Predictive modeling of medication adherence in post myocardial infarction patients: a bayesian approach using beta-regression.

Abstract

Aims: Predicting medication adherence in post myocardial infarction (MI) patients has the potential to improve patient outcomes. Most adherence prediction models dichotomize adherence metrics and status. This study aims to develop medication adherence prediction models that avoid dichotomizing adherence metrics and to test whether a simplified model including only 90-days adherence data would perform similarly to a full multivariable model.

Methods: Post MI adult patients were followed for 1-year post the event. Data from pharmacy records were used to calculate proportion of days covered (PDC). We used Bayesian beta-regression to model PDC as a proportion, avoiding dichotomization. For each medication group, statins, P2Y12 inhibitors and aspirin, two prediction models were developed, a full and a simplified model.

Results: 3692 patients were included for model development. The median (Inter quartile range) PDC at 1-year for statins, P2Y12 inhibitors and aspirin was 0.8 (0.33, 1.00), 0.79 (0.23, 0.99) and 0.79 (0.23, 0.99), respectively. All models showed good fit to the data by visual predictive checks. Bayesian R2 for statins, P2Y12 inhibitors and aspirin models were 61.4%,71.2% and 55.2%, respectively. The simplified models showed similar performance compared with full complex models as evaluated by cross validation.

Conclusions: We developed Bayesian multilevel models for statins, P2Y12 inhibitors and aspirin in post MI patients that handled 1-year PDC as a proportion using the beta-distribution. In addition, simplified models, with 90-days adherence as single predictor, had similar performance compared with full complex models.