Abstract
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 rats is the response variable. We also discuss the biological conclusions that can be drawn from applying various statistical methods to this data set.
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Facer, M.R., Müller, HG., Clifford, A.J. (1998). Statistical Models for Quantitative Bioassay. In: Clifford, A.J., Müller, HG. (eds) Mathematical Modeling in Experimental Nutrition. Advances in Experimental Medicine and Biology, vol 445. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-1959-5_10
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DOI: https://doi.org/10.1007/978-1-4899-1959-5_10
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