Sharon-Lise T. Normand is the principal investigator on this project. Jennifer Grandfield is the project contact: 617-432-3287. 

A team of statisticians, economists, and clinicians are collaborating on the development and application of longitudinal hierarchical discrete choice models for understanding diffusion of mental health technologies and for causal inference. By developing better statistical models for understanding the dynamics of treatment adoption and rejection, researchers will gain greater insight into longitudinal patterns of usual care treatments.

The work extends likelihood-based approaches to accommodate a flexible family of dynamic discrete choice diffusion models. This permits the study of the effects of patient, provider, product, and market characteristics on technology adoption or rejection. The study also includes geographic variation in the diffusion of mental health treatment technologies, permitting estimation of geographic-specific diffusion effects. Finally, the researchers are studying the causal effect of patient, provider, product, and market characteristics on diffusion, applying these methods to cohorts of patients with depression, bipolar affective disorder, and schizophrenia using multiple sources of data.

The methodological advances from this research will enable researchers, policymakers, and methodologists to better characterize factors affecting technology adoption or rejection and to expand the inferences for usual care.