DIA BSWG © 2015 DIA, Inc. All rights reserved.

Outline Brief Overview of DIA BSWG Bayesian Survey Results Overview Areas of Focus © 2015 DIA, Inc. All rights reserved.

Value of Bayesian Approach Emulates how we naturally think Addresses questions directly Naturally aligned with adaptive designs Creates environment to evaluate how others are using prior knowledge Yields more appropriate sample sizes (can result in saving time and money) Provides more integrated clinical plan Increases understanding of uncertainty to enable more informed decisions throughout medical product development Ability to incorporate prior information from different sources Natural for evidence synthesis or meta-analysis Handling multiplicity through borrowing strength and hierarchical modeling Appealing in dealing with rare events as the model modulates the extremes Ability to handle complex problems via unified modeling, taking all the uncertainty into appropriate account Allowing direct probability inferences on different scales Rev. Thomas Bayes 3

Some Key Hurdles Increased acceptance could transform the future of drug development, but there are some key barriers that currently result in limited uptake, including: Perceived lack of acceptance from regulatory, including lack of acceptance of simulations to demonstrate control of type 1 error and lack of acceptance for formal use of prior knowledge; Lack of acceptance for use of Bayesian methods in confirmatory study as basis of approval; No clear path for timely communication with regulators regarding technical aspects associated with adaptive design and/or Bayesian approaches; Limited expertise regarding adaptive design and Bayesian methods (computation, methods, knowledge of designs, etc.); and Internal resistance to change.

DIA BSWG: Who are we? Group of representatives from Regulatory, Academia, and Industry, engaging in scientific discussion/collaboration facilitate appropriate use of the Bayesian approach contribute to progress of Bayesian methodology throughout medical product development

Opportunity Statement Bayesian methods provide framework to leverage prior information and data from diverse sources. Bringing together academic, industrial, and regulatory representatives is essential to overcome hurdles. Improved patient outcomes Provides opportunity to influence proactively by engaging in scientific discussion

Subteams (chairs) Safety (Karen Price/Amy Xia) Use of historical data/prior specification (John Zhong/Satrajit Roychoudhury) Non-inferiority (Mani Lakshminarayanan) Reporting/Tools (Mani Lakshminarayanan) Joint Modeling (Larry Gould) Program-wide Decision Making (Bin Yao/Karen Price) Missing Data (Frank Liu/Stacy Lindborg) Education (Fanni Natanegara/Mat Davis) Joint with Pediatric community and ADSWG (Jasmina Savic)

DIA BSWG Impact Published numerous papers, including Special Issue Pharmaceutical Statistics Presented at several conferences, held webinars, taught short courses, etc. Facilitated many productive conversations across academia/industry/regulatory Joint DIA Bayes/AD conference (Feb 2015)

Bayesian Survey and Recommendations © 2015 DIA, Inc. All rights reserved.

Bayesian Survey In 2012, we conducted an industry-wide Bayesian survey first industry-wide survey to collect information on the use of Bayesian methods amongst statisticians working in medical product development results and recommendations were submitted as a paper to be included in the Special Issue of Pharmaceutical Statistics

Implementation Hurdles Insufficient knowledge of the Bayesian approach, particularly on the practical level Lack of clarity of the regulatory position and/or lack of guidance and experience Lack of tools including case examples and user-friendly software Company-internal difficulties (lack of time, lack of support/guidance, general reluctance from team members to accept the Bayesian approach) include difficulty in explaining Bayesian concepts and lack of understanding where it may be appropriate to consider Xx Within an organization lack of precedents to motivate Support from management and team

HOW TO IMPROVE THE PROGRESS ON BAYESIAN APPLICATION IN MEDICAL PRODUCT DEVELOPMENT?

Recommendation #1: Bayesian education Familiarity Understanding Appreciation Internal and external forums Internal live training is preferred Central repository of literature (books/articles) organized by topics to be made available Learning process is continuous! Through education, all stakeholders can be made familiar and have basic understanding of advantages and limitations of Bayesian methods

Recommendation #2: Centralized location for case examples Searchable by categories e.g. statistical model, drug phase, therapeutic area Include examples from internal decision making to regulatory submission Be made available publicly or internally

Recommendation #3: Internal Bayesian infrastructure A group of statisticians with dedicated time for focusing on Bayesian methods allow systematic training of statisticians and non-statisticians recruitment of statisticians with thorough Bayesian training collaboration with other functions

Recommendation #4: Interaction between Bayesian and frequentist statisticians and other stakeholders How to convince stakeholders that both Bayesian and frequentist methods are useful Focus should be on the problem and not the methods convey balanced opinions when presenting a Bayesian approach to a problem Our focus in 2013/2014 via paper

Specific Actions © 2015 DIA, Inc. All rights reserved.

General Needs Acceptance of type 1 error control through simulation in AD and non-AD Agreement to use informative priors Greater and consistent knowledge of AD and Bayesian methods in Industry and Regulatory Sponsors and agency can discuss and reach agreement on technical questions related to modeling and simulations earlier Consistent format to enable easier agency review when sponsors submit simulations More transparency from Sponsors on use of Bayes/AD More insight and metrics in terms of the types of AD/Bayesian approaches which are submitted Better use of modeling clinical endpoints AD/Bayesian focus in one of the FDA-funded Centers of Excellence in Regulatory Science and Innovation. This could allow the FDA to leverage external expertise in these areas in an academic setting which could better inform agency decisions. Bayesian topics: type 1 error (define null space), agreement on informative priors as appropriate, consistent format, Education is important!

Areas BSWG will focus moving forward Case examples Best practices document(s) Enhanced medical involvement and outreach Continue with existing subteams © 2015 DIA, Inc. All rights reserved.