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.