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Precision medicine is a promising framework for generating evidence toimprove health and health care. Yet, a gap persists between the ever-growingnumber of statistical precision medicine strategies for evidence generation andimplementation in real world clinical settings, and the strategies for closingthis gap will likely be context dependent. In this paper, we consider thespecific context of partial compliance to wound management among patients withperipheral artery disease. Through the use of a Gaussian process surrogate forthe value function, we expand beyond the common precision medicine task oflearning an optimal dynamic treatment regime to characterization of classes ofdynamic treatment regimes and how those findings can be translated intoclinical contexts.

Dynamic treatment regime characterization via value function surrogate with an application to partial compliance