The University of Chicago Research Funding

• Principal Investigator: Anirban Basu, PhD, Section of Hospital Medicine, Assistant Professor of Medicine
• Start Date: March 1, 2010
• Total Award Amount: $194,392

Public Health Relevance

Clinically, our findings have the potential to have important implications for the treatment of schizophrenia by providing physicians and their patients with rich information on the distribution of outcomes of treatments that can help guide them in making more informed treatment choices. Furthermore, the proposed value of information analyses will direct future research and resources in this field by identifying research priorities on those parameters where more precise estimates would be most valuable.

Controversies are growing regarding the use of second generation versus the first generation antipsychotics by patients with schizophrenia in the face of rising costs and ambiguous evidence on the benefits of the newer drugs. Frequent switching between alternative drugs indicates that no one drug may be optimal for a patient. Enormous uncertainties in current estimates of treatment effect imply that the value of future research in this filed may be substantial. In order to address these questions, the proposed work aims to develop a comprehensive micro-simulation model to assess the costs, effectiveness, cost-effectiveness of alternative pharmacological treatment algorithms in schizophrenia and to conduct value of information and value of future research analyses in this field.

Project Description

Schizophrenia affects about 1.3% of the population and yet is responsible for US$28 billion in annual health care costs. The burden of schizophrenia to the patients, their family members and to the society is large. Antipsychotic drugs are the first line of treatment of schizophrenia and have helped some patients with this disease to lead productive and fulfilling lives.

With the advent of the second-generation of antipsychotic drugs, which are typically much more expensive than first generation antipsychotics, the growth in medical expenditures among these patients have risen steadily, calling into question the marginal value of the newer second-generation antipsychotic drugs (atypicals) over the older generation neuroleptics. There is also ambiguous evidence on whether pharmaceutical expenditures can offset the expensive inpatient care for these patients. Consequently, controversies are growing regarding the use of these newer and more expensive drugs, especially when some of them have recently documented evidence of increasing cardiovascular risks in this already vulnerable population.

This debate is further fueled by the recently published results from the NIMH funded CATIE study that reported equivalence of continuation rates between patient randomized to receiving first-generation versus atypical antipsychotic drugs. In the presence of such controversy, there remain crucial questions to be answered. These questions span a variety of policies, both present and future, influence a wide range of stake-holders, and primarily focus on the comparative effectiveness, costs and cost-effectiveness of the atypicals versus the neuroleptics. They can only be answered with careful research on evaluating treatment options in schizophrenia and identifying research priorities in this field. This proposal attempts to address these questions using innovative methods in economics, statistics and decision sciences.

In this work, we propose to develop and apply a comprehensive probabilistic micro-simulation model in schizophrenia to provide information about population level costs, effectiveness and cost-effectiveness of pharmacological treatments and treatment algorithms. The work proposed here is important methodologically and clinically. The methodological advancements that are proposed will have major applications for technology assessment in many domains in health care and hope to provide valuable insights for their potential application in many other contexts.

This award is funded under the American Recovery and Reinvestment Act of 2009, NIH Award number: 3R01MH083706-02S1.