Structured multiplicity and confirmatory statistical analyses in pharmacodynamic studies using the quantitative electroencephalogram

Abstract

Pharmacodynamic (PD) clinical studies are characterised by a high degree of multiplicity. This multiplicity is the result of the design of these studies that typically investigate effects of a number of biomarkers at various doses and multiple time points. Measurements are taken at many or all points of a “hyper-grid” that can be understood as the cross-product of a number of dimensions each of which has typically 3–30 discrete values. This exploratory design helps understanding the phenomena under investigation, but has made a confirmatory statistical analysis of these studies difficult, so that such an analysis is often missing in this type of studies. In this contribution we show that the cross-product structure of PD studies allows to combine several well-known techniques to address multiplicity in an effective way, so that a confirmatory analysis of these studies becomes feasible without unrealistic loss of power. We demonstrate the application of this technique in two studies that use the quantitative EEG (qEEG) as biomarker for drug activity at the GABA-A receptor. QEEG studies suffer particularly from the curse of multiplicity, since, in addition to the common dimensions like dose and time, the qEEG is measured at many locations over the scalp and in a number of frequency bands which inflate the multiplicity by a factor of about 250.