title: Simulation-based estimation of stochastic process parameters in tumor growth.
abstract: Models are generally developed at the micro level. Data are generally gathered at the macro level. Obtaining the macromodel which is the natural consequence of the underlying micro model is generally not feasible. SIMEST gives a means whereby the micromodel is used to generate, for a given assumed set of parameters, simulated sets of macro data. These data are compared with the actual clinical macro data. The parameters are then adjusted to obtain concordance with the clinical data. In this manner, simulation gives us a means of parameter estimation without the necessity of generating the macro model.
authors.affiliation: Department of Statistics, Rice University, 6100 South Main Street, Houston, TX 77001-1892, USA. thomp@rice.edu
authors.forename: James R
authors.initials: JR
authors.lastname: Thompson
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D008954:Models, Biological; D009362:Neoplasm Metastasis; D009369:Neoplasms / Q000473:pathology*; D016012:Poisson Distribution; D013269:Stochastic Processes