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Estimating optimal decisions using stochastic dynamic models

Deyadeen Ali Alshibani

Libyan Academy

Department of Mathematics and Statistics

ABSTRACT

Stochastic Dynamic Models are functions of decision and covariate history which are used to advice on decisions to be taken. Murphy (2003) and Robins (2004) have proposed models and developed semi-parametric methods for making inferences about the optimal dynamic treatment regime in a multi-interval study that provide clear advantages over traditional parametric approaches.

In this paper the author investigates the estimation of optimal dynamic treatment decisions based on a full parametric approaches: Inverse Probability Treatment Weighted and Regret-Regression of Henderson et al. (2010). A numerical example on determination of optimal decisions is presented in detail.

Key words: Optimal decisions, stochastic dynamic models.