We discuss two hot topics in clinical trials. In Part I we discuss the design and analysis of clini-cal trials with multiple outcomes. In Part II, we discuss benefit:risk evaluation in clinical trials by using outcomes to analyze patient rather than patients to analyze outcomes.
PART I: The effects of interventions are multidimensional. Use of more than one outcome offers an attractive design feature in clinical trials as they capture more complete characterization of the benefit and risk of an intervention and provide more informative intervention comparisons. The tutorial will focus on design and analysis of clinical trials with such multiple outcomes. The first part of the tutorial will focus on methods for clinical trial designs evaluating efficacy of two interventions with multiple primary endpoints, especially multiple co-primary endpoints. “Co-primary” means that a trial is designed to evaluate if the test intervention is superior (or noninferior) to the control on all primary endpoints. We describe methods for power and sample size calculations in clinical trials with multiple endpoints including recently developed approaches. We include real clinical trial examples to illustrate the concepts and to help participants apply the methods in practice, and illustrate how to implement the methods using standard statistical software including R and SAS.
PART 2: In the future, clinical trials will have an increased emphasis on pragmatism, providing a practical description of the effects of new treatments in realistic clinical settings. Accomplishing pragmatism requires better summaries of the totality of the evidence that allow for informed benefit:risk decision-making and in a way that clinical trials consumers—patients, physicians, insurers—find transparent. The current approach to the analysis of clinical trials is to analyze efficacy and safety separately and then combine these analyses into a benefit:risk assessment. Many assume that this will effectively describe the impact on patients. But this approach is suboptimal for evaluating the totality of effects on patients. In part II of the tutorial, we will describe a broad vision for the future of clinical trials consistent with increased pragmatism. Greater focus on using outcomes to analyze patients rather than patients to analyze outcomes particularly in late-phase/stage clinical trials is an important part of this vision. We discuss the desirability of outcome ranking (DOOR) and the partial credit strategy for design and analysis of clinical trials based on benefit:risk assessment. These strategies involve utilizing composite benefit:risk endpoints with a goal of understanding how to analyze one patient before trying to figure out how to analyze many. With a desire to measure and weigh outcomes that are most important from the patient’s perspective, we discuss using patients as a resource to inform analyses.
Toshimitsu Hamasaki is the Director of Data Science at National Cerebral and Cardiovascular Center (NCVC), Osaka, Japan. He has been involved in biopharmaceutical statistics for over 20 years, and prior to joining NCVC, worked at Shiogoni, Pfizer Japan and Osaka University. He has been actively involved in biostatistical research, and is the author of more than 150 peer-reviewed publications and three textbooks on clinical trials including Group-Sequential Clinical Trials with Multiple Co-Objectives. Dr. Hamasaki was the member of ICH E5 Guideline Implementation Working Group as a representative of Japan Pharmaceutical Manufacturers Association to develop the Q & A document on the guideline. He currently serves as an Associ-ate Editor for Statistics in Biopharmaceutical Research and Journal of Biopharmaceutical Sta-tistics, and Editor for CHANCE. He is an elected member of International Statistical Institute and a Fellow of the American Statistical Association (ASA). He is a recipient of the Japanese Society of Computational Statistics Distinguished Article Award and Behaviormetric Society of Japan Hida-Mizuno Prize, and the Poster Competition Winner at the ASA Biopharmaceutical Section Regulatory-Industry Statistics Workshop.
Scott R Evans is the Director of the Statistical and Data Management Center (SDMC) for the Antibacterial Resistance Leadership Group (ARLG). His interests include the design, monitor-ing, analyses, and reporting of and education in clinical trials. He is the author of more than 100 peer-reviewed publications and three textbooks on clinical trials including Fundamentals for New Clinical Trialists. Dr. Evans is a member of an FDA Advisory Committee, the Board of Directors for the American Statistical Association, the Society for Clinical Trials and Mu Sigma Rho (the National Honorary Society for Statistics), and the Steering Committee of the Clinical Trials Transformation Initiative (CTTI). He is the Editor-in-Chief of CHANCE and Statistical Communications in Infectious Diseases (SCID). Dr. Evans is a Visiting Professor at the Department of Innovative Clinical Trials and Data Science at Osaka University in Japan. He is the Past-President of the Boston Chapter of the American Statistical Association (ASA), the Past-Chair of the Development Committee of ASA, the Past-Chair of the Teaching Statistics in the Health Sciences section of ASA, and the Past-Chair of the Statistics in Sports section of ASA. Dr. Evans is a recipient of the Mosteller Statistician of the Year Award, the Robert Zackin Distinguished Collaborative Statistician Award, and is a Fellow of the ASA.