A flexible mixed-data model applied to claims data for post-market surveillance of prescription drug safety behavior.

We develop a new modeling framework for jointly modeling first prescription times and the presence of risk-mitigating behavior for prescription drugs using real-world data. We are interested in active surveillance of clinical quality improvement programs, especially for drugs which enter the market under an FDA-mandated Risk Evaluation and Mitigation Strategy (REMS). Our modeling framework attempts to jointly model two important aspects of prescribing, the time between a drug's initial marketing and a patient's first prescription of that drug, and the presence of risk-mitigating behavior at the first prescription. First prescription times can be flexibly modeled as a mixture of component distributions to accommodate different subpopulations and allow the proportion of prescriptions that exhibit risk-mitigating behavior to change for each component. Risk-mitigating behavior is defined in the context of each drug. We develop a joint model using a mixture of positive unimodal distributions to model first prescription times, and a logistic regression model conditioned on component membership to model the presence of risk-mitigating behavior. We apply our model to two recently approved extended release/long-acting (ER/LA) opioids, which have an FDA-approved blueprint for best prescribing practices to inform our definition of risk-mitigating behavior. We also apply our methods to simulated data to evaluate their performance under various conditions such as clustering.