Statistical models for quantitative bioassay.
We discuss various statistical approaches useful in the analysis of nutritional dose-response data with a continuous response. The emphasis is on the multivariate case with several predictors. The methods which will be discussed can be classified into parametric models, including change-point models, and nonparametric models, which rely on smoothing methods such as weighted local linear fitting. The methods will be illustrated with the analysis of data generated from a folate depletion-repletion bioassay experiment conducted on rats, where the measured growth rate of the rate is the response variable. We also discuss the biological conclusions that can be drawn from applying various statistical methods to this data set.
Division of Statistics, University of California, Davis 95616, USA.
M R
MR
Facer
H G
HG
Müller
A J
AJ
Clifford
D000824:Animal Nutritional Physiological Phenomena; D000818:Animals; D001681:Biological Assay / Q000706:statistics & numerical data*; D004305:Dose-Response Relationship, Drug; D005492:Folic Acid / Q000008:administration & dosage / Q000378:metabolism*; D016018:Least-Squares Analysis; D016014:Linear Models; D008297:Male; D015233:Models, Statistical; D015999:Multivariate Analysis; D011897:Random Allocation; D051381:Rats; D017207:Rats, Sprague-Dawley / Q000254:growth & development / Q000378:metabolism
D005492:Folic Acid