Patent ID: 7085690

Claim:
A computer implemented method for selecting a near optimal or optimal mathematical model from a set of candidate models, comprising: a) defining a candidate model search space having n dimensions, wherein n is a positive integer and each dimension represents a set of mutually exclusive features from which exactly one of said mutually exclusive features is chosen from each set of mutually exclusive features for each candidate model; b) selecting an initial set of candidate models by selecting one feature from each set of mutually excusive features by a uniform random process for each candidate model; c) computing a goodness of fit (fitness) of each model in terms of the log likelihood using a pharmacokinetic and/or pharmacodynamic model; d) calculating for each model an overall objective function given by the expression: fitness+theta penalty·ntheta+random effect penalty·nrand+success·success penalty+covariance·covariance penalty+correlation·correlation penalty, wherein fitness is −2*log likelihood of the observed data given a pharmacokinetic and/or pharmacodynamic model, theta penalty is the penalty for each fitted parameter, ntheta is the number of parameters, random effect penalty is the penalty for each random effect, nrand is the number of random effects, success is 0 if the minimization was successful and 1 if not, success penalty is the penalty if the minimization is not successful, covariance is 0 if the covariance step was successful and 1 if not, covariance penalty is the penalty if the covariance step is not successful, correlation is 0 if no estimation correlations are >0.95 and 1 if at least one is >0.95 and correlation penalty is the penalty for a correlation >0.95; e) searching said models using the objective function and a method selected from the group consisting of full grid search, simulated annealing, integer programming, scatter search/path relinking, neural networks, tabu search and genetic algorithm to select the next set of models; f) repeating steps c) to e) with the selected method of searching and next set of models until no further improvement in the lowest value of overall objective functions of models is achieved; g) selecting the model with lowest value of the objective function as the optimal or near optimal model.