Dynamic Learning of Patient Response Types: An Application to Treating Chronic Diseases

Many chronic-disease medications work only for a subgroup of patients, and no biomarker identifies that subgroup in advance. We develop an adaptive treatment framework that learns from both continuous measures of disease progression and the timing and severity of infrequent events such as relapses, helping physicians decide when to persist and when to stop. Applied to interferon treatment for multiple sclerosis, the resulting policies provide a cost-effectiveness frontier and benchmarks for existing treatment guidelines.

Management Science, vol. 64, no. 8, pp. 3469–3488