and who had at least one different medication dispensed, used for measuring prior adherence. In addition, the study used data from 67,607 bisphosphonates initiators and 109,059 antihypertensives initiators.
The research team split the sample into training and testing cohorts. Using data from the training cohort, the team fit lasso logistic regressions to identify variables that could predict full adherence to statins other than prior adherence. With these variables and measures of prior adherence, the team used multiple logistic regressions to predict full adherence to statins. The team then applied each model from the training cohort to the testing cohort and selected the best performing algorithm.
To test whether the selected statin adherence prediction algorithm could apply to other drug classes, the research team applied the algorithm in cohorts of patients starting bisphosphonates or antihypertensives. The team compared the performance of the statin algorithm with algorithms they developed for bisphosphonates and antihypertensives. Finally, the team pooled all patients and developed a unified algorithm to predict adherence to any of the three drug classes and compared its performance to the class-specific algorithms.
A panel of 10 patients provided input into the design and conduct of the study.
Data Source View help for Data Source
Healthcare claims database for 176,666 insurance beneficiaries enrolled in commercial UnitedHealth Group health plans and patients with a Medicare supplement plan
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