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

ABSTRACT:

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 myo

SUBMITTER: Zullig LL 

PROVIDER: S-EPMC6863234 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature