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Abstract:
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A dynamic treatment regime consists of a sequence of decision rules, one per stage of treatment, that make treatment decision based on the accrued information on the patient to that point. The regime is of critical importance for precision medicine as it provides sequential, evidence-driven personalized decision making. A wide variety of statistical methods have been developed for estimating optimal dynamic treatment regimens from randomized trials and observation studies, including backwards outcome weighted learning, Q-learning, value search, and classification. We will review the underlying assumption, relative merits and demerits, performance of these methods and discuss practical issues associated with these methods.
The following will be included in the presentation.
This presentation was sponsored by AbbVie. AbbVie contributed to the design, research, and interpretation of data, writing, reviewing, and approving the publication.
Yingyi Liu, Hongwei Wang, and Weili He are employees of AbbVie Inc. and may own AbbVie stock.
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