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Biostat Workshop- Statistical Power Boosts for Randomized Clinical Trials

Dec 05, 2013 (Thu) | 1:15 PM -3:00 PM
MSOB x303 : Stanford, CA

Increasing cost pressures in the pharmaceutical industry necessitate the development of novel approaches for making clinical development cheaper and faster while maintaining strong scientific rigor. From a statistical perspective, tangible progress can be made, for example, by developing and deploying innovative analyses of randomized clinical trials that are substantially more powerful than their traditional counterparts. Any increase in statistical efficiency readily translates to a reduction in the sample size (and hence time) required to address the objectives of the given clinical trial. In this talk, I will illustrate examples of resource-saving statistical innovation across all phases of clinical drug development, with a focus on more efficient use of baseline data in crossover trials, earlier detection of pharmacogenomics signals, and enhanced analyses of stratified time-to-event trials. Suggested readings: D.V. Mehrotra, SC Su and X Li. An efficient alternative to the stratified Cox model analysis. Stat Med, 2012 Jul 30; 31 (17), p.1849-56. DM Bloomfield, JT Kost, K Ghosh, D Hreniuk, LA Hickey, MJ Guitierrez, K Gottesdiener and JA Wagner. The effect of moxifloxacin on QTc and implications for the design of thorough QT studies. Clin Pharmacol Ther, 2008, 84 (4): 475-80. A Dmitrienko, C Beasley and M Mitchell. Design and analysis of thorough QT studies. Biopharmaceutical Network. Report 2008-04-29. [http://biopharmnet.com/cardiac.html]

Department:  Health Research & Policy - Epidemiology

Contact: Ton Ali | (650) 723-5456 | toniali@stanford.edu

Presenter(s):

  • Devan V. Mehrotra, PhD Executive Director (Biostatistics) and Presidential Fellow