ArticleHealth services research2019
Novel application of approaches to predicting medication adherence using medical claims data.
Article in Health services research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Large language model-based triage to identify antiretroviral therapy adherence barriers and risk levels in patient messages.JAMIA open · 2026Article
- Predicting pharmacy choice for managed care network design.Journal of managed care & specialty pharmacy · 2026Article
- Artificial intelligence approaches to predicting treatment non-adherence in chronic diseases: a narrative review.Frontiers in digital health · 2026Review
- Indigenous Knowledge and Food-Based Coping Mechanisms for ART Side Effects: A Qualitative Inquiry.AIDS research and treatment · 2026Article
- Machine learning approaches to predicting medication nonadherence: a scoping review.International journal of medical informatics · 2025Article
- Development and Validation of Predictive Models for Non-Adherence to Antihypertensive Medication.Medicina (Kaunas, Lithuania) · 2025Article
- Predictive Modeling of Factors Influencing Adherence to SGLT-2 Inhibitors in Ambulatory Care: Insights from Prescription Claims Data Analysis.Pharmacy (Basel, Switzerland) · 2024Article
- Comparison of machine learning approaches for positive airway pressure adherence prediction in a veteran cohort.Frontiers in sleep · 2024Article
- A dynamic analysis of medication adherence.Journal of managed care & specialty pharmacy · 2022Article
- Comparing and validating medication complexity from insurance claims against electronic health records.Journal of managed care & specialty pharmacy · 2022Article
- Prior Cardiovascular Treatments-A Key Characteristic in Determining Medication Adherence After an Acute Myocardial Infarction.Frontiers in pharmacology · 2022Article
- Patient Adherence to Therapy After Myocardial Infarction: A Scoping Review.Patient preference and adherence · 2022Article
- Article
- Statistical methods versus machine learning techniques for donor-recipient matching in liver transplantation.PloS one · 2021Article
- Predicting adjuvant endocrine therapy initiation and adherence among older women with early-stage breast cancer.Breast cancer research and treatment · 2020Article
- Novel application of approaches to predicting medication adherence using medical claims data.Health services research · 2019Article
- Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
objectiveTo compare predictive analytic approaches to characterize medication nonadherence and determine under which circumstances each method may be best applied. DATA SOURCES/STUDY
settingMedicare Parts A, B, and D claims from 2007 to 2013. STUDY
designWe evaluated three statistical techniques to predict statin adherence (proportion of days covered [PDC ≥ 80 percent]) in the year following discharge: standard logistic regression with backward selection of covariates, least absolute shrinkage and selection operator (LASSO), and random forest. We used the C-index to assess model discrimination and decile plots comparing predicted values to observed event rates to evaluate model performance. DATA EXTRACTION: We identified 11 969 beneficiaries with an acute myocardial infarction (MI)-related admission from 2007 to 2012, who filled a statin prescription at, or shortly after, discharge. PRINCIPAL
findingsIn all models, prior statin use was the most important predictor of future adherence (OR = 3.65, 95% CI: 3.34-3.98; OR = 3.55). Although the LASSO regression model selected nearly 90 percent of all candidate predictors, all three analytic approaches had moderate discrimination (C-index ranging from 0.664 to 0.673).
conclusionsAlthough none of the models emerged as clearly superior, predictive analytics could proactively determine which patients are at risk of nonadherence, thus allowing for timely engagement in adherence-improving interventions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.