Stochastic analysis of therapeutic modalities using a database of patient responses

Proposes a new method for stochastic analysis and control which does not require a model, but which is constructed directly from a raw database of patient responses to therapy. Roughly speaking, the basic idea is to evaluate a control (a therapeutic policy or modality) which has, on the average, proved to work well for similar patients in the database. By "similar" is meant patients who have the same covariates and who are in similar dynamical states. The proposed stochastic analysis and control approach for databases is new, although it is motivated by methods of machine learning put forth by D.P. Bertsekas et al. (1996) and R.S. Sutton et al. (1998) and methods of dynamic programming for stochastic control given by D.S. Bayard (1991, 1992).