Deerfield
About Deerfield

Launched in 1994, Deerfield Management Company is an investment firm dedicated to advancing healthcare through information, investment, and philanthropy—all toward the end goal of cures for disease, improved quality of life, and reduced cost of care.

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Investment

Supporting companies across the healthcare ecosystem with flexible funding models…

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Information

Delivering market research to the Deerfield team, its portfolio companies and other partners.

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Philanthropy

A New York City-based not-for-profit devoted to advancing innovative health care initiatives.

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Portfolio Companies

Deerfield generally maintains a combined portfolio of more than 150 private and public investments across the life science, medical device, diagnostic, digital health and health service industries at all stages of evolution from start-up to mature company.

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Research Collaborations

Deerfield partners with leading academic research centers, providing critical funding and expertise to further sustain and accelerate the commercialization of discoveries toward meaningful societal impact by advancing cures for disease.

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Strategic Partners

As a strategic partner, Deerfield offers capital, scientific expertise, business operating support, and unique access to innovation.

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Deerfield Foundation

The Deerfield Foundation is a New York City-based not-for-profit organization whose mission is to improve health, accelerate innovation and promote human equity.

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Cure Campus

Cure is a 12-story innovations campus in New York City that intends to bring together innovators from academia, government, industry, and the not-for-profit sectors to advance human health and accelerate the fight against disease.

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Cure Programming

Cure has a series of expert lectures intended to advance thought in healthcare, management, innovation, policy, and other relevant subjects. This fosters growth and education for those at Cure and its guests.

Events at the Cure

A hybrid approach for prediction of event times in double-blind clinical trials

In clinical trials especially those events-driven trials, it is often of great interest to predict the timing of pre-specified landmark event accurately in order to prepare for the interim or final analyses. Both parametric and non-parametric approaches have been proposed in the literature to estimate the underlying survival functions which is the key to the prediction of future event times. However, the existing approaches are neither not applicable to double-blind clinical trials or assuming smooth survival functions which might not hold in real clinical settings. In this talk, a hybrid parametric and non-parametric approach is proposed to predict event times in double-blind clinical trials with time-to-event outcomes. A greedy algorithm is first developed to detect change points in survival functions. The survival function before the last change point is estimated non-parametrically and the tail distribution beyond the last change point is estimated parametrically. Numerical results show that the proposed approach provides accurate predictions for future event times.