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A Bayesian hierarchical model for characterizing the diffusion of new antipsychotic drugs
Author(s) -
Gu Chenyang,
Huskamp Haiden,
Donohue Julie,
Normand SharonLise
Publication year - 2021
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.13324
Subject(s) - medical prescription , antipsychotic , specialty , bayesian probability , medicine , polypharmacy , bayesian inference , econometrics , psychiatry , family medicine , computer science , schizophrenia (object oriented programming) , intensive care medicine , artificial intelligence , pharmacology , mathematics
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. This model captures the complex prescriber‐specific relationships among the different diffusion processes and takes advantage of the Bayesian paradigm to quantify uncertainty for all parameters straightforwardly. To evaluate the prescribing patterns for each physician, we propose various indices to identify early new SGA adopters. A sample of nearly 17,000 US physicians whose antipsychotic drug prescribing information was collected between January 1, 1997 and December 31, 2007 illustrates the methods. Determinants of high prescription rates and adoption speeds of new SGAs included physician sex, age, hospital affiliation, physician specialty, and office location. Large within‐ and between‐provider variations in prescribing patterns of new SGAs were identified. Early adopters for one drug were not early adopters for another drug.