Novel application of approaches to predicting medication adherence using medical claims data.

Journal Article (Journal Article)

OBJECTIVE: To compare predictive analytic approaches to characterize medication nonadherence and determine under which circumstances each method may be best applied. DATA SOURCES/STUDY SETTING: Medicare Parts A, B, and D claims from 2007 to 2013. STUDY DESIGN: We 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 FINDINGS: In 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). CONCLUSIONS: Although 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.

Full Text

Duke Authors

Cited Authors

  • Zullig, LL; Jazowski, SA; Wang, TY; Hellkamp, A; Wojdyla, D; Thomas, L; Egbuonu-Davis, L; Beal, A; Bosworth, HB

Published Date

  • December 2019

Published In

Volume / Issue

  • 54 / 6

Start / End Page

  • 1255 - 1262

PubMed ID

  • 31429471

Pubmed Central ID

  • PMC6863234

Electronic International Standard Serial Number (EISSN)

  • 1475-6773

Digital Object Identifier (DOI)

  • 10.1111/1475-6773.13200


  • eng

Conference Location

  • United States