The goal of this core project is the development of modeling methodologies for the experimental study of sparse data systems. Alan Forrest and Jerry Schentag, S.U.N.Y. Buffalo.
Some recent work is outlined below.
Similar evaluation of the uncertainty intervals calculated for plasma concentrations further document the ability of the proposed stochastic control method to predict the uncertainty associated with future therapy. The current interest in designing effect-controlled clinical trials, that aim to achieve a set of drug response values in a population of experimental subjects, provides an important application of this approach. In a simulation study of the control algorithm, the probability that FEV 1 was within the target interval increased from .36 to .80 to .95, form the first to the third dose interval. Sparse Data Systems One of the important applications of modeling in the biomedical sciences in general, and pharmacokinetics in particular, involves its use in estimating those properties of a system that are not amenable to direct measurements. Consequently, potentially important sources of model error are ignored when using a regression model-estimation framework.