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

<bold>Objective: </bold>To compare predictive analytic approaches to characterize medication nonadherence and determine under which circumstances each method may be best applied.<bold>Data Sources/study Setting: </bold>Medicare Parts A, B, and D claims from 2007 to 2013.<bold>Study Design: </bold>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.<bold>Data Extraction: </bold>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.<bold>Principal Findings: </bold>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).<bold>Conclusions: </bold>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. [ABSTRACT FROM AUTHOR]