Statistical Approaches for Modelling Cancer Bioassays

This paper discusses the possible ways to analyse the data, adopting a matrix notation, so often used in Bioassays. The paper also reviews the Multistage Models (MM). The MM class of models is applied for extrapolation, to the region of Low-Dose. The effect of covariates in experimental carcinogenes...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Kitsos, Christos P, Tavoularis, Nikolaos K, Toulias, Thomas L, Lolas, George
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 24.06.2013
Subjects
Bioassays
Carcinogens
Economic models
Online AccessGet full text

Cover

More Information
Summary:This paper discusses the possible ways to analyse the data, adopting a matrix notation, so often used in Bioassays. The paper also reviews the Multistage Models (MM). The MM class of models is applied for extrapolation, to the region of Low-Dose. The effect of covariates in experimental carcinogenesis is introduced and the relative efficiency is evaluated. Certainly the discussed case was refereed to uncorrelated covariates and therefore an open problem might be the multicollinear predictive covariates.Various nonlinear models are discussed, giving more emphasis on the Michaelis-Menten and the Fisher's information for them is discussed
ISSN:2331-8422