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

Graphical approaches for the control of generalized error rates

When simultaneously testing multiple hypotheses, the usual approach in the context of confirmatory clinical trials is to control the familywise error rate (FWER), which bounds the probability of making at least one false rejection. In many trial settings, these hypotheses will additionally have a hierarchical structure that reflects the relative importance and links between different clinical objectives. The graphical approach of Bretz et al (2009) is a flexible and easily communicable way of controlling the FWER while respecting complex trial objectives and multiple structured hypotheses. However, the FWER can be a very stringent criterion that leads to procedures with low power, and may not be appropriate in exploratory trial settings. This motivates controlling generalized error rates, particularly when the number of hypotheses tested is no longer small. We consider the generalized familywise error rate (k-FWER), which is the probability of making k or more false rejections, as well as the tail probability of the false discovery proportion (FDP), which is the probability that the proportion of false rejections is greater than some threshold. We also consider asymptotic control of the false discovery rate, which is the expectation of the FDP. In this article, we show how to control these generalized error rates when using the graphical approach and its extensions. We demonstrate the utility of the resulting graphical procedures on three clinical trial case studies.

Early Decision-Making in Drug Development: The Potential Role of Pharmaco-EEG and Pharmaco-Sleep

The pharmaceutical industry has been suffering from low clinical success rates of new drugs for some time with particularly high attrition in early clinical development, especially for drugs aimed at central targets. Both pharmaco-electroencephalography (EEG) and pharmaco-sleep, along with other biomarker techniques, have significant potential to assist with this problem by enabling early decisions to be made about the likelihood of a compound proving successful in the clinic. This paper discusses the role and points of application of biomarker techniques in early drug development. It proposes a framework for the use of pharmaco-EEG and pharmaco-sleep in drug development that (i) relies on the combination of preclinical data and an understanding of translatability to generate robust hypotheses for testing in early clinical studies and (ii) is backed up by a clear decision-making process. The areas that need further development before this framework can be put fully into practice are discussed, along with some possible routes by which this could be achieved through precompetitive co-operation within the industry.