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Abstract:
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In a treatment regime for diseases such as cancer, physicians make multiple decisions over the course of a treatment depending on the patient's accrued biomedical information. Essentially, the treatment rule at each decision point is a function which takes the patients' clinical, biomarker information, treatment and outcome history available up to that point as an input and returns the treatment choice as an output. The optimal treatment rule aims to provide the 'best' outcome for the patient based on this whole process. We propose a Bayesian approach to optimal treatment allocation given a patient's information. We compare the performance of the proposed approach with the other comparator methods in the literature in a simulation study where the treatment selection depends on the patients' baseline information and also in a real data.
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