As treatment regimens for chronic hepatitis C evolve at a rapid pace, the decision to treat immediately or defer for the promise of future therapy has become increasingly complex. Treatment considerations must balance multiple patient characteristics, preferences, and treatment efficacy data, much of which fraught with uncertainty. Decision analytic modeling can augment traditional clinical studies by elucidating the comparative benefits and harms of treatment timing in different patient populations. The active integration of treatment priorities and challenges as directly elicited from patients and providers during the modeling process can ensure the relevancy of such analyses to key stakeholders as well as the effective dissemination of results. Such data could be highly useful in communicating tailored, patient-oriented estimates of risks and benefits of HCV therapy between health care providers affected patients, thereby improving informed medical decision making. Additionally, a clearer understanding of data uncertainty in the current literature could lead to rational priority setting for future research in hepatitis C care and treatment. I have developed a strong foundation in biostatistics, epidemiology, and decision-analytic methods through the completion of a Masters of Public Health, as well as through broad analytic experience within highly rigorous research environments at the Massachusetts General Hospital, Northwestern University, and now the University of Chicago. Through this K99/R00 proposal I will further develop my expertise in complex disease modeling, and examine, within the context of provider and patient interaction, how medical decision-making can be better informed by stakeholder engagement as well as better understanding of data uncertainty. Through Aim 1 I propose to develop a community partnership through the creation of a Patient and Stakeholder Advisory Board (PSAB) in order to inform the design of a decision-analytic model, identify the factors impacting decision-making surrounding hepatitis C treatment, review and monitor model outputs and optimize the dissemination of results. This will be achieved through systematic process throughout the course of the award and will utilize nominal group techniques, focused surveys and open discussion. I will then use the knowledge gained from this partnership to adapt and further develop a published state transition model of HCV natural history and treatment (Aim 2) that may serve as a platform to evaluate clinical questions requiring sequential decision-making between current and future therapies. I will use this model to examine the comparative effectiveness of immediate versus deferred treatment for different subpopulations of patients afflicted with hepatitis C (Aim 3). Using advanced sensitivity analysis techniques and value of information analyses, I will identify areas of future research in hepatitis C natural history and treatment that would have greatest impact on overall clinical outcomes (Aim 4). The proposed analysis addresses the specific research priorities of the Department of Health and Human Services Action Plan for the Prevention, Care, &Treatment of Viral Hepatitis to advance research in care for the diverse populations living with viral hepatitis, and is fully aligned with the mission of Section 6301(b) of the Patient Protection and Affordable Care Act to strengthen data-driven, patient-centered outcomes research. To ensure the success of this proposed K99/R00 project I have assembled a highly experienced and expert team of mentors and scientific advisors, including my mentor Dr. David Meltzer MD, PhD, and co-mentor Dr. Donald Jensen, MD, who will provide training in advanced analytic methodologies such as complex model building, value of information analysis, and qualitative methods, as well as content expertise in hepatitis C natural history and treatment. The University of Chicago is a world-class research institution, and the Department of Medicine and Section of Hospital Medicine have committed resources and support based on the strength of this proposal, as well as my own potential to contribute meaningfully to the field of comparative effectiveness research in hepatitis C. The K99/R00 award will provide a critical mechanism, through high intensity advanced training, expert mentorship, and analytic platform building to achieve a rapid transition to independence and to establish a career in patient-centered decision-analytic outcomes research.

Public Health Relevance

Improved knowledge of the risks, benefits, and uncertainties surrounding current and future hepatitis C viral (HCV) therapy could improve patient and provider decision-making regarding the optimal timing of treatment initiation. In this proposal, decision-analytic methodologies, as shaped through patient and provider engagement, is used to evaluate the comparative effectiveness of immediate versus deferred therapy for chronic hepatitis C based on individual patient characteristics, preferences, and projected features of future treatment. Results will help identify priorities for future HCV research, and will provide te framework for R01 funded work in optimal hepatitis C care models, sequential decision-making for HCV treatment and liver transplantation, and model-driven decision aids for hepatitis C therapy.

National Institute of Health (NIH)
Agency for Healthcare Research and Quality (AHRQ)
Career Transition Award (K99)
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Special Emphasis Panel (ZHS1)
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Willis, Tamara
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University of Chicago
Internal Medicine/Medicine
Schools of Medicine
United States
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Pho, M T; Jensen, D M; Meltzer, D O et al. (2015) Clinical impact of treatment timing for chronic hepatitis C infection: a decision model. J Viral Hepat 22:630-8
Pho, Mai T; Linas, Benjamin P (2014) Valuing cure: bridging cost-effectiveness and coverage decisions for hepatitis C therapy. Hepatology 60:12-4