A Bayesian hierarchical model for characterizing the diffusion of new antipsychotic drugs.

ABSTRACT: New prescription medications are a primary driver of spending growth in the United States. For patients with severe mental illnesses, second-generation antipsychotic (SGA) medications feature prominently. However, many SGAs are costly, particularly before generic entry, and some may increase the risk of diabetes. Because physicians play a prominent role in new prescription adoption, understanding their prescribing behaviors is policy-relevant. Several features of prescription data, such as different antipsychotic choice sets over time, variable physician prescription volumes, and correlation among drug choices within physicians, complicate inferences. We propose a multivariate Bayesian hierarchical model with piecewise random effects to characterize the diffusion of new antipsychotic drugs

SUBMITTER: Gu C 

PROVIDER: S-EPMC8108482 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature