Pharmacological data, statistical analysis

De Lean, A., Hancock, A. A. and Lefkowitz, R. J. (1982). Validation and statistical analysis of a computer modeling method for quantitative analysis of radioligand binding data for mixtures of pharmacological receptor subtypes, Mol. Pharmacol., 21, 5-16. 

Anderson, H., Spliid, H., Larsen, S. and Dali, V (2000). Statistical analysis of time to event data from preclinical safety pharmacology studies. Tox. Methods 10 111-125. 

The most commonly employed univariate statistical methods are analysis of variance (ANOVA) and Student s r-test [8]. These methods are parametric, that is, they require that the populations studied be approximately normally distributed. Some non-parametric methods are also popular, as, f r example, Kruskal-Wallis ANOVA and Mann-Whitney s U-test [9]. A key feature of univariate statistical methods is that data are analysed one variable at a rime (OVAT). This means that any information contained in the relation between the variables is not included in the OVAT analysis. Univariate methods are the most commonly used methods, irrespective of the nature of the data. Thus, in a recent issue of the European Journal of Pharmacology (Vol. 137), 20 out of 23 research reports used multivariate measurement. However, all of them were analysed by univariate methods. 

The quality of data was such that even differences in activity between identical compounds supplied by different vendors sufficient to cause them to be misclassified using less rigorous assays could be noted. For example, resveratrol would be identified as active at 2.3 i.M in one sample but inactive in the other, when a 50% threshold is used. The authors suggest that qHTS shifts the analysis of screening data from a statistical to a pharmacological... 

---