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Clinical Trials – A Bayesian Approach Sreedevi Menon Cognub Decisions Solutions (formerly known as Kreara Solutions) Introduction Bayesian approach in the design and analysis of clinical trials is gaining wide application in the industry Bayesian statistics uses a mathematical approach to effectively utilize prior and current information Where does it find its application? Trial Design Dose Allocation Trial Monitoring Analysis of Clinical Data MetaAnalysis Bayes’ Theorem The roots of Bayesian Statistics lies in Bayes’ theorem Bayes’ Theorem is a rule about probabilities which is used in any analysis describing random variables Thus for two events A and B... P[ A|B ] = P[ A and B ]/ P[ B ] = P[ B|A ] (P[ A ]/ P[ B ]) Bayesian Approach Starts with a prior belief and then uses new evidence to attain a posterior belief Provides a mathematical method for calculating the likelihood of a future event based on prior knowledge Uses the ‘language’ of probability to describe what is known about parameters Components of Bayesian Approach Prior distribution Likelihood principle Posterior probabilities Predictive probability Exchangeability of trials Decision rules Bayesian Strategies Frequent interim analyses Longitudinal modelling Response adaptive randomization Simulation of trial performance Dose response modelling Applications of Bayesian Approach Adaptive Trial Design Key trial parameters not kept constant Utilizes accumulated data Dose Allocation Trial Monitoring Analysis Determines minimum effective and maximum tolerated doses Skeptical prior distributions Dose Finding Studies Continual reassessment method Overcomes limitations of group sequential methods Proof of concept studies Optimizes design Probability of toxicity assigned to each dose based on historical information Analysis of phase II–III trials Reduces risk of negative results Dose relationship model defined Post marketing surveillance Meta-Analysis Advantages of Bayesian Approach Provides formal mechanism for using prior information Places emphasis on estimation and graphical presentation rather than hypothesis testing Avoids the confusion over use of 1–tailed/2–tailed test Allows use accumulating information from current well as other trials Limitations of Bayesian Approach Posterior probabilities may be hard to compute Requires good statistical knowledge to choose the prior distribution Choice of data inclusion from other trials should be done carefully Adherence to regulatory requirements Ethical considerations Case Studies Sample Size Calculation Safety Analysis References o Use of Bayesian statistics in drug development: Advantages and challenges Sandeep K Gupta, Department of Medical Affairs and Clinical Research, Ranbaxy Laboratories Ltd, India o Bayesian Statistics (a very brief introduction) Ken Rice Epi 515/Biostat 519 April, 2014 o BIO249 Bayesian Methodology in Biostatistics o An Introduction to Bayesian Methods with Clinical Applications Frank E Harrell Jr and Mario Peruggia, School of Medicine, University of Virginia o Adaptive design clinical trials: Methodology, challenges and prospect, Rajiv Mahajan and Kapil Gupta o An Overview of Bayesian Adaptive Clinical Trial Design, Roger J. Lewis, MD, PhD, Department of Emergency Medicine, Berry Consultants, LLC THANK YOU