Statistical Approaches to Improving Trial Efficiency and Conduct

Janice Pogue (), P. J. Devereaux () and Salim Yusuf ()
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Janice Pogue: McMaster University, Department of Clinical Epidemiology and Biostatistics and Faculty of Health Sciences
P. J. Devereaux: McMaster University, Department of Clinical Epidemiology and Biostatistics and Faculty of Health Sciences
Salim Yusuf: McMaster University, Department of Clinical Epidemiology and Biostatistics and Faculty of Health Sciences

Chapter Chapter 4 in Developments in Statistical Evaluation of Clinical Trials, 2014, pp 71-84 from Springer

Abstract: Abstract Given the trend towards increasing complexity and cost of clinical trials today, future trials may end up being small, complex, and under-powered to detect clinically meaningful treatment effects. In order to continue to perform important research in the future, we need to make clinical trial designs more efficient. Through the retrospective statistical analysis of variation in the design of past trials and the prospective comparisons of clinical trials methods, we can determine which trial procedures truly influence the bias and precision of treatment estimates and where complexity and costs can be reduced. We provide two examples of the retrospective study of clinical trials methods that could change the conduct of future trials. First, an overview of the effect of outcome adjudication on treatment estimates for cardiovascular trials is presented. Second, a prognostic model to detect fraud within multi-centre trials is developed as part of a system of central statistical monitoring. There are many more unanswered questions about efficiencies in clinical trials methodology that need to be examined by statisticians and researchers.

Keywords: Trial Methodology; Clinical Trial Methodology; European Stroke Prevention Study; Data Fabrication; Central Statistical Monitoring (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-55345-5_4

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DOI: 10.1007/978-3-642-55345-5_4

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