Statistical considerations in the analysis of multivariate Phase II testing.

Abstract

In medical diagnosis and treatment, many diseases are characterized by multiple measurable differences in clinical (e.g., physical or radiological differences) and laboratory parameters (biomarkers from "healthy levels". Each of these differences is a symptom of the disease often correlated with the other symptoms. In Phase II human trials, the level of a single symptom is often used as a surrogate for disease level. In multi-symptom diseases, all relevant symptoms often should be included in the evaluation, thus a multivariate approach to Phase II analysis becomes critical. In this dissertation we formulate seven (7) multivariate tests for use in multivariate Phase II studies. Each method is evaluated using metabolic syndrome with data obtained from the public domain data set, NHANES III as an example to train the algorithms and the associated tests.