||Methods for Comparing Salmonella Mutagenicity Data Sets Using Nonlinear Models.
Alvord, W. G. ;
Driver, J. H. ;
Claxton, L. ;
Creason, J. P. ;
||National Cancer Inst., Frederick, MD. Frederick Cancer Research Facility. ;Data Management Services, Inc., Frederick, MD. ;Versar, Inc., Springfield, VA. RiskFocus Div.;Department of Health and Human Services, Washington, DC.;Health Effects Research Lab., Research Triangle Park, NC.
Salmonella typhimurium ;
Mathematical models ;
||Most EPA libraries have a fiche copy filed under the call number shown. Check with individual libraries about paper copy.
A variety of linear and nonlinear mathematical models have been proposed to characterize Ames test data sets, but no systematic procedure has been proposed to compare two or more data sets across conditions, laboratories, occasions, mutagens or treatments. In the paper, general method for data set comparison is provided. Nonlinear regression techniques are applied to real data sets as hypothetical and real hypotheses are considered. Data-set and parameter invariance are described in depth. Confidence-band construction for nonlinear models and other graphical techniques are presented as auxillary tools. (Copyright (c) 1990 Elsevier Science Publishers BV (Biomedical Division.)