Toxicological Sciences, Vol 51, 245-258, Copyright © 1999 by Society of Toxicology
D Keil, RW Luebke, M Ensley, PD Gerard and SB Pruett
In immunotoxicology, the critical functions of the immune system (host
resistance to infection and neoplasia) cannot be measured directly in
humans. It is theoretically possible to predict changes in host resistance
based on changes in immunological functions known to mediate host
resistance. However, quantitative predictive models of this type have not
yet been achieved in humans or in animal models. Multivariate statistical
methods were developed for analysis and modeling of the effects of several
explanatory variables on a dependent variable, and they seem well suited
for attempts to predict host resistance changes caused by changes in
immunological parameters. However, these methods were developed with the
assumption that all variables can be measured for each experimental
subject. For a number of reasons, this generally cannot be done in
comprehensive immunotoxicology evaluations. In the present study, the
suitability of multivariate methods for analysis of variables measured in
different experiments was examined, using a limited data set consisting of
immunological parameters that could all be measured for each mouse.
Analysis was done on the original data set and test data sets produced by
randomizing data within dosage groups. This was done to simulate the random
pairing of data that would occur if measurements were obtained from
different sets of mice in different experiments. Statistical theory
indicates that randomization will disrupt the correlation matrices that are
central in multivariate analyses. However, the present results demonstrate
empirically that for at least one immunotoxicant (dexamethasone),
remarkably similar multivariate models were obtained for the original and
109 randomized data sets. In contrast, the randomized data sets produced
substantially different multivariate models when data obtained with a
different immunotoxicant (cyclosporin A) were analyzed. The major
difference between the two data sets was that dexamethasone strongly and
dose- responsively suppressed many more parameters than did cyclosporin A.
Additional work is needed to determine whether there are consistent
criteria that could be used to identify immunotoxicology data sets, which
would be amenable to multivariate analysis.
ARTICLES
Evaluation of multivariate statistical methods for analysis and modeling of immunotoxicology data
Department of Biological Sciences, Mississippi State University 39762, USA.
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