Statistics Speaker Agenda 5 by S67a95


									                       Statistical Issues for
                  Medical Devices and Diagnostics
                                 Bethesda Marriott Hotel
                                     Bethesda, MD

                                    April 16 - 17, 2008

April 16, Wednesday


9:00 – 9:10           Welcome
                      Thomas Maeder, Executive Director, MTLI, AdvaMed
                      Daniel Shultz, Director, CDRH, FDA
                      Gregory Campbell, Director Division of Biostatistics, CDRH, FDA

9:10 – 9:30           Statistics in Medical Devices and Diagnostics – An Overview
                      Gregory Campbell, Director Division of Biostatistics, CDRH, FDA
                      General overview of how statistics for devices and diagnostics differs
                      from that in drugs and other areas. How has the field evolved over the
                      years, what are the current issues, and what do we hope to accomplish
                      here during the next two days?

9:30 – 10:50          Session: The Use of Adaptive Design
                      Session planners:        Yonghong Gao, Mathematical Statistician, CDRH,
                                               Xiaolong Luo, Senior Mathematical Statistician,
                      A considerable amount of recent research and discussion has focused
                      on statistical and logistical issues associated with adaptive clinical study
                      designs. Presentations in this session address experiences and lessons
                      learned in the design and implementation of such studies – some
                      utilizing a frequentist adaptive approach and some a Bayesian
9:30 – 9:50     Case Study: Sample Size Re-Estimation
                Roseann White, Director, Global Biostatistics Clinical Data Systems,
                Abbott Vascular
                Adaptive sample size re-estimation techniques can be useful when one
                is unsure of assumptions about the true rates in a randomized or non-
                randomized trial. Operational challenges arise, however, in avoiding the
                introduction of bias or when enrollment time is much shorter than the
                time to follow-up for the interim analysis. This session features a case
                study in which many of these operational issues are creatively

9:50 – 10:10    Case Study: The SAPPHIRE Study
                David Snead, Associate Director, Johnson & Johnson
                The SAPPHIRE Study used a randomized non-inferiority design with a
                novel sequential interval censored survival method to compare carotid
                artery stenting to endarterectomy. The study design and results will be
                presented along with lessons learned.

10:10 – 10:30   A Consultant’s Perspective on Bayesian Adaptive Device Trials
                Scott Berry, President, Berry Consulting
                A consultant discusses aspects of Bayesian approach to device trials
                that are advantageous to the sponsor, regulator, and to patients. A case
                study examines an adaptive device trial using predictive probabilities to
                select one of two devices and the appropriate sample size, in which
                partial information for subjects is utilized with longitudinal models.

10:30 – 10:50   The FDA Perspective on Adaptive Device Trial Design
                Yonghong Gao, Mathematical Statistician, CDRH, FDA
                An FDA statistical reviewer shares experiences in reviewing adaptive
                design trials in terms of both statistical and regulatory considerations.
                Discussion points will be raised that may facilitate better communication
                between industry and FDA in future trials of this type.

10:50 – 11:10   BREAK

11:10 – 12:30   Session: The Application of Bayesian Statistics
                Session planners:        Telba Irony, Chief, General and Surgical
                                         Devices Branch, Division of Biostatistics, CDRH,
                                         Roseann White, Director, Global Biostatistics
                                         Clinical Data Systems, Abbott Vascular
                In 2006, CDRH issued a draft guidance document on the use of
                Bayesian statistics to help people developing or reviewing device or
                diagnostic clinical trials determine whether Bayesian methods would be
                effective and to apply those methods appropriately. This session
                provides more detailed information and illustrative case studies on
                appropriate use of the Bayesian approach. In a concluding panel
                discussion, the presenters and organizers will be available to answer
                questions from the audience.
11:10 – 11:15   Introductory Remarks on Bayesian Device and Diagnostics Studies
                Roseann White, Director, Global Biostatistics Clinical Data Systems,
                Abbott Vascular

11:15 – 11:30   To Use Bayesian or Not to Use Bayesian, that is the Question
                Andrew Mugglin, Research Associate Professor, University of Minnesota
                With any statistical methodology one must understand not only how to
                use it but also when it makes sense in terms of resources and time. This
                session examines the key issues to consider before deciding to use a
                Bayesian approach to the design and analysis of a study.

11:30 – 11:45   Bayesian Statistics for Therapeutic Devices
                Pablo Bonangelino, Mathematical Statistician, CDRH, FDA
                Xuefeng Li, Mathematical Statistician, CDRH, FDA
                Bayesian adaptive designs using predictive probability and regulatory
                considerations regarding these designs for therapeutic devices will be
                presented. How much strength could be borrowed in a hierarchical
                model for a simple case will be discussed.

11:45 – 12:00   Recent Experiences Applying Bayesian Analysis to Medical Device
                A. James O’Malley, Associate Professor of Statistics, Department of
                Health Care Policy, Harvard Medical School
                Applying Bayesian analysis to medical device trials, though challenging,
                can provide insights into your data that are unattainable with other
                methods. Assuring that the method is appropriately applied can be
                challenging, however, and this session addresses the use and
                interpretation of Bayesian analyses.

12:00 – 12:10   Update on the Bayesian Guidance Document
                Telba Irony, Chief, General and Surgical Devices Branch, Division of
                Biostatistics, CDRH, FDA

12:10 – 12:30   Bayesian Panel Discussion
                Moderator:         Roseann White, Director, Global Biostatistics
                                   Clinical Data Systems, Abbott Vascular
                Panel Members:     Andrew Mugglin, Research Associate Professor,
                                   University of Minnesota
                                   A. James O’Malley, Associate Professor of
                                   Statistics, Department of Health Care Policy,
                                   Harvard Medical School
                                   Telba Irony, Chief, General and Surgical
                                   Devices Branch, Division of Biostatistics, CDRH,

12:30 – 2:00    LUNCH
2:00 – 3:20   Multiplicity
              Session planners:      Shiowjen Lee, Mathematical Statistician, CDRH,
                                     Michael Lu, Edwards Lifesciences
              Medical device clinical trials often consider multiple endpoints to assess
              the safety and efficacy of a given treatment. In exchange for distinct
              advantages, however, trials with multiple endpoints also pose
              challenges, and interpretation of the results must be considered

2:00 – 2:30   Rectifying FDA and Bayesian Views on Adjustment for Multiplicity
              Gene Pennello, Mathematical Statistician, CDRH, FDA
              Although frequentist properties of Bayesian designs are not routinely
              evaluated in academic research, FDA can request that the Type I error
              rate be assessed for proposed Bayesian trials, especially those involving
              multiple testing. Justifiable under the Agency’s mandate to obtain valid
              evidence of safety and effectiveness, strict control of the overall Type I
              error rate can nonetheless negate the advantage of using prior
              information through Bayesian analysis. How can one relax the Type I
              error rate while maintaining required standards of evidence?

2:30 – 3:00   Control of Type I Error and the Correlation of Multiple Endpoints in
              a Medical Device Trial
              Kevin Najarian, Boston Scientific
              Multiple univariate analyses can inflate the Type I error rate,
              necessitating adjustments of the observed p-values. In addition to
              common techniques for multiplicity adjustments, some others use
              correlation information and consider the homogeneity of treatment
              effects to determine the degree of adjustment necessary. An analysis of
              real life simulated medical device data is considered to assess
              techniques for multiplicity with added regard to the relationships among

3:00 – 3:20   Multiplicity Panel Discussion
              Panelists:     Gene Pennello, Mathematical Statistician, CDRH, FDA
                             Shiowjen Lee, Mathematical Statistician, CDRH,
                             Zengri Wang, Medtronic
                             Andrew Mugglin, Research Associate Professor,
                             University of Minnesota

3:20 – 3:40   BREAK

3:40 – 5:00   Session: Issues with Missing Data
              Session planners:       Jianxiong (George) Chu, Mathematical
                                      Statistician, CDRH, FDA
                                      John Evans, Senior Biostatistics Manager,
                                      Boston Scientific
              Missing data is a common problem in clinical studies, which causes the
              usual statistical analysis of complete or all available data to be subject to
              potential biases. However, there is no universally applicable best
              method for handling missing data. In this session, speakers/panelists will
              discuss theoretical considerations, technical issues and case studies
              with an emphasis on the importance of sensitivity analysis to assess the
              impact of missing data on statistical inference and interpretation under
              different scenarios of assumptions.

3:40 – 4:00   Missing Data: An Overview
              Michael Kenward, Professor of Biostatistics, London School of Hygiene
              & Tropical Medicine
              In this introduction and overview to the statistical problem of missing
              data, the nature and cases of missingness will be considered in a trial
              setting, and Rubin’s framework for missing value mechanisms will be
              introduced, together with the implications for subsequent analysis. Both
              ad hoc (e.g. completers, last observation carried forward) and
              statistically principled (e.g. likelihood, multiple imputation) approaches to
              analysis will be discussed. The inherent ambiguity of statistical analyses
              for incomplete data will be stressed throughout, and the important role of
              sensitivity analysis considered.

4:00 – 4:20   Case Study: Sensitivity Analysis in DES Clinical Trials from the
              FDA Reviewer’s Perspective
              Xu (Sherry) Yan, Mathematical Statistician, CDRH, FDA
              The impact of missing data has always been a major concern in the
              interpretation of clinical trial results. Procedures of statistical inference
              for handling missing data, such as multiple imputation, typically require
              the specification of missing data mechanisms for the implementation.
              When the missing data mechanism is unknown, sensitivity analyses may
              provide helpful information. This presentation addresses several
              sensitivity analyses, such as worst case scenario analysis and tipping-
              point analysis that were performed for two drug-eluting stent trials.

4:20 – 4:40   Incomplete Data in Clinical Studies: Analysis & Sensitivity Analysis
              Geert Molenberghs, Professor of Biostatistics, Hasselt University,
              Diepenbeek, & Katholieke Universiteit, Leuven, Belgium
              Incomplete data are common, as are modeling and other data analysis
              tools for such data, with ever increasing complexities. Model
              assumptions may directly affect inferences and substantive conclusions
              without being fully verifiable from the observed data, and it is therefore
              essential to recognize sensitivities caused by these assumptions before
              proceeding with the analysis. In particular, one cannot formally
              distinguish between MAR and MNAR mechanisms. Theoretical
              considerations and practical illustrations highlight the implications for
              sensitivity analysis.

4:20 – 5:00   Panel Discussion on Missing Data
              Moderator:   Jason Roy, Research Investigator, Center for Health
                           Research, Geisinger Health System
              Panelists:   Michael Kenward, London School of Hygiene & Tropical
                                      Xu (Sherry) Yan, CDRH, FDA
                                      Geert Molenberghs, Hasselt University, Belgium
                                      Recai Yucel, Assistant Professor of Biostatistics,
                                      Department of Epidemiology & Biostatistics, School of
                                      Public Health, SUNY-Albany

5:00                  ADJOURNMENT

5:00 – 6:30           RECEPTION

April 17, Thursday

8:30 – 9:00          CONTINENTAL BREAKFAST

9:00 – 10:20         Session: Trial Design – No Controls Available or Imperfect Controls
                     Session Planners:       Lilly Yue, Chief, Cardiovascular & Ophthalmic
                                             Devices Branch, Division of Biostatistics, CDRH,
                                             Brandon Sparks, Medtronic
                     Randomized, well-controlled, double-blind clinical trials have been viewed
                     as the gold standard in the evaluation of medical products, but medical
                     device clinical studies often depart from this ideal paradigm for ethical or
                     practical reasons. In studies with imperfect or no controls, the resulting
                     statistical inferences may carry a lower level of scientific assurance. This
                     session provides an overview, illustrated with case studies, of non-
                     randomized or imperfectly controlled medical device clinical trials,
                     considering design and statistical analysis from industry and regulatory

9:00 – 9:20          Unique Challenges Regarding Control Groups for Neurostimulation
                     Steven Broste, Director of Biostatistics & Data Management, Medtronic
                     Implantable neurostimulation systems are in clinical studies or
                     development for several new indications or anatomical regions. The
                     mechanism of action for these novel, invasive treatments is unclear, yet
                     the optimal blinded, controlled studies may not be possible, whether for
                     ethical reasons or because physical effects of treatment make blinding
                     impossible. This presentation elaborates on the challenges in such a
                     study, and describes attempts to deal with these issues.

9:20 – 9:40          Case Study of a Trial Design with Imperfect Control: Industry View
                     Bryan Randall, Senior Manager, Biostatistics & Clinical Science, Boston
                     This case study features a proposed trial design with imperfect control to
                     establish the safety and efficacy of carotid stenting compared to carotid
                     surgery in high-risk patients. The essential design feature is a concurrent,
                     non-randomized surgery control arm that poses challenges to minimizing
                     bias from several sources, including operator selection, patient selection,
                and outcome evaluation. How can the statistical design and analysis and
                trial execution plans mitigate the impact of these potential biases?

9:40 – 10:00    Trial Design with Imperfect Controls or No Control Available: a
                Regulatory Perspective
                Lilly Yue, Chief, Cardiovascular & Ophthalmic Devices Branch, Division of
                Biostatistics, CDRH, FDA
                Ethical or practical considerations do not always permit randomized, well-
                controlled, double-blind clinical trials for the evaluation of medical
                products. How does a regulatory agency approach the challenge of
                imperfect or no controls?

10:00 – 10:20   Panel Discussion on No Controls Available or Imperfect Controls
                Moderator:   Brandon Sparks, Medtronic

                Panelists:    Steven Broste, Director of Biostatistics & Data
                              Management, Medtronic Neurostimulation
                              Bryan Randall, Senior Manager, Biostatistics & Clinical
                              Science, Boston Scientific
                              Lilly Yue, Chief, Cardiovascular & Ophthalmic Devices
                              Branch, CDRH, FDA
                              Lei Peng, Abbott Cardiovascular

10:20 – 10:40   BREAK

10:40 – 12:00   Session: Data Poolability
                Session Planners:       Chul H. Ahn, Mathematical
                                        Statistician, CDRH, FDA
                                        Dennis W. King, President & CEO, STATKING
                Poolability comprises a heterogeneous set of problems that need to be
                addressed on a case by case basis, depending, among other things, on
                whether the groups in question are investigational sites, geographical
                regions, clinical studies (or substudies), patient populations, device
                models, and so on. This session will consist of presentations by both FDA
                and the industry regarding their perspective on this issue followed by the
                discussion by the panelist.

10:40 – 11:00   Data Pooling in Medical Device Trials
                Chul H. Ahn, Mathematical Statistician, CDRH, FDA
                One often encounters data poolability problems when trying to combine
                data from different groups to obtain an overall estimate of an outcome
                variable. Different groups may mean different centers, studies, patient
                populations, device models, and so on. This presentation addresses
                some of the issues in data pooling with examples from recent clinical
11:00 – 11:20   Medical Device Case Studies in Poolability
                John C. Evans, Senior Biostatistics Manager, Boston Scientific
                Medical device poolability case studies will be presented from 1- and 2-
                arm studies. Poolability of patients from different centers assumes that all
                sites follow identical protocols. To determine poolability, the comparability
                of baseline variables, demographic characteristics, and safety and efficacy
                endpoints is assessed across sites. In single-arm studies, the effect of
                study center is investigated to determine the poolability of the primary
                endpoint from different institutions. For two- or more arm studies, the
                effect of interaction between treatments and sites is considered to
                determine the poolability of treatment differences in the primary endpoint
                across sites.

11:20 – 11:40   Pooling Non-Poolable Clinical Data: So What
                Jeng Mah, Principal Biostatistician, American Medical Systems
                The ability of statistics to examine underlying consistent states is based on
                the assumption that random samples from each state are independently
                identically distributed (IID). Acceptance of this assumption is not trivial, as
                evidenced by the stringent requirements for RCTs and the difficulty of
                developing complicated covariate analyses to accommodate lesser clinical
                data. Homogeneity of device clinical data, even from well conducted
                RCTs, may require closer scrutiny than with pharmaceuticals, because
                observed effects of medical devices show a combination of patient
                characteristics, device design, and implanter skill. Device studies often
                prospectively balance treatment assignments within centers and
                retrospectively try to verify data poolability. This study uses simulation to
                evaluate the cost of neglecting center effects in terms of false positive
                rate, assay sensitivity, and degree of bias in estimating treatment effects.

11:40 – 12:00   Poolability Panel Discussion
                Moderator:    Dennis W. King, President & CEO, STATKING
                Panelists:    Chul H. Ahn, Mathematical Statistician, CDRH, FDA
                              John C. Evans, Senior Biostatistics Manager, Boston
                              Jeng Mah, Principal Biostatistician, American Medical

12:00 – 1:00    LUNCH

                                        Two Simultaneous Tracks

1:00 – 5:00     Track A ;
                Statistical Issues in Diagnostics and Imaging
                Session Planners:     Lakshmi Vishnuvajjala, Chief, Diagnostic Device
                                      Branch, Division of Biostatistics, CDRH, FDA
                                      Betty Stephenson, Becton Dickinson
                                      Vicki Petrides, Statistician Section Head, Abbott
                                      Pat Meyers, Abbott Diagnostics
              Statistical Issues in Diagnostics and Imaging Studies. Diagnostic studies
              present unique challenges in the medical device area. For laboratory tests
              which are regulated by the Office of In Vitro Diagnostics at CDRH,
              randomized studies are not the rule since samples can be split in most
              cases to be tested by both the old and the new tests. But certain types of
              bias are much more common and need to be guarded against. Imaging
              studies such as mammography, ultrasound and MRI are greatly affected
              by the case mix and reader variability.

1:00 – 1:10   Diagnostic Devices: A Brief Overview
              Lakshmi Vishnuvajjala, Chief, Diagnostic Device Branch, Division of
              Biostatistics, CDRH, FDA
              There are many challenges that must be addressed in the development of
              diagnostic and imaging products. This session will explore some of these
              concerns, including co-development of biomarkers and assays, special
              problems encountered in diagnostic imaging studies, and quality control

1:10 – 1:35   Statistical Design Challenges in Phase III Biomarker-based Clinical
              Dan Sargent, Statistician, Mayo Clinic (Biomarkers)
              This presentation will focus on issues to consider when designing a clinical
              trial assessing the usefulness of a predictive marker. We present two
              classes of clinical trial designs, the 'selection design', which enrolls only a
              specific target population, and an unselected but prospectively specified
              marker-based design. We also discuss the relative merits of prospectively
              enrolled clinical trials versus prospectively specified analyses on data from
              previous clinical trials.

1:35 – 2:00   Design Issues in Radiological Image Reader Studies
              Tom Gwise, Imaging Studies, CDRH, FDA
              Medical devices reaching a certain risk threshold must be evaluated as to
              their safety and efficacy before they can be legally marketed in the United
              States. Evaluation of radiological imaging devices poses certain
              complications because some level of subjective reader interpretation is
              necessary to utilize them. Many sources of bias can affect such studies.
              This presentation will discuss sources of bias in reader studies and steps
              that can be taken to mitigate them.

2:00 – 2:25   Considerations When Building Microarray-based Classifiers and
              Predictors of Clinical Endpoints: Perspectives from the MAQC II
              Wendell Jones, Senior Director of Statistics and Bioinformatics,
              Expression Analysis
              The MicroArray Quality Control consortium is currently focused on the
              ability of microarrays to classify or predict outcomes for samples and
              subjects in clinical and toxicogenomic studies. There are particular
              aspects of microarray data including the magnitude of factors available,
              quality issues, and technical effects which may have large ramifications to
              the success of a classifier/predictor.
2:25 – 2:45   Question and Discussion

2:45 – 3:00   BREAK

3:45 – 5:00   Expert Panel on Guidance for Laboratory Tests
              Moderator:          Betty Stephenson, Becton Dickinson

              Speakers:             Jan Krouwer, Krouwer Consulting, (CLSI
                                    Kyungsook Kim, CDRH, FDA (CLIAI Guidance)
                                    Marina Kondratovich, CDRH, FDA,(Migration
                                    Guidance, FDA perspective)
                                    Angel DeGuzman, Abbott Diagnostics, (Migration
                                    Guidance, industry perspective)

              There are multiple aspects of diagnostic products that need to be
              evaluated by manufacturers, the FDA, and laboratories. To assist in
              evaluating some of these characteristics, guidance documents have been
              published through the joint efforts of interested parties. This session will
              provide a forum during which these guidance documents will be

1:00 – 3:00   Track B:
              Postmarket Statistical Issues
              Session Planners:       Greg Campbell, Director, Division of Biostatistics,
                                      CDRH, FDA
                                      An Liu, Principal Statistician, Medtronic
                                      Cardiac Rhythm Disease Management
              This session will concentrate on statistical issues that arise in the post-
              market arena. It will include statistical concerns arising in condition of
              approval studies, as well as other post-approval studies to enable a
              change in the indication for use for a marketed device. Another
              postmarket issue in the session is the use of statistical methodologies in
              the analysis of data from FDA’s Medical Device Reporting (MDR) system.

1:00 – 1:30   General Statistical Methodologies in the Analysis of Spontaneously
              Reporting Post-Marketing Medical Device Reports (MDRs) and
              Design for Post-Approval Studies
              Chang S. Lao, Senior Mathematical Statistician, Division of Biostatistics,
              CDRH, FDA
              The sources of Medical Device Reports (MDRs) and related required
              regulations are introduced. Attention is then focused on the statistical
              methods for MDR spike detection based on modeling and non-modeling
              approaches as well as non-parametric regression smoothing for
              exploratory analysis and non-parametric trend analysis, with two real data
              examples. Related statistical issues for analysis of spontaneously
              reporting MDRs will be briefly discussed. Finally, a few real examples for
              Post-approval studies and general statistical issues for study design of
              Post-approval studies will be described.
1:30 – 2:00          Using Seattle Angina Questionnaire (SAQ) to Evaluate Patient
                     Reported Outcome in a Post-marketing Setting
                     Author: Vivian Mao, Clinical Research Manager, Abbot Vascular
                     Presenter: Roseann White, Director, Global Biostatistics Clinical Data
                     Systems, Abbott Vascular
                     Patient-reported quality of life outcome instrument can be used to
                     measure the impact of an intervention on patient health status in medical
                     device clinical trials. Using a valid and reliable instrument to
                     systematically evaluate patient’s perspective on the impact of drug eluting
                     stents may provide valuable information that can be lost through a
                     clinicians’ interpretation. This talk discusses the usage of the Seattle
                     Angina Questionnaire (SAQ) to evaluate patient health status in patients
                     receiving XIENCE™ V Everolimus Eluting Coronary Stent System
                     (EECSS) in a post-marketing environment and its statistical

2:00 – 2:30          Statistical Challenges in the Post-Approval Registry for Drug Eluting
                     Peter S. Lam, Director, Biostatistics & Clinical Sciences, Boston Scientific
                     The statistical challenges in the mandatory post-approval registry for Drug
                     Eluting Stents (DES) to address the safety endpoints of death, myocardial
                     infarction, and stent thrombosis will be presented using a case example.
                     These endpoints were not sufficiently powered in the pre-approval trials
                     due to their low incidence rates. Additional challenges to attempt to
                     investigate the optimal duration of dual anti-platelet therapy for DES
                     patients will also be explored in this presentation.

2:30 – 2:45          BREAK

2:45 – 3:15          A Bayesian Design Applied to an Endovascular Post-Market Study
                     Yuqing Dai, Principal Statistician, Medtronic Cardiovascular
                     Feng Tang, Principal Statistician, Medtronic Cardiovascular
                     Often prior knowledge, which contains valuable information, on an existing
                     device is available when we conduct a new post-market study.
                     Appropriately using such prior information could lead to sample size
                     reduction, which is very desirable when the accruement of the sufficient
                     number of patients could be challenging due to the relatively low incidence
                     of a rare outcome. In this talk, we use an endovascular trial as an
                     example, illustrating a Bayesian design on a post-market study. Also, we
                     will study the relationship between the different operation characteristics,
                     the decision criteria and the sample size.

5:00                 ADJOURNMENT

                                        Important Notice
The information provided in this course represents the personal opinions of the instructors and
does not necessarily represent the opinions of AdvaMed staff. Companies relying on the
information do so at their own risk and assume the risk of any subsequent liability that results
from relying on the information. The information does not constitute legal advice.

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