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Abstract:
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An optimal mHealth strategy for type I diabetes (T1D) maximizes longterm patient health by tailoring recommendations for diet, exercise, and insulin uptake to the unique biology and evolving health status of each patient. We develop a response-adaptive randomization method that learns an optimal intervention strategy while controlling the risk of adverse events. The method, which uses a variant of Thompson Sampling (TS) to facilitate learning, maximizes efficiency while providing strict controls on the probability of an adverse event and, in this way, aligns with the Neyman-Pearson framework in testing and classification. Thus, we term the method Neyman-Pearson Thompson Sampling (NP-TS). We illustrate the application of NP-TS using data from a pilot mHealth study on T1D.
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