Clinical Trial Design and other Statistical Issues Mary A. Foulkes, Ph.D. Office of Biostatistics and Epidemiology Vaccines and Related Biological Products Advisory Committee February 17, 2005

“Chance favors the prepared mind” “Chance favors the prepared mind” Louis Pasteur

Novel Statistical Applications Usual approach: Develop theory/model/method Seek application Critical Path approach: No prior stat approach or existing approximation Develop new or improved theory/model/method

Quantitative Methods Maximizing efficiency while maintaining reliability –Improved analytic approaches –Flexible study designs Transparency –Best Practices –Underlying assumptions

CBER Products Vaccines –Huge target populations –Administered to healthy people –Major public health impact –Growing public safety concerns Blood, blood products and tissues Cellular and gene therapies

Study Endpoints Biomarkers Intermediate endpoints Composite endpoints Multiple endpoints Assessment scales

Genomics/Proteomics Rapidly emerging area of research and application Statistical practices not well established Multiplicity issues Missing data issues Experimental design opportunities

Statistics in Manufacturing Quality control in blood collection –Low volume processing –False positives highly burdensome –Efficiency potential in alternative statistical approaches Vaccine lot consistency –Normality assumption does not hold

Flexible Study Design Initial parameters may be inaccurate Interim modifications may improve trial ability to answer question Traditional approaches –Group sequential designs with early stopping for efficacy, harm, futility –Other changes if not influenced by knowledge of interim data Emerging approaches

Flexible Designs for Biologics CBER regulates cutting-edge products May be less information on design parameters, more need for flexibility Safety concerns Optimal design approaches?

Trial Design & Analyses Non-inferiority trials –ICH E10 gives gen’l principles, not specifics –Optimal approach for defining acceptable difference not yet defined Missing data –No preferred analytic approach –Implications of assumptions

Data Mining New tool to improve sensitivity to post-marketing surveillance False positive signals could have non-trivial consequences –Multiplicity –Threshold for “signal” FDA-PhRMA wkgp to develop “best practices”

Risk Analysis Decisions often needed in the absence of full information Modeling risks can indicate influential parameters, informing risk minimization Models can indicate where to direct resources for add’l information

Summary Quantitative methods are central to many critical path issues Quantitative methods will be needed to evaluate potential gain from new approaches Statisticians and Epidemiologists involved in many issues identified by stakeholders