Search by Last Name:

Keyword:

Sun, Sep 14

2008 FDA/Industry Registrant List

6:00 AM - 6:30 PM

Chair(s): Kathleen Wert, ASA

2008 FDA/Industry Registrant List
 View Presentation Kathleen Wert, ASA

Mon, Sep 15

SC1 Short Course 1

8:30 AM - 12:00 PM
Salons ABC

Clincial Trial Adaptive Design
H.M. James Hung, U.S. Food and Drug Administration; Sue-Jane Wang, U.S. Food and Drug Administration

SC2 Short Course 2

8:30 AM - 12:00 PM
Salons HJK

Biomarkers in Risk Prediction
 View Presentation Margaret S. Pepe, Fred Hutchinson Cancer Research Center

SC3 Short Course 3

8:30 AM - 12:00 PM
Salons I&II

Statistical Graphics
 View Presentation Frank E. Harrell, Jr., Vanderbilt University

SC4 Short Course 4

1:30 PM - 5:00 PM
Salons HJK

Multiple Testing Problems in Pharmaceutical Statistics
 View Presentation Alex Dmitrienko, Eli Lilly and Company

SC5 Short Course 5

1:30 PM - 5:00 PM
Salons ABC

Meta-analysis of Clinical Trials
 View Presentation Anne Whitehead, Lancaster University, UK

Meta-analysis of Clinical Trials: Section 1
 View Presentation Anne Whitehead, Lancaster University, UK

Meta-analysis of Clinical Trials: Section 1 Supplement
 View Presentation Anne Whitehead, Lancaster University, UK

Meta-analysis of Clinical Trials: Section 2
 View Presentation Anne Whitehead, Lancaster University, UK

Meta-analysis of Clinical Trials: Section 2 Supplement
 View Presentation Anne Whitehead, Lancaster University, UK

Meta-analysis of Clinical Trials: References
 View Presentation Anne Whitehead, Lancaster University, UK

SC6 Short Course 6

1:30 PM - 5:00 PM
Salons I&II

Applications of Bayesian Statistics in Clinical Trials: Part 1
 View Presentation Thomas A. Louis, Johns Hopkins University

Applications of Bayesian Statistics in Clinical Trials: Part 2
Andrew Mugglin, The University of Minnesota

Applications of Bayesian Statistics in Clinical Trials: Part 3
 View Presentation Scott Berry, Berry Consultants

Tue, Sep 16

GS1 Adaptive Designs: Regulatory Perspective and Evolving Methodology

8:30 AM - 10:00 AM
Salons ABJK

Organizer(s): Steve Bai, Food and Drug Administration; Erik Pulkstenis, Human Genome Sciences

Chair(s): Steve Bai, Food and Drug Administration; Erik Pulkstenis, Human Genome Sciences

The use of adaptive design in clinical development programs has grown considerably in the recent years. It is clear that having regulatory guidance on adaptive designs would be useful to the clinical trial community, and its development is a high priority for the FDA. This session will focus on regulatory position, application issues and new findings of the adaptive design methodology. Regulatory presentation will focus on the FDA’s current thinking on the various types of adaptive trial designs, outstanding clinical issues and statistical requirements. Methodology presentations will focus on recent developments in dose-ranging studies and confirmatory multi-arm studies.

Adaptive Designs for Dose Ranging Studies
 View Presentation Vladimir Dragalin, Wyeth Research

On the Design and Analysis of Multiarmed Adaptive Trials
 View Presentation Gernot Wassmer, IMSIE

Regulatory (Draft) Guidance for Industry and Statistical Properties on Early versus Late Phase Adaptive Design Trials
Sue-Jane Wang, U.S. Food and Drug Administration

Discussant(s): Robert O'Neill, U.S. Food and Drug Administration

GS2 Effective Communications between FDA and Industry Statisticians

10:15 AM - 11:45 AM
Salons ABJK

Organizer(s): Susan Duke, GlaxoSmithKline; Qian Graves, U.S. Food and Drug Administration; Patrick Liu, UCB Pharma; Karen Qi, U.S. Food and Drug Administration

Chair(s): Susan Duke, GlaxoSmithKline; Qian Graves, U.S. Food and Drug Administration

We are encouraged to improve communications between FDA and industry statisticians on drug projects, but how does one do that? This session aims to suggest and discuss practical ways to improve communications and understanding for both industry project statistician and FDA statistical reviewer.

Top 10 Effective Strategies for Successful Interactions
Tammy Massie, U.S. Food and Drug Administration

Developing Best Practices Regarding Statistical Analysis Plans
 View Presentation Mary E Nilsson, Eli Lilly & Company

Statistical Analysis Plans - Keeping development plans on track by resolving the disconnect between the FDA and Sponsors
 View Presentation Paul Flyer, Pacific Northwest Statistical Consulting, Inc.

How Do We Communicate? The Ways and Means and Getting Better
 View Presentation Stephen Wilson, U.S. Food and Drug Administration

R1 2008 Workshop Roundtables

11:45 AM - 1:00 PM
Salons C-H & Skyview

RL01 Effective Communication with an FDA Statistician
Aloka Chakravarty, U.S Food and Drug Administration

RL02 Missing Data
Shekar Yadavagiri, sanofi pasteur

RL03 Evaluating the Quality of Randomization
Vance Berger, National Cancer Institute

RL04 Statistical Issues in Vaccine Research
Jack S Nyberg, sanofi pasteur

RL05 Gene Expression-based Assays
Samir Lababidi, U.S. Food and Drug Administration

RL06 Logistics of Implementing Adaptive Trial Designs
Eva Miller, ALMAC Clinical Technologies

RL07 Path from SDTM to ADaM for Regulatory Submission
Sun Sook Kim, BioMarin, Inc

RL08 Analysis of Time-to-Event Data with Low Event Rates
Estelle Russek-Cohen, U.S. Food and Drug Administration

RL09 Statistical Challenges in Alzheimer's Disease (AD) Clinical Trials
Yonggang Zhao, Wyeth

RL10 Statistical Analysis of Safety of Medical Devices
Pablo Bonangelino, U.S. Food and Drug Administration

RL11 More Effective Use of Graphics
Susan Duke, GlaxoSmithKline; Naomi Robbins, NBR

RL12 Data Poolability
Chul Ahn, U.S. Food and Drug Administration

RL13 The Role of a Biostatistician in a Multi-disciplinary Team
Min Yao, Vertex Pharmaceuticals, Inc.

RL14 Sample Size Re-estimation: Pros and Cons
Shiling Ruan, U.S. Food and Drug Administration

RL15 Multiregional Trials
Yoko Adachi, U.S. Food and Drug Administration

RL16 Statistical Prediction in Adaptive Designs
Ming-Dauh Wang, Eli Lilly and Company

RL17 Fixed vs. Mixed Non-Inferiority Margin in the Evaluation of Medical Devices
Shiowjen Lee, U.S. Food and Drug Administration

RL18 Appropriate Variance Estimation when Setting Limits and Implementing QbD
Anthony J. Lonardo, Imclone Systems

RL19 What Do We Gain with Placebo Treated Patients in First-in-Human Studies?
Bob Parker, Amgen

RL20 Multiple Comparisons for Efficacy Analysis in Pivotal Phase 3 Trials
Brian Wiens, Gilead Colorado

RL21 Challenges & Issues Faced by Clinical Trial Data Monitoring Committees in Clinical Trials with Adaptive Designs
Yan Wang, U.S. Food and Drug Administration

RL22 Can a Postmarket Study be Used to Expand Indications for use of Medical Devices?
Hesha Duggirala, U.S. Food and Drug Administration

RL23 Statistical Issus in Pharmacogenetics
Liling Warren, GalxoSmithKline

RL24 Planning for Effective Use of QTc Data in Early Clinical Trials
Mike Hale, Amgen

RL25 Utilizing a Bayesian Predictive Probability Design for a Phase II Cancer Trial
Li Chen, Amgen

RL26 Patient-Reported Outcomes Measurement Information System (PROMIS)
Laura Lee Johnson, DHHS/NIH/NCCAM

RL27 Missing Data Handling
Yongman Kim, U.S. Food and Drug Administration

RL28 Talking Standards: From ADaM to WebSDM
Stephen Wilson, U.S. Food and Drug Administration

RL29 Issues on Statistical Assessment of Abuse Potential of a New Drug (Human Studies)
Ling Chen, U.S. Food and Drug Administration

RL30 Restricted and Unrestricted Models in the Domain of Mixed Models
Jennifer Smith, Geron Corporation

RL31 Challenges in Pain Assessment in Infants, Companion Animals, and other Non-verbalizing Patient Population
Jean Recta, U.S. Food and Drug Administration

RL32 Risk Model for Informing Protocol and Design
Carolyn Carroll, Stat Tech Inc

RL33 Biomakers in Clinical Development
Maha Karnoub, Wyeth

RL34 Subgroup Claim: When to Accept?
Rajeshwari Sridhara, U.S. Food and Drug Administration

RL35 Communicating Effectively in a Multi-disciplinary Setting: What should be taught in graduate school?
Janice Derr, U.S. Food and Drug Administration

RL36 Evaluate Overall Survival with Crossover in Oncology Clinical Trials
Xin Huang, Pfizer

RL37 Is the "Grizzle Option" the Best Approach When There's a Sequence Effect in a 2X2 Cross-Over
Veronica N Taylor, U.S. Food and Drug Administration

RL38 Statistical Issues on Cardiac Risk Assessment Based on Thorough QTc Studies
Yi Tsong, U.S. Food and Drug Administration

RL39 Experience with CDISC and Why Statisticians Need to be Involved
Phillip Pichotta, Statistical Consultant

RL40 Challenges and Solutions in Designing of CNS Trials
David Jianjun Li, Wyeth

CMC1 (Panel Discussion) How Can Statistics Facilitate the Clinical/Nonclinical Link in a Quality by Design Approach to Pharmaceutical Development?

1:00 PM - 2:15 PM
McLean Room

Organizer(s): Stan Altan, Johnson & Johnson; David Christopher, Schering-Plough Research Institute; Timothy Schofield, Merck & Company; Yi Tsong, U.S. Food and Drug Administration

Chair(s): Timothy Schofield, Merck & Company

Pharmaceutical development typically proceeds down three parallel tracks. One track studies the impact of a drug, biologic, or vaccine in the target clinical population; the second studies product in preclinical models; while the third focuses on the chemistry, manufacturing and control of the pharmaceutical product. Although there are many opportunities for coordination among these tracks, the pharmaceutical industry has not taken full advantage of the potential synergies. In particular nonclinical development has generally not engaged clinical and preclinical development in an effort to establish clinically meaningful acceptance criteria on key specifications for manufactured product. The link between Chemistry, Manufacturing and Control (CMC) attributes and clinical outcome is essential to meeting the vision of Quality by Design. In this session a panel of specialists and statisticians in the three areas of pharmaceutical development and regulatory sciences will discuss challenges and strategies and engage participants in a conversation about developing clinically meaningful specifications for pharmaceutical products.

How can Statistics Facilitate the Clinical/Nonclinical Link in a Quality by Design Approach to Pharmaceutical Development?
Darrell Abernathy, USP; Alan Hartford, Merck & Co., Inc.; Jeff Hofer, Eli Lilly; Ron Menton, Wyeth; Tom Schultz, Johnson & Johnson

PS01 Missing Data in Confirmative Trials

1:00 PM - 2:15 PM
Salon A

Organizer(s): Jose Pinheiro, Novartis Pharmaceuticals; Guoxing (Greg) Soon, U.S. Food and Drug Administration

Chair(s): Guoxing (Greg) Soon, U.S. Food and Drug Administration

Missing data is pervasive in clinical trials, having a direct impact on the interpretation and validity of their conclusions. The problem is particularly critical in the context of confirmatory trials, where approaches for addressing and assessing the impact of missing data need to be prospectively discussed with regulators and included in the study protocol. Despite its relevance and the considerable research interest it has generated in recent years, missing data continues to be an elusive problem. This section will discuss missing data in the context of confirmatory trials from a regulatory (what are the issues and concerns) and methodological (possible ways of addressing it) perspectives.

Missing Data: What Is the Question?
Thomas Permutt, U.S. Food & Drug Admin.

Analysis of Informatively Coarsened Data: A Sensitivity Analysis Approach
Michelle Shardell, University of Maryland School of Medicine

Sensitivity Analyses to Assess Non-Ignorability of Missing Data in Confirmatory Clinical Trials
 View Presentation Jose Pinheiro, Novartis Pharmaceuticals; Jie Zhang, Novartis

PS02 Leaping Forward: from Symptomatic to Disease Modification

1:00 PM - 2:15 PM
Salon B

Organizer(s): Chunming (Mark) Li, Pfizer; Jingyu (Julia) Luan, U.S. Food and Drug Administration

Chair(s): Jingyu (Julia) Luan, U.S. Food and Drug Administration

Today most of medicines could only provide symptomatic benefit, developing a drug that truly modifies the course of a disease will be extremely rewarding but also is extremely challenging. The traditional study endpoint analysis and parallel group design would not differentiate study drug effect that could be either symptomatic, disease modifying or combination of both. Randomized start design or delayed-start design was proposed by FDA clinical scientist more than ten years ago as an ambiguous path to demonstrate such a structure effect. Unfortunately these trials are rarely performed due to unresolved statistical methodology issues. In this session, novel statistical analytic approaches are proposed and discussed to address these issues first time.

Issues in the Implementation and Statistical Analysis of Two-period Designs for Demonstrating Disease-modifying Effects
 View Presentation Michael P. McDermott, University of Rochester Medical Center

Exploring Statistical Approaches to Demonstrate Disease Modifying Effect in Alzheimer’s Disease Trials
Richard Y Zhang, Pfizer Inc

Method to Discern Symptomatic and Disease Modifying Drug Effects for the Treatment of Parkinson’s Disease
Ohidul Siddiqui, Food and Drug Administration

Discussant(s): H.M. James Hung, U.S. Food and Drug Administration

PS03 Statistical Challenges in Biomarker Development as a Diagnostics Classifier

1:00 PM - 2:15 PM
Salon K

Organizer(s): Maha Karnoub, Wyeth; Wei Liu, U.S. Food and Drug Administration; Liling Warren, GalxoSmithKline

Chair(s): Sue-Jane Wang, U.S. Food and Drug Administration

The use of in vitro diagnostic (IVD) tests or imaging diagnostic tests to detect and measure biomarkers or clinical characteristics provides promising avenues for development of new and better drugs, and will be central to patient selection or therapeutic outcome assessment. With the availability of new technologies, measurement of a single biomarker or a composite biomarker developed from gene expression data or whole genome SNPs data can be considered. Proper validation of the diagnostics’ performance as an IVD test or an imaging test used to classify patients based on the biomarker status is vital to characterize benefit/risks of a therapeutic. This session will include statistical uncertainties in the develop of a genomic biomarker based on microarray technology, statistical metrics to assess if a diagnostic test can perform accurately and reproducibly prior to its use for therapeutic trials, establishment of the clinical utility of a diagnostic test, and statistical metrics for assessing agreement between two diagnostics when a gold standard comparator is available versus when no gold standard exists. Examples from an in vitro diagnostic test and an imaging diagnostic test will be given. Presenters will give regulatory and scientific views, propose statistical metrics and share experiences.

Hunting for Significance with the False Discovery Rate
Martin Posch, Medical University of Vienna

Concordance Correlation Coefficient to Assess Reproducibility of Gene Expression Indices
 View Presentation Christos Hatzis, Nuvera Biosciences

Showing Clinical Utility of a Diagnostic Test by Evaluating Agreement with a Reference Test: Study DesignIissues
 View Presentation Ann Olmsted, CV Therapeutics, Inc.; Whedy Wang, CV Therapeutics, Inc.

Some Liabilities of Kappa Measure of Agreement between Two Imaging Diagnostics
Anthony G Mucci, FDA/CDER

PS04 Meta-Analysis of Clinical Studies

1:00 PM - 2:15 PM
Salon J

Organizer(s): Jesse Berlin, Johnson & Johnson Pharmaceutical R&D, LLC; Bruce Binkowitz, Merck Research Laboratories; May Mo, Amgen; Lilly Yue, U.S. Food and Drug Administration

Chair(s): Bruce Binkowitz, Merck Research Laboratories

Meta-analyses of clinical studies have drawn much attention in recent public health news. This session will provide perspectives on meta-analyses in which the summary statistics or patient-level data from multiple clinical studies are statistically combined (or contrasted) for quantitative evaluation. Discussants from industry, FDA, and academia will provide statistical and other methodological discussions of these meta-analyses. Specific examples, drawn from analyses intended to answer questions on safety and/or efficacy, will be discussed to illustrate the methodological issues.

The Cochrane Collaboration's Standards for Conducting Systematic Reviews of Interventions: One of Many Valid Approaches or Gold Standard?
 View Presentation Kay Dickersin, Johns Hopkins Bloomberg School of Public Health

Meta -analysis in the Evaluation of the Safety of Erythropoietin Stimulating Agents: Recent Experiences and Lessons Learned
 View Presentation Jesse Aaron Berlin, Johnson & Johnson Pharmaceutical R&D; Dianne K. Tomita, Amgen

Planning and Executing a Meta-analysis of Drug Safety: General Principles and Special Considerations
 View Presentation Mark Steven Levenson, FDA/CDER

CMC2 (Panel Discussion) Statistical Considerations for Development of Design Space

2:30 PM - 3:45 PM
McLean Room

Organizer(s): Stan Altan, Johnson & Johnson; David Christopher, Schering-Plough Research Institute; Timothy Schofield, Merck & Company; Yi Tsong, U.S. Food and Drug Administration

Chair(s): David Christopher, Schering-Plough Research Institute

Quality by Design (QbD) and the Critical Path Initiatives have placed a greater emphasis on the science underlying the manufacturing process and characteristics of pharmaceutical products. It holds out the promise of reduced regulatory burden in exchange for a greater level of understanding, as demonstrated by design space and applications of multivariate statistical methods. Consequently, the question of greater level of understanding has given rise to a host of new questions and approaches in demonstrating process and product capability. Emphasis will be placed on the issue of drawing inferences where the process characteristics at the batch level will be argued as completely consistent with the new QbD and Critical Path paradigm. In this session a panel of specialists and statisticians in pharmaceutical development will discuss challenges and strategies and engage participants in a conversation about developing a design space which meets regulatory expectations.

Statistical Considerations for Development of Design Space
Theodora Kourti, GlaxoSmithKline; Christina Moore, U.S. Food and Drug Administration; Venkat Sethuraman, Novartis; Greg Stockdale, GlaxoSmithKline; Yi Tsong, U.S. Food and Drug Administration

PS05 Important Statistical Concepts in the Planning, Analysis and Monitoring of Vaccine Trials

2:30 PM - 3:45 PM
Salon A

Organizer(s): Christine Gause, Merck Research Laboratories; Tammy Massie, U.S. Food and Drug Administration; Radha Railkar, Merck Research Laboratories

Chair(s): Christine Gause, Merck Research Laboratories; Tammy Massie, U.S. Food and Drug Administration

Vaccines are administered to most of the American population, and development of vaccines for administration to a broad population present unique design and analysis issues in clinical trials. Statistical concepts in vaccine development such as the selection of endpoints, the choice of analysis populations in adult vaccine trials, and post-licensure surveillance of vaccines will be presented by speakers from the FDA, Academia, and Industry. An overview of regulatory perspectives for evaluation of safety and efficacy in vaccine clinical trials will be presented. Important criteria to consider for selection of endpoints in assessing vaccine safety, immunogenicity, and efficacy will be presented, with an emphasis on surrogate endpoints. To adequately evaluate the ability of a vaccine to prevent a disease, the primary analysis population typically needs to be a per-protocol population that at least requires subjects to be naïve for the disease immediately pre-vaccination and often requires subjects to receive the complete vaccination regimen as well. However, when the vaccine under study is intended for adolescents and adults, it is often not realistic to expect the target population to be completely naïve for the disease of interest. Modified intention-to-treat (ITT) analyses can help to explain the vaccine’s impact on a broader population than the per-protocol. However, caution is often required in interpreting the results of these analyses. Some issues in using ITT analyses in vaccine studies are discussed with particular application to a recently licensed vaccine. Statistical issues in community randomized vaccine trials will be presented.

Statistical Challenges in Vaccine Clinical Studies: An FDA Perspective
Amelia Dale Horne, FDA-CBER

Considerations in Subject Selection for Preventative Vaccine Studies in Older Populations
Lisa C. Lupinacci, Merck & Co., Inc.

Black Box Approaches to the Evaluation of Indirect Effects of Vaccine Deployment
 View Presentation Lawrence H. Moulton, Johns Hopkins Bloomberg SPH

PS06 Applications of Mixed-Effects Models in Confirmatory Clinical Trials

2:30 PM - 3:45 PM
Salon B

Organizer(s): Phillip Dinh, U.S. Food and Drug Administration; Veronica N Taylor, U.S. Food and Drug Administration

Chair(s): Veronica N Taylor, U.S. Food and Drug Administration

The use of mixed-effects models in early drug development is widespread, but it is less frequent in late phase, and especially in confirmatory trials. There are several potential advantages associated with the use of mixed-effects models compared to more traditional, single time point or change from baseline approaches, including more efficient use of information and better handling of missing data. However, because of their added complexity, such models also require more assumptions and design specifications (e.g., number of random effects, covariance structures, etc). This session will discuss the pros and cons of mixed-effects models in confirmatory trials, with a focus on applications.

Issues and Approaches for the Analysis of Longitudinal Data in Confirmatory Clinical Trials
 View Presentation Craig H Mallinckrodt, Eli Lilly

The Use of Mixed-Effect Models in Confirmatory Clinical Trials: Some Issues and Cautions
 View Presentation Yeh-Fong Chen, FDA

Mixed-Effects Models with Parametric Time-Response in Confirmatory Trials
 View Presentation Jose Pinheiro, Novartis Pharmaceuticals

PS07 Statistical and Regulatory Issues for the Companion Diagnostic in Drug-diagnostic Combinations

2:30 PM - 3:45 PM
Salon K

Organizer(s): Victoria Petrides, Abbott Laboratories; Rong Tang, U.S. Food and Drug Administration

Chair(s): Victoria Petrides, Abbott Laboratories

Drug-Diagnostic combinations are a fast developing area and one of FDA's critical path initiatives. What is required for the diagnostic to be approved and when it needs approval are still complicated and confusing in a lot of cases. This panel will consider some of the issues faced by manufacturers of the diagnostic part.

Regulatory Issues in Co-Development
Steven Gutman, FDA

Drug-Diagnostic Co-Development: Statistical Issues for Companion Diagnostic
 View Presentation Lakshmi Vishnuvajjala, U.S. Food and Drug Administration

Important Considerations when Developing Complex Companion Diagnostics
 View Presentation Wendell Davis Jones, Expression Analysis, Inc.

Statistical Issues for the Companion in Drug-Diagnostic Combinations
 View Presentation Nusrat S Rabbee, Genentech, Inc.

PS08 Current Challenges and Developments in Sample Size Re-estimation

2:30 PM - 3:45 PM
Salon J

Organizer(s): Ning Li, U.S. Food and Drug Administration; May Mo, Amgen; Vivian Yuan, U.S. Food and Drug Administration

Chair(s): May Mo, Amgen

Coined at the 1990 Society for Clinical Trials Annual Conference in Toronto, sample size re-estimation has attracted many researchers’ interest in the clinical trial field. Literatures and discussions on sample size re-estimation are abundant in journals, newsletters and meetings, with proposals of adaptive sequential design and two-stage adaptive designs. Estimation of sample size in clinical trials requires knowledge of parameters that involve the treatment effect, within- or between-patient variability, patient recruitment pattern, patient compliance, and many other related issues, which are uncertain in many cases to researchers before starting the trial. Sample size re-estimation at the interim analysis provides the flexibility to use the information from the current trial at an interim stage to update the initial estimates and make adjustment of the sample size to ensure that the study's objective is accomplishable and sufficient power is achievable. However, the methods for sample size re-estimation in various adaptive sequential designs have been less than ideal to provide flexibility yet maintain the integrity of the trial. In this session representatives from academia, industry and regulatory share their new advances in research as well as reviews and recommendations on this topic.

Controlling Family Wise Error for Multiple Endpoints within a Group Sequential Trial with Sample Size Re-estimation
 View Presentation Cyrus R. Mehta, President, Cytel Inc.

Sample Size Re-estimation: Technical and Practical Problems
Kuang-Kuo Gordon Lan, Johnson & Johnson PRD

A Statistical Reviewer’s Perspective on Sample Size Re-estimation
 View Presentation Boguang Zhen, FDA/CBER

PS09 Data Formats and Programming Issues

4:00 PM - 5:15 PM
Salon A

Organizer(s): Jingyee Kou, U.S. Food and Drug Administration; Patrick Liu, UCB Pharma; Yadavagiri Shekar, Sanofi Pasteur

Chair(s): Jingyee Kou, U.S. Food and Drug Administration; Patrick Liu, UCB Pharma

For data gathered in clinical trials, each pharmaceutical company has its own data format and variable names. For the past decade, CDISC has been developing standard data formats and implementation guide for clinical trial data. In this session, we will have two presentations on the ADaM data format, one from the CDISC perspective and one from the FDA perspective. Since computer programming is inseparable from data analysis, we will show in the 3rd presentation that some seemingly harmless anomalies in a SAS program could lead to incorrect conclusions of study results.

A Statistician’s Guide to Applying ADaM Standards for the Analysis of Clinical Trial Data
Susan J. Kenny, Inspire Pharmaceuticals

CDISC Standards in CDER: An Informal Survey
 View Presentation Chris Holland, FDA

Computational Considerations in using SAS
 View Presentation Yadavagiri Shekar, Sanofi Pasteur

PS10 Special Survival Analysis Methods Used in Different Types of Clinical Trials

4:00 PM - 5:15 PM
Salon B

Organizer(s): Mridul Chowdhury, U.S. Food and Drug Administration; Barbara Krasnicka, U.S. Food and Drug Administration; William Wang, Merck & Co., Inc.

Chair(s): Terry Therneau, Mayo Clinic

The importance of survival analysis in medical research, particularly in clinical trials, can hardly be overemphasized. However, use of survival analysis in different clinical trials requires sometimes development of new special methodologies that solve particular problems encountered. This session will cover some of such methodologies applied in clinical trials on vaccines, cancer treatments, and diagnostic devices (such as gene expression, microarrays, and prognostic biomarkers).

Cure Rate Models in Clinical Trials
Joseph G Ibrahim, UNC

Survival with Rare Outcomes: Examples from Diagnostic Device Trials
 View Presentation Estelle Russek-Cohen, U.S. Food and Drug Administration

Application of Survival Analysis Methodologies in Vaccine Trials
Yanli Zhao, Biostatistics and Research Decision Sciences, Merck Research Laboratory, Merck

Discussant(s): Terry Therneau, Mayo Clinic

PS11 Statistical Issues in Clinical Trials to Develop Drugs/Devices for Medical Conditions with Demonstrated Placebo Response

4:00 PM - 5:15 PM
Salon K

Organizer(s): Jianxiong (George) Chu, U.S. Food and Drug Administration; Sarah Hurwicz Kogut, i3 Statprobe

Chair(s): Robert J. Coffey, Medtronic, Inc.

This session will focus on some of the issues in studying treatment of conditions such as chronic pain or asthma with drugs or devices. Chronic pain has multiple etiologies including surgery, work-related injury, or side effects of conditions such as diabetes. Treatment may include medication, neurostimulation device, nerve-destruction, and acupuncture. Evaluation of the efficacy of pain treatments can be difficult, in particular because the endpoints are usually based on patient-reported outcomes. The patient’s own evaluation of their response could be influenced by their expectation, which in turn could be influenced by information provided by study personnel and documentation. For this reason, randomized, placebo-controlled, double-blinded comparative trials are desirable. Desirable aspects of clinical trial design can be potentially difficult to implement in implantable device studies. However, when simplistic designs are used the sake of convenience, the study results may not provide valuable scientific evidence. Asthma is another condition where placebo effect has been demonstrated, with the difference that outcome is an objective physiologic measurement. This session will include both pharmaceutical and device examples, as well as discussion of the comparative magnitude of effects of drug and device placebos in treatment of chronic pain.

Strategies for Reducing the Placebo Response: Some Examples from Drug Clinical Trials
 View Presentation Arlene S Swern, Merck Research Laboratories

Design Considerations in Medical Device Clinical Trials for Chronic Conditions
Sarah Hurwicz Kogut, i3 Statprobe

Results of a Randomized Trial to Study Placebo Effects
Roger B. Davis, Beth Israel Deaconess Medical Center; Ted J. Kaptchuk, Harvard Medical School

Neuromudulation Therapies for Chronic Pain, Movement Disorders, and Psychiatric Indications
Robert J. Coffey, Medtronic, Inc.

PS12 Issues in Phase I Drug Development

4:00 PM - 5:15 PM
Salon J

Organizer(s): Marla Currin, GalxoSmithKline; Stephanie Dunbar, Merck Research Laboratories; Alan Hartford, Merck & Co., Inc.

Chair(s): Alan Hartford, Merck & Co., Inc.

Issues in Phase I Drug Development
 View Presentation Dennis Cosmatos, Wyeth; Jeetu Ganju, Amgen; Fang Liu, Merck; Charles Locke, Abbott; Xiaoling Meng, sanofi-aventis; Venkat Sethuraman, Novartis; Hao Zhu, Pharmacometrics, FDA

Wed, Sep 17

PS13 How Graphs Can Make a Difference for Statisticians and Their Customers

8:45 AM - 10:00 AM
Salon A

Organizer(s): Mary Bartholomew, U.S. Food and Drug Administration; Anna Nevius, U.S. Food and Drug Administration

We've all heard the saying, "a picture is worth 1000 words." Putting data and study results into graphical form makes it easier for both the statistician and their colleagues who are also involved in data interpretation and decision-making for a given drug or device to understand the information. This session will focus on graphical design, graphical methods for statisticians, and communicating results via graphs to customers.

How to Avoid Common Graphical Mistakes Biostatisticians Make
 View Presentation Naomi Robbins, NBR

Communicating Clinical Trial Results: An FDA Statistical Reviewer’s Perspective
Mat Soukup, U.S. Food and Drug Administration

A Case Study for More Effective Use of Graphics
 View Presentation Susan P Duke, GlaxoSmithKline

PS14 What Multiplicity Adjustment Is Needed?

8:45 AM - 10:00 AM
Salon B

Organizer(s): Qian Li, U.S. Food and Drug Administration; Kooros Mahjoob, U.S. Food and Drug Administration; Gosford A. Sawyer, Purdue Pharma

Chair(s): Peiling Yang, U.S. Food and Drug Administration

NDA submissions usually contain data from multiple clinical trials investigating efficacy of multiple dosing regimens, with multiple endpoints possibly assessed at multiple time points. As a result, issues relating to control of the Type I error rate (alpha) arise. Confirmatory trials usually achieve a control by prospectively planned multiplicity adjustment strategies. However, due to the complexity of some Clinical Development programs supporting NDAs, often it is unclear how alpha should be controlled, whether at the trial level or at the level of indication. When is it necessary to adjust, and what sort of adjustment is needed? By means of a set of questions posed to a distinguished panel, the session will debate multiplicity issues for trials with composite and co-primary endpoints, and reflect on decision error concepts when evidence of efficacy is derived from multiple trials.

A Short Introduction on Composite and Co-primary Endpoint Issues
 View Presentation Mohammad Huque, U.S. Food and Drug Administration

Multiplicity Issues in Multiple Studies
 View Presentation Qian Li, U.S. Food and Drug Administration

Panel Discussion
 View Presentation

Discussant(s): Alex Dmitrienko, Eli Lilly and Company; Joseph Heyse, Merck & Co Inc; Jason C. Hsu, The Ohio State University; H.M. James Hung, U.S. Food and Drug Administration; Mohammad Huque, U.S. Food and Drug Administration; Robert O'Neill, U.S. Food and Drug Administration; Robert Temple, U.S. Food and Drug Administration; Marc Walton, Food and Drug Administration; Brian Wiens, Gilead Colorado

PS15 Statistical Issues and MAQC II

8:45 AM - 10:00 AM
Salon K

Organizer(s): Fred Immerman, Wyeth; Lakshmi Vishnuvajjala, U.S. Food and Drug Administration; Jialu Zhang, U.S. Food and Drug Administration

Chair(s): Greg Campbell, U.S. Food and Drug Administration

The MAQC II project is a collaboration between different centers of FDA, N.I.H, academia and the private industry to develop methodology to build classifiers using micro-array data and validating them. Several groups are working on six different datasets that are available to learn what methods provide robust classifiers.

The MicroArray Quality Control (MAQC) Project: An FDA-led Effort Toward Personalized Medicine
Leming Shi, FDA's National Center for Toxicological Research

MAQC-II Experiences in the Development and Validation of Genomic Predictive Models for Clinical and Toxicogenomic Data Sets
Russell D. Wolfinger, SAS Institute Inc.

MAQC-II Project: The Experience of the CDRH Data Analysis Team
Samir Lababidi, U.S. Food and Drug Administration; Francisco Martinez-Murillo, FDA / CDRH; Gene Pennello, FDA / CDRH; Reena Philip, FDA / CDRH; Daya Ranamukhaarachchi, FDA / CDRH; Rong Tang, U.S. Food and Drug Administration; Zivana Tezak-Fragale, 2098 Gaither Road

PS16 Adaptive Design Case Studies: Theory into Practice

8:45 AM - 10:00 AM
Salon J

Organizer(s): Brenda Gaydos, Eli Lilly and Company; Kooros Mahjoob, U.S. Food and Drug Administration

Formulating Decision Rules for an Adaptive Long-Term Disease Modifying Trial in Osteoarthritis
 View Presentation Michael J Brown, Pfizer Global Research and Development

Case Study for a Two Stage Adaptive Seamless Phase IIb/III Confirmatory Design
 View Presentation Jeffrey Maca, Novartis Pharmaceuticals

A Seamless 2/3 Design Incorporating a Clinical Utility Index
 View Presentation Zachary Kulkarni Skrivanek, Eli Lilly & Companay

PS17 Assessment of QTc Prolongation in Clinical Development

10:20 AM - 11:35 AM
Salon A

Organizer(s): Alex Dmitrienko, Eli Lilly and Company; Yi Tsong, U.S. Food and Drug Administration

Chair(s): Alex Dmitrienko, Eli Lilly and Company

Assessment of cardiac liability of new compounds, particularly with respect to life threatening ventricular tachydysrhythmias, e.g., Torsades de Pointes (TdP), is becoming an increasingly important component of clinical drug development. Lengthening of QTc interval is commonly used as a surrogate biomarker for an increased risk of TdP. The International Conference on Harmonization (ICH) published a guidance document (ICH E14) to describe strategies for the evaluation of cardiac safety of drugs in clinical development. This document introduced a new approach to the assessment of proarrhythmic potential of new drugs (thorough QT/QTc study). Thorough QT/QTc study are now required for virtually all non-cardiac drugs with systemic bioavailability and design and analysis considerations in thorough QT/QTc studies have received much attention in clinical trial literature. This session will focus on statistical issues arising in the design and analysis of thorough QT/QTc studies, including • Key design issues (single-dose and steady-state designs, time points for ECG acquisition, number of replicate ECG recordings, etc). • Common approaches to the analysis of QTc interval data in thorough QT/QTc studies (e.g., QT correction methods, multiplicity issues, assay sensitivity analysis, QTc-exposure analysis, etc).

Concentration-QT Analysis in the Presence of Pharmacokinetic and/or Pharmacodynamic Interaction
Garnett Christine, Pharmacometrics, FDA; Jogarao Gobburu, Pharmacometrics, FDA; Yaning Wang, Pharmacometrics, FDA; Hao Zhu, Pharmacometrics, FDA

Impact of Delayed Effects in the Exposue-response Analysis of Clinical QT Studies
 View Presentation Arne Ring, Boehringer Ingelheim Pharma GmbH & Co. KG

QTc-exposure Modeling in Thorough QT Studies
Xiaoling Meng, sanofi-aventis; William Wang, Biostatistics and Research Decision Sciences, Merck Research Laboratory, Merck

PS18 Utilization of Subgroup Analysis Findings

10:20 AM - 11:35 AM
Salon B

Organizer(s): Chul Ahn, U.S. Food and Drug Administration; Joan Buenconsejo, U.S. Food and Drug Administration; Peter Lam, Boston Scientific

Chair(s): Chul Ahn, U.S. Food and Drug Administration

Findings from a subgroup analysis may be viewed as a definitive result if the subgroups are pre-specified and properly sized. However, a clinical trial is not usually sized to permit valid subgroup analyses. In this session, speakers from the industry and the FDA will review issues surrounding the use of subgroup analysis findings from both frequentist and Bayesian perspectives. The session will close with a discussion by Dr. Richard Simon on his views about the issues presented by the speakers.

Statistical Considerations for Multiplicity in Subgroup Analyses
Mohamed Ahmad Alosh, Division of Biometrics III, OB, OTS, CDER, FDA

Industry Perspective of Subgroup Analyses
 View Presentation Joe W Bero, Boston Scientific

Road to a New Study from Post-hoc Findings: a Bayesian Approach
Chul Ahn, U.S. Food and Drug Administration; Yunling Xu, CBER/FDA

Discussant(s): Richard Simon, National Cancer Institute

PS19 Monitoring, Diagnostic Imaging and In Vitro Diagnostics

10:20 AM - 11:35 AM
Salon K

Organizer(s): Arkendra De, U.S. Food and Drug Administration; Lakshmi Vishnuvajjala, U.S. Food and Drug Administration

Chair(s): Lakshmi Vishnuvajjala, U.S. Food and Drug Administration

In Vitro diagnostics or Lab tests are what people usually think of as diagnostics. Diagnostic imaging and monitoring devices are also part of the diagnostic testing, and the number of submissions to CDRH in these areas is increasing very rapidly. This session considers both In Vitro and In Vivo diagnostics.

Some Design Issues in Radiological Image Reader Studies
Thomas E Gwise, FDA/CDRH

Imprecision Studies for Qualitative Tests: A Framework
 View Presentation Marina Kondratovich, CDRH/FDA; Kristen Meier, CDRH/FDA

Experiments for Improving an IVD
 View Presentation Victoria Hill Petrides, Abbott Laboratories

PS20 Special Topics in Oncology Drug Development

10:20 AM - 11:35 AM
Salon J

Organizer(s): Aloka Chakravarty, U.S Food and Drug Administration; Somesh Chattopadhyay, U.S. Food and Drug Administration; Gang Chen, Johnson & Johnson; Stella Lee, Exelixis; Erik Pulkstenis, Human Genome Sciences; Shenghui Tang, U.S. Food and Drug Administration

Chair(s): Somesh Chattopadhyay, U.S. Food and Drug Administration; Stella Lee, Exelixis

Oncology drug development presents many important and unique challenges across all phases of development. In this session, experts from academia, industry and the regulatory agencies will present various issues spanning all 3 phases of drug development in oncology. Special topics include dose-finding with the continual reassessment and other methods and surrounding issues, design analysis and interpretation of Phase 2 studies to maximize potential for success in Phase 3, and the role of imaging and the complex statistical issues present in the unbiased evaluation of imaging data in oncology trials.

Phase II Trials Designed to Increase the Probability of Success of a Subsequent Phase III Trial
 View Presentation Stephen L George, Duke University

Patient-Specific Dose-Finding Based On Bivariate Outcomes and Covariates
 View Presentation Peter F Thall, M.D. Anderson Cancer Center

Role of Medical Imaging in Cancer trials
Anthony G Mucci, FDA/CDER

GS3 Safety Risk Assessments and Risk Minimization Action Plans

1:00 PM - 2:30 PM
Salons ABJK

Organizer(s): Ih Chang, Novartis; Qian Graves, U.S. Food and Drug Administration; Amy Xia, Amgen, Inc

Chair(s): Ih Chang, Novartis; Qian Graves, U.S. Food and Drug Administration

Recent safety concerns on a number of widely prescribed drugs highlighted the critical role of well-planned thorough safety assessments in the development of new drug, biologics, and vaccines as well as the importance of methods that can aid in managing identified risks. . It has been four years since the publication of the three PDUFA III’s Risk Management Guidance documents premarketing risk assessment, RiskMAP, and pharmacovigilance. A review of current standard on the planning, evaluation, and reporting of data following the Guidance and a case study of a successfully implemented RiskMAP will be great interest to the participants of the workshop. We propose a plenary session for these key presentations. Due to the time constraint of each session, we do not currently have plan for a speaker on pharmacovigilance. We also propose a separate break-out session for technical discussions on some statistical issues in safety assessment, depending on the interest level and availability of session slots.

Planning, Evaluation and Reporting of Safety Data During Drug, Biologic and Vaccine Development
 View Presentation Brenda Crowe, Eli Lilly and Company

Developing Safety Analysis Plans for Regulatory Purposes
George Rochester, U.S. Food and Drug Administration

Development and Use of Risk Minimization Action Plans – A Case Study
Marc Walton, Food and Drug Administration

Discussant(s): Jesse Berlin, Johnson & Johnson Pharmaceutical R&D, LLC; Brenda Crowe, Eli Lilly and Company; Gerald Del Pan, U.S. Food and Drug Administration; George Rochester, U.S. Food and Drug Administration; Robert Temple, U.S. Food and Drug Administration

GS4 Issues in Developing Guidance for Non-inferiority Trials: An Update

2:45 PM - 4:00 PM
Salons ABJK

Organizer(s): Guoxing (Greg) Soon, U.S. Food and Drug Administration; Yong-Cheng Wang, Biogen Idec, Inc.

Chair(s): Guoxing (Greg) Soon, U.S. Food and Drug Administration

Non-inferiority trial design and evaluation is complicated for many reasons. Among them are how to characterize the historical data to get the best estimates of effect size, how to deal with heterogeneity of the effect sizes, how to deal with the lack of compatibility in the populations, and how to take both clinical and statistical views into consideration, and how different statistical methods that have different performance characteristics should be applied. Currently FDA is developing guidance to industry on these important issues. In this session FDA will provide updates on the development of the guidance, and industry will share its perspective and the issues raised will be further discussed in the session.

An Update on FDA’s Guidance for Industry on Non-Inferiority (NI) Trials
Robert O'Neill, FDA

Industry Update
 View Presentation Steve Snapinn, Amgen