Andrew W. Lo - 2019 Life Science Conference
December 10, 2019

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Estimating and Predicting Clinical Trial Success Rates: A Data Science Approach
All investors require some understanding of the potential risk and reward of a given venture before they’re willing to invest, and the less they know about it, the less capital will be available. This is especially true with biomedical assets in which the risks and rewards are equally outsized and hard to evaluate. Therefore, a prerequisite for addressing the so-called “Valley of Death” in early-stage biomedical R&D funding is better risk analytics. In this talk, Prof. Lo will describe his latest research on estimating historical probabilities of success for clinical trials and then applying machine-learning techniques to predict future success rates across a variety of drug/indication pairs. With more accurate assessments of these success probabilities, capital can be deployed more efficiently and at lower cost, thereby bringing greater amounts of capital into the biopharma industry at a time when capital is needed most.

Please login to view this video.
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Video details
Estimating and Predicting Clinical Trial Success Rates: A Data Science Approach
All investors require some understanding of the potential risk and reward of a given venture before they’re willing to invest, and the less they know about it, the less capital will be available. This is especially true with biomedical assets in which the risks and rewards are equally outsized and hard to evaluate. Therefore, a prerequisite for addressing the so-called “Valley of Death” in early-stage biomedical R&D funding is better risk analytics. In this talk, Prof. Lo will describe his latest research on estimating historical probabilities of success for clinical trials and then applying machine-learning techniques to predict future success rates across a variety of drug/indication pairs. With more accurate assessments of these success probabilities, capital can be deployed more efficiently and at lower cost, thereby bringing greater amounts of capital into the biopharma industry at a time when capital is needed most.