Online Program
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Wed, Sep 12 |
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SC1 Recent Adaptive Designs in Phase 2 and Phase 3: Theory and Implementation |
09/12/12 |
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Instructor(s): Paul Gallo, Novartis; Anastasia Ivanova, University of North Carolina, Chapel Hill; Eva R Miller, ICON Clinical Research |
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This course will offer recent case studies of well designed adaptive trials in learn and confirm clinical studies to help participants better understand motivations and justifications for use of adaptive designs relative to traditional trials. Theoretical frameworks, additional logistical challenges for implementation and special challenges for DMCs and decision-making processes will be emphasized. We will review multi-stage adaptive dose-finding methods for Phase 2 studies and introduce a novel two-stage method. The emphasis will be on protocol development and simulation studies to support the justification for use of adaptive design. Sample size re-estimation methods will be also discussed. Basic examples will illustrate the utility, advantages and limitations of these methods. Since DMC issues provide special challenges within adaptive trials, the course will also emphasize sound research practices related to DMCs in adaptive trial designs. |
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SC2 Utility-Based Clinical Trial Design and Analysis |
09/12/12 |
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Instructor(s): Peter Francis Thall, University of Texas M.D. Anderson Cancer Center |
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This half-day short course will present clinical trial methods based on numerical utilities. The overarching theme is the use of elicited utilities or scores of multi-dimensional patient outcomes or parameters as a basis for treatment evaluation. The basic idea is to use utilities to obtain a one dimensional criterion for decision making or estimation in a way that reflects the relative importance of two or more outcomes. As time permits, the illustrative examples will include Bayesian, frequentist, and Bayesian-frequentist hybrid methods, including (1) phase I dose-finding based on total toxicity burden; (2) phase I-II dose-finding based on joint utilities of ordinal efficacy and toxicity outcomes; (3) use of adaptive randomization in utility-based dose-finding trials to improve reliability; (4) a phase II-III select-and-test design based on (efficacy, toxicity) parameter pairs; (5) jointly optimizing the concentration and initial bolus of a drug delivered by continuous infusion; (6) evaluation of multi-stage treatment regimes using elicited scores of joint ordinal (response, toxicity) outcomes. |
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SC3 A Sensitivity Analysis Paradigm for Randomized Studies with Missing Data |
09/12/12 |
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Instructor(s): Daniel Scharfstein, Johns Hopkins Bloomberg School of Public Health |
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Description: In this short course, we will present a sensitivity analysis paradigm for analyzing and reporting randomized trials with potentially informative missing data. The paradigm will be illustrated with case studies. |
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SC4 Classical, Adaptive and Bayesian Clinical Trial Simulations : Concepts, Execution and Implementation |
09/12/12 |
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Instructor(s): Mark Chang, AMAG Pharmaceuticals / Boston University; Gheorghe Doros, Boston University School of Public Health; Sandeep M Menon, Pfizer Inc / Boston University |
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Clinical trial simulations (CTS) have evolved over the past two decades from a simple instructive game to "full" simulation models. The need to make drug development more efficient and informative have advanced considerably the use of simulation of clinical trials in pharmaceutical product development over the past decade. This short course will provide an overview of CTS and will cover Classical CTS, Adaptive CTS including Group Sequential Design, Sample Size Re-estimation, Conditional Power and Error Estimation, Multiple Arm Dose-Response Models and Biomarker Adaptive Design. The course will also briefly cover CTS for Bayesian classical and group sequential designs. The topics will extensively discuss translation of a clinical trial problem into a CTS problem, its execution and interpretation. Hands on in-class simulations and exercises will be conducted. Attendees are encouraged to bring their laptops in order to do in-class demos in SAS or R (SAS is preferred) along with the instructors. Bayesian examples will be done in R and OpenBuGS. |
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SC5 Statistical Evaluation of Diagnostic Performance Using ROC Analysis |
09/12/12 |
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Instructor(s): Gregory Campbell, Food and Drug Administration; Alicia Y Toledano, Statistics Collaborative, Inc.; Kelly H. Zou, Pfizer Inc |
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Classification methods play an important role in accuracy and reliability analysis. Advanced screening and diagnostic modalities have become commonplace and have posed new challenges for analysis, modeling, and interpretation. We review the ROC methodology for estimating and comparing performance indices using diagnostic classification tests. Associated measures derived from ROC analysis and extensions to the traditional classification methods to a number of state-of-art applications are presented. Monotone transformation models are chosen to improve goodness-of-fit of the modeling approaches. Likelihood-based algorithms for estimating an ROC curve are discussed, along with the associated characteristics for univariate and multivariate data. Methods for pooling and combination of biomarkers, Bayesian hierarchical transformation models, and sequential analysis are elaborated on. A thorough understanding and validation of complex biomarkers and high-dimensional classification data may be achieved. |
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SC6 Meta-Analysis with Multivariate Data |
09/12/12 |
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Instructor(s): Christopher Schmid, Brown University |
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Description: This workshop will describe Bayesian and frequentist techniques for statistical analysis of multivariate meta-analytic data using problems for which standard meta-analytic methods are suboptimal. Models for meta-regression using study-level and patient-level covariates, multiple outcomes, multiple treatments and multiple follow-ups will be covered with emphasis on Bayesian analyses. Familiarity with basic concepts in meta-analysis is needed, but the emphasis will be on practical applications and issues in collecting, extracting, analyzing and summarizing multivariate data. These include heterogeneity, missing and unbalanced data, direct and indirect comparisons, correlation, heterogeneity and network incoherency. Knowledge of the motivation for meta-analysis as well as basic statistical models for combining data of different types will be assumed. |
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Thu, Sep 13 |
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GS1 Statistical Issues and Challenges in Post-Licensure Safety Surveillance and Treatment Effect Assessment |
09/13/12 |
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Organizer(s): Vijay Chauhan, CEO, Alpha Stats Inc; Yun Lu, FDA/CBER; Guoying Sun, FDA/CBER |
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Chair(s): Nancy Dreyer, Quintiles Outcome |
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Post-licensure observational studies are becoming increasingly important for performing post-marketing safety surveillance as well as for conducting post-approval treatment effect assessment, because pre-licensure clinical trials may not detect rare drug/vaccine related adverse events and heterogeneity of treatment effect (HTE) due to small sample size and other study limitations. Dr. Tom MaCurdy from Stanford University will discuss adaptations of sequential testing methods to account for delays in data accrual encountered in near real-time safety surveillance. Dr. Ravi Varadhan from Johns Hopkins University will then discuss the methodological issues and challenges in the assessment of HTE in observational studies, with an emphasis on post-marketing surveillance studies. Dr. Priscilla Velentgas from Quintiles Outcome will illustrate application of HTE assessment using RiGOR (a prospective, observational cohort study designed to compare the effectiveness of laser surgery, other procedures, and medications for treatment of glaucoma) as a case study. The speakers will then join Dr. David Martin from the FDA for a panel discussion. |
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Updating Sequential Probability Ratio Test for Real-Time Surveillance of Vaccine/Drug Safety
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Heterogeneity of Treatment Effects in Observational Studies and in Post-Marketing Surveillance
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Investigating Heterogeneity of Treatment Effect Using Propensity Scores in a Prospective Observational Study of the Comparative Effectiveness of Treatments for Glaucoma
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Statistical issues and challenges in post-licensure safety surveillance and treatment effect assessment: Panel Discussion
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GS2 Statistical Considerations in Subgroup Identification and Analysis in Randomized Clinical Trials |
09/13/12 |
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Organizer(s): Mohamed Alosh, FDA; Richard C. Zink, JMP Life Sciences / SAS Institute, Inc. |
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Chair(s): Shiowjen Lee, US Food and Drug Administration, CBER; David Li, Pfizer, Inc. |
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Subgroup analyses are commonly conducted in clinical trials with the objective of learning about differential treatment effects across subgroups. Typical subgroups are defined by pre-specified clinical or biological factors known or suspected to affect the disease. For developing personalized medicines using subgroup analyses, new prospective approaches to identifying the specificity of treatment effects are required. In developing molecularly targeted treatments for biologically heterogeneous populations, variation in treatment effects among subsets is expected but traditional post-hoc subset analysis without control for multiplicity cannot serve as a reliable basis for marketing approval of personalized medicine. This session will address the following issues: (i) subgroup identification, (ii) discerning subgroup chance–findings from true heterogeneity, and (iii) designing clinical trials to establish an efficacy claim for the total population or a targeted subgroup when the objective for the total population is not met. Speakers from FDA, industry and academia will be invited to discuss the issues. |
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Statistical Issues in Subgroup Analyses of Clinical Trials
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Rigorous Biomarker/Subgroup Identification to Enable Development of Tailored Therapeutics
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Multiplicity Concerns in Subgroup Analysis
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Panel Discussion
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Roundtable Topic: Adaptive and Other Study Designs |
09/13/12 |
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TL1: Practical Issues with Non-Randomized study designs
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TL2: Logistics and Implementation of Adaptive Trial Designs
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TL3: Sample Size Re-estimation: Concepts and Applications
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TL4: Innovated Design for First Time in Human Study
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TL5: Randomized Withdrawal Design
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Roundtable Topic: Bayesian Methods and Designs |
09/13/12 |
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TL6: Evaluation of Type 1 Error in Bayesian Medical Device Trials with Informative Priors
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TL7: Bayesian Application in Registration Trials with Confirmatory Secondary Endpoints
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TL8: Bayesian Method, Adaptive Design and Enhanced Quantitative Decision in Early Drug Development
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Roundtable Topic: Biomarkers/Biosimilars |
09/13/12 |
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TL9: Biomarker Qualification in Drug Safety
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TL10: Evaluation of the Risk Prediction Performance of Biomarkers and Tests
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TL11: Selection and Validation of Biomarkers and Surrogate Endpoints
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TL12: Statistical Issues in the Approval of Biosimilars
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Roundtable Topic: Collaboration/Guidelines |
09/13/12 |
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TL13: Awareness and Implementation of CDISC Standards
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TL14: PhD, MS Statisticians – Roles and Responsibilities in the Pharmaceutical Industry
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TL15: Application of EMA Bioequivalence Guidelines (2010) in a Global Setting
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TL16: New FDA cUTI Draft Guidance and Design Implications
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Roundtable Topic: Comparative Effectiveness/PROs |
09/13/12 |
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TL17: How can Quantitative Methods and Tools be Applied by Industry & Regulators to Determine a Medicine’s Value to Payers, Providers and Patients
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TL18: Industry Perspective on Practical Issues of PROs
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Roundtable Topic: Diagnostics/Devices |
09/13/12 |
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TL19: Statistical Issues in Companion Diagnostics
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TL20: Statistical Design and Analysis Issues for Cardiovascular Medical Device Studies
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TL21: Evaluating Performance Measures where Patients Contribute a Measure in a Temporal Sequence
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TL22: Methods for Developing and Validating Diagnostic Tests
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TL23: Design Considerations for Pivotal Clinical Investigations for Medical Devices
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TL24: Diagnostic Imaging Studies
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TL25: Missing Data Due to the Lack of a Reference Standard in Evaluation of Diagnostic Medical Devices
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TL26: Clinical Trials for Devices with Aesthetic Indications
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Roundtable Topic: Drug Development |
09/13/12 |
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TL27: Innovation Session - Re-designing the Pharmaceutical R&D Process
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TL28: Bridging to Bridges in Vaccine Development: Managing the Drift in Multi-Serotype Vaccines
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TL29: Challenges & Opportunities in Small to Mid-size Pharmaceutical Companies
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TL30: First-Time-In-Human Trials - Everything but the kitchen sink?
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Roundtable Topic: Futility |
09/13/12 |
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TL31: Role of Futility Analysis in an Unblinded Interim Analysis
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TL32: Futility Analyses
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Roundtable Topic: Methodology |
09/13/12 |
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TL33: Practices on Benefit-risk Assessment
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TL34: Missing Data: Bridging the Gap between Industry and Academia
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TL35: Statistical Issues in Oncology Clinical Trials: Progression Free Survival and Overall Survival
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TL36: Agreement Assessment among Medical Devices or Raters
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TL37: The Actual Practice of Randomization Management
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TL38: Assessment of the Dose Proportionality of PK Parameters in SRD or MRD Trials and the Evaluation of the Steady State – What is the Common Practice
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TL39: Analysis of Change from Baseline Data in the Presence of Covariate-by-treatment Interaction
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TL40: Meta Analysis based on Post-hoc Selected Subgroups in Evaluating overall Treatment Effect
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TL41: Experiences with Zero-inflated Poisson or Negative Binomial Models in Clinical Trials or other types of Studies for Regulatory Submission Purposes
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TL42: What do we do when the Sites are not Poolable?
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TL43: Covariate Adjustment: Should study center be included?
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TL44: Statistical Modeling to Evaluate Long-Term Persistence
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TL45: Mediators and Moderators in Randomized Clinical Trials
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TL46: Issues in Clinical Trials with a Time Lag between Randomization and Initiation of Treatment
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TL47: Precision Study for a Qualitative Assay
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TL48: Sensitivity Analyses for Progression-free Survival in Supporting Labeling Claim
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Roundtable Topic: Noninferiority |
09/13/12 |
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TL49: Challenges in the Designs of Non-Inferiority and Equivalence Trials with Clinical Endpoints
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TL50: Considerations in Defining the Primary Analysis Population for Non-Inferiority Studies
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Roundtable Topic: Propensity Scores |
09/13/12 |
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TL51: Subgroup Matching by Propensity Score in Randomized Trials
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TL52: Safety Assessment using Propensity Score Methods in Observational Database Cohort Studies
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Roundtable Topic: Safety |
09/13/12 |
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TL53: QT/QTc Evaluation in Early Development Studies
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TL54: Analysis of Safety Events of Interest in Placebo-Controlled Clinical Trials
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Roundtable Topic: Veterinary |
09/13/12 |
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This roundtable will be an opportunity to discuss using the R programming language to support research in the field of Veterinary Medicine. The discussion will focus on opportunities and obstacles related to using R, including validation and use of packages, graphics capabilities, system implementation and maintenance, and use to support submissions to regulatory agencies that require Title 21 CFR Part 11 compliance. Expert and novice R users are welcome. Please come and share your experiences. |
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TL55: Using R in Veterinary Medicine Research
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PS1a High Placebo Response - Measureable, Observable, Avoidable? |
09/13/12 |
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Organizer(s): Cristiana Gassmann-Mayer, Johnson & Johnson; Xiaohong Huang, Sanofi-Aventis; Jack Zhou, FDA CDRH |
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Chair(s): Sue-Jane Wang, US Food and Drug Administration |
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Placebo response and signal detection is a research area that is gaining intensifying interest. When a trial fails to demonstrate a study drug’s efficacy, high placebo response is commonly observed, especially in CNS trials where the failure rate is often about 50%. The statistical and operational approaches to manage investigators or patients expectations, avoid treatment effect bias, and improve signal detection have intrigued researchers in the pharmaceutical and regulatory environments. The only overall agreement is that no single strategy can address the multiplicity of factors and issues responsible for high placebo responses. To tackle the problem of high placebo response, this session will provide a platform for statisticians to share insights and to discuss potential solutions including innovative designs that can be implemented in clinical trials. |
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A two-way enriched clinical trial design merging placebo lead-in and randomized withdrawal
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Single stage design with or without placebo lead-in or a two-stage design, which approach to use in your trial with high placebo response?
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Evaluation of Performance of Some Enrichment Designs Dealing with High Placebo Response in Psychiatric Clinical Trials
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Addressing the High Placebo Response in Neuroscience Clinical Trials: Doubly-Randomized Delayed Start Design to Increase Efficiency and Likelihood of Success
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PS1b Adaptive Designs: Are they innovative and fulfilling their promise? |
09/13/12 |
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Organizer(s): Bruce Binkowitz, Merck and Co., Inc.; Richard M Kotz, FDA/CDRH; Deborah R Shapiro, Merck & Co. |
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Chair(s): Matthew Hoblin, Quintiles |
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Innovation is mantra at FDA now, and Innovation in Clinical Trial Design is one aspect of this. To date, two of the most innovative clinical trials conducted are the BATTLE trial for lung cancer and the I-SPY2 for breast cancer. These Bayesian Adaptive trials utilize several types of adaptation, including randomization and biomarker selection, to help determine drug selection tailored to appropriate patient populations. Adaptive methods and operations used in these trials may prove useful in drug and device trials in identifying good biomarkers and appropriate patient populations thus increasing the likelihood of trial success. The utility of adaptive trials and the ability to create innovative adaptive study designs and operationalize those designs for the drug/device industry and within the FDA regulatory framework will be discussed by a panel of FDA and Industry Representatives. |
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Bayesian Adaptive Designs for Efficient Targeted Agent Development
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Matching Therapy to Tumor Biomarker Signatures: The Case of I-SPY 2 in Breast Cancer
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Discussant(s): Olga Marchenko, Quintiles; Jenny Zhang, FDA/CDER |
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PS1c Responder definitions and non-inferiority margins for patient reported outcomes |
09/13/12 |
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Organizer(s): Laura Lee Johnson, NIH; JoAnn Shapiro, Bayer Pharmaceuticals |
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Chair(s): JoAnn Shapiro, Bayer Pharmaceuticals |
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Regardless of whether the primary endpoint for a clinical trial is based on individual responses to treatment or the group response, an individual responder definition (i.e., the individual patient outcome change over a predetermined time period that should be interpreted as a treatment benefit) many times is based only on clinical considerations, e.g. a drop of x mm in blood pressure. The first speaker in this session will discuss a method he used to determine a responder and a MCID definition empirically by relating the treatment effect, e.g., a change in the number of hot flushes for menopausal symptoms, to the patient‘s satisfaction with the treatment. Additional topics covered will be potential use of such definitions when attempting to define the non-inferiority margin for an active controlled trial, patient and group level differences, regulatory acceptance, and difficulties that may arise when attempting to use these methods in particular with patient observed outcome measures that involve more than one item. |
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Statistical derivation of a responder definition for the reduction of hot flushes
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Scoring Systems for Person Reported Outcome Measurements
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Discussant(s): Joseph Cappelleri, Pfizer Inc; Lisa Kammerman, FDA |
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PS1d Statistical Issues in Developing Companion Diagnostic Tests |
09/13/12 |
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Organizer(s): Jessica Hu, FDA/CBER; Songbai Wang, Ortho Clinical Diagnostics /J&J |
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Chair(s): Estelle Russek-Cohen, FDA CBER/OBE Division of Biostatistics |
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Companion diagnostic tests have been playing a critical role in personalized medicine. As utilities and accuracy of companion diagnostic tests are associated with efficacy of new drugs in development, assay validation and performance evaluation of a companion diagnostic test could be challenging. This session will discuss statistical issues such as choice of performance endpoint associated with drug effectiveness and determination of assay algorithm during clinical studies. This session may be interesting to statisticians in fields such as pharmaceuticals, diagnostic medicine and methodology research. |
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Statistical Evaluation of Companion Diagnostic Tests Under Regulatory Review
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Combing biomarkers to improve diagnostic accuracy
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The Many Pharmaceutical Statistician Roles and Activities Required to support Companion Diagnostic Development
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PS1e Safety Monitoring and Evaluation |
09/13/12 |
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Organizer(s): Qi Jiang, Amgen Inc.; Daphne Lin, FDA/CDER |
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Chair(s): Ken Gerald, Westat |
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Drug safety has always been important, but it’s taken on increasing criticality and scrutiny in recent years. Two fundamental factors make the evaluation of safety particularly challenging: important safety problems can be unexpected, and they can be rare. This session will highlight these and other challenges related to safety monitoring and evaluation and recommend some best practice to address key safety issues that arise in the development of biologics and drug. |
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Absolute vs Relative Measures of Harm
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Continuous Safety Monitoring for Randomized Controlled Clinical Trials with Blinded Treatment Information
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Statistical Challenges and Considerations in Drug Safety Evaluation
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PS2a Operational Considerations for Adaptive Trials |
09/13/12 |
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Organizer(s): Susan Huyck, Merck; Lingyun Liu, Cytel Inc.; Jason Schroeder, FDA |
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Chair(s): Susan Huyck, Merck |
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There is great potential for clinical trials designed with adaptive features to result in more efficient decision making within a drug development program. However, clinical trials with adaptive features are more complex to implement than traditional designs such as fixed-sample or group sequential. Workarounds and/or inefficiencies in adaptive design (AD) trial execution may result in human and material wastes. Further, they may result in the introduction of operational biases that may potentially negate any gains in designing an AD trial and may even render trial results not interpretable. This session will identify the challenges and the technology now being used to specifically address the demands inherent particularly with adaptive studies. New processes and online systems will be examined which are designed to preserve the integrity of the trial by securely managing access to the blinded data, supporting DMC procedures, facilitating audits, and automating analyses and reporting. The speakers will also share their recent experiences in implementing clinical trials with AD features in several areas, including clinical supply, patient enrollment management, and the use of DMC. |
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Some Practical Considerations in the Implementation of Adaptive Clinical Trials in Pharmaceutical Development Programs
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Interim Analyses Workflow Optimization and Case Studies
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Discussant(s): Paul Gallo, Novartis; Weili He, Merck & Co., Inc.; Martin Ho, FDA/CDRH |
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PS2b New methodological development on consistency assessment and other issues for multi-regional trials |
09/13/12 |
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Organizer(s): Steven Bai, FDA; Yeh-Fong Chen, FDA; Paul Gallo, Novartis |
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Chair(s): Joshua Chen, Merck and Co., Inc. |
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Multi-regional clinical trials (MRCTs) have been widely used for efficient global new drug developments. These trials present us great opportunities but also significant challenges. To address issues associated with MRCTs, tremendous effort has been made recently in methodological research. In this session, new ideas and results (beyond those discussed in previous Workshops) will be presented. They include methods for consistency assessment of treatment effects across regions based on empirical shrinkage estimator and other approaches. Computations, simulations and examples are used to demonstrate their performances. Other trial design, data analysis and result interpretation considerations for MRCTs will also be discussed. |
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Considerations for Consistency or Inconsistency Assessment in Global Trial Design
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Shrinkage Estimators for Consistency Assessment in Multi-Regional Clinical Trials
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Statistical Analysis for Multi-National Clinical Trials on Inter-variations among Regions
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PS2c Clinical trials with Safety Endpoints |
09/13/12 |
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Organizer(s): Cristiana Gassmann-Mayer, Johnson & Johnson; Qi Jiang, Amgen Inc.; Estelle Russek-Cohen, FDA CBER/OBE Division of Biostatistics |
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Chair(s): Christy Chuang-Stein, Pfizer |
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This session will provide three excellent talks on topics such as the challenges of running a safety trial including the issue of obtaining informed consent, designing large pragmatic safety trials and then, in contrast, designing smaller safety trials for treatment of rare diseases. Speakers include Janet Wittes (Statistics Collaborative), Jesse Berlin (Janssen R & D) and John Scott (FDA CBER). Talks will be followed by an open floor discussion. |
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Trials of safety: sample sizes and informed consent
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Some challenges in the analysis and interpretation of large pragmatic (and possibly “simple”) safety studies
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Small Safety Trials for Rare Diseases
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PS2d Recent Advances in the Statistics of Vaccines Research |
09/13/12 |
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Organizer(s): John Jezorwski, Sanofi Pasteur; Barbara Krasnicka, FDA |
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Chair(s): Mridul Chowdhury, FDA; Anthony Homer, Sanofi Pasteur |
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Bringing a pharmaceutical product to market as quickly as possible is always at the forefront of a pharmaceutical company business model. In aiming for that target, many factors in a clinical trial, such as the determination of an effective treatment in the most efficient manner, stopping early for inadequate patient benefit, and an adequate assessment of the safety of trial participants are the priority in the development of a product. In vaccine clinical research, there has been considerable advancement in vaccine development with regards to efficacy, safety evaluation and flexibility of trial designs. This session will combine recent technical research advancements among industry and academic statistical professionals in these critical areas which are extremely important to regulatory approval of new vaccines. Note that the topics presented during this session have significant relevance to non-vaccine products as well. |
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A Two-Stage Sequential Phase 2b Trial Design for Evaluating Vaccine Efficacy and Immune Correlates for Multiple HIV Vaccine Regimens
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Sequential generalized likelihood ratio tests for vaccine safety evaluation
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Adaptive Design Strategies for Vaccine Development
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PS2e Where we are and what's new: Statistical Review and Development of New Drugs and Devices |
09/13/12 |
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Organizer(s): Brenda Crowe, Eli Lilly and Company; Rima Izem, FDA/CDER/OB/DB4; Chengxing (Cindy) Lu, Novartis |
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FDA speakers: Lisa Lavange (CDER), Greg Campbell (CDRH) Industry speaker: Jose Pinhero This session highlights the important contributions of statisticians in the review and development of new drugs and devices over the past year (2011-2012). Speakers will share examples from advisory committee meetings where important statistical issues were discussed. In addition, speakers will present metrics (such as number of New Drug Applications or Pre-market Applications for devices reviewed/approved) on review and development as well as information on important guidances and industry working groups. |
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Statistical Review and Other Developments for New Medical Devices
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Key Current Statistical Issues in Drug Development and Review: an Industry Perspective
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Recent Experiences from CDER Advisory Committee Meetings
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PS3a Recent developments in Bayesian methods for comparative effectiveness and drug safety |
09/13/12 |
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Organizer(s): Xiao Ding, The US FDA; David I Ohlssen, Novartis Pharmaceuticals Corporation |
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Chair(s): David I Ohlssen, Novartis Pharmaceuticals Corporation |
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Motivated by the use of evidence based medicine to evaluate health technology, there has been an enormous increase in the use of quantitative techniques that allow data to be combined from a variety of sources. In a drug development setting, there have been a number of recent key works: The recommendations on the use and application of network meta-analysis that were recently presented by the ISPOR task force; From a regulatory perspective, the work of the Canadian Agency (Indirect Evidence: Indirect Treatment Comparisons in Meta-Analysis) and the UK NICE Evidence synthesis series, have recently been published; Further, the FDA also started a number of recent projects on comparative effectiveness research as part of a plan to enhance regulatory science. By drawing on examples from a drug development setting, this session aims to examine these recent advances. In particular, emphasis will be placed on the application of Bayesian evidence synthesis methods when applied to drug safety evaluation and comparative effectiveness. |
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Hierarchical Bayesian Methods for Combining Efficacy and Safety in Multiple Treatment Comparisons
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An introduction to network meta-analysis: recent advances and controversies
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Bayesian variable selection approach for the simultaneous investigation of adverse event and laboratory data
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PS3b Can adverse event data tell you more? |
09/13/12 |
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Organizer(s): ; Junshan Qiu, FDA/CVM; Wei Zhang, The U.S. FDA |
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Chair(s): Anna Nevius, FDA/CVM |
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Adverse event data are generated from a variety of types of studies such as phase I to phase IV clinical trials or equivalent types of trials for animal drugs approval in CVM. Generally, adverse event data are not analyzed in a statistically and biologically sophisticated way. However, summaries of adverse event data will only document the existence of adverse events but will not explore relationships between adverse events or look for associations between adverse events and other measured variables. Building up links between adverse events and other types of data and detecting the relationship between adverse events and physiological and biological parameters will help answer why the adverse events exist and when they exist. This procedure is especially meaningful for biomarker identification. Building up the relationship of interest sometimes is challenging because adverse events data may be in categorical and ordinal forms and very sparse. In this session, statisticians and pharmacokinetist from both industry and FDA sides will present their researches on how to overcome these challenges and have the adverse event data tell more. Potential speakers: Joseph F Boucher from pfizer, joseph.f.boucher@pfizer.com Eric Rasmussen from Amegen, hansr@amgen.com Micheal Myers from CVM, michael.myers@fda.hhs.gov |
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Power analysis for early cardiovascular signal detection
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Using safety signals to detect subpopulations: a population pharmacokinetic/pharmacodynamic mixture modeling approach
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PK/PD Model for a Biomarker of Kidney Function
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PS3c Methodologies and applications in agreement assessment |
09/13/12 |
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Organizer(s): Lori A Christman, STATKING Clinical Services; Meijuan Li, FDA/CDRH; Lakshmi Vishnuvajjala, FDA/CDRH |
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Chair(s): Caiyan Li, Baxter Healthcare |
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Agreement assessments are widely used in quantifying the differences between observations measured with two methods on the same subjects. The common theme is to assess the agreement between observations of an assay or rater (Y) and its reference counterpart values (X). In some situations, the reference values are the reference standard measurements, being both well established and widely acceptable. While in other situations, there is no established gold-standard as a reference standard to assess against. Many statistical methods and tools have been developed to assess the degree of agreement in different situations. While this is a very important topic, especially in the diagnostic medicine, it has not been given much attention in the past several workshops. This session seeks to blend both the theory and applications in agreement assessment from the views of regulatory, industry, and academia. The session plans to have three presentations, one from industry (Dr. Lawrence Lin, confirmed), one from FDA/CDRH (Dr. Shanti Gomatam, confirmed), and one from academia (Dr. Vernon Chinchilli, confirmed), followed by a panel discussion. |
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Matrix-based Concordance Correlation Coefficient
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Overview of Agreement Statistics for Continuous, Binary, and Ordinal Data
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Method comparison studies: Assessing Agreement for Medical Devices
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PS3d Statistical Review: Using, Communicating and Improving the CDISC/ADam Data Standards |
09/13/12 |
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Organizer(s): Paul DeLucca, Merck; Karen Higgins, FDA/CDER/OTS/OB/DBIII; Eva R Miller, ICON Clinical Research |
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Chair(s): Stephen Wilson, FDA/CDER/OTS/OB/DBIII |
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The FDA-CDISC partnership has the potential and promise to increase the efficiency and effectiveness of agency review and the development of new drugs and biologics. For these efforts to be successful, FDA must clearly describe the specific needs of medical and statistical reviewers that are not being adequately addressed by the content of data submissions and/or FDA infrastructure. We need to establish an effective “feed-back loop” -- given a well-defined FDA need, FDA and CDISC/ADaM can work together to develop scientifically valid and consistent solutions. Foundational to all of this work is the experience currently being gained by reviewers and sponsors with the submission and review of NDA/BLA data based on CDISC/ADaM standards and in scientific efforts. So what are we learning about analysis files that have been based on the ADaM standards? What do reviewers like and dislike? This session will provide insights into the utilization of ADaM-based standard data in review and a discussion of the CDISC ADaM team’s ongoing work to improve processes and standards. Invited speakers: Joy Mele, Nate Freimark, Behrang Vali |
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How the CDISC ADaM team is working to support the safety and efficacy of FDA review
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Coaxing ADaM data into formats useful for review by both clinical and statistical reviewers
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ADaM Review from a CDER Statistical Reviewer's Perspective
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PS3e Diagnostic Devices For Assessing Risk |
09/13/12 |
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Organizer(s): Deepak B. Khatry, MedImmune; Jingjing Ye, Food and Drug Administration |
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Chair(s): Yuying Jin, FDA/CDRH |
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The discovery and validation of risk assessment markers has received great attention in recent years. These markers have the potential to personalize medical decision making, e.g., giving preventative intervention to a high risk subject or more aggressively treating/managing a patient with a poor prognosis. In the regulatory setting, submissions of diagnostic devices for risk assessment are burgeoning. In general, diagnostic devices are evaluated for association of the device result with the diagnostic truth and for the reliability of the result. Because risk assessment is a prediction of the future, traditional diagnostic accuracy measures that condition on the diagnostic truth (e.g., sensitivity and specificity) may not be sufficient. Many new performance measures have been developed recently. This session will be on study design, study conduct, and performance evaluation of risk assessment devices submitted for regulatory approval. |
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Evaluating the Clinical Utility of Biomarkers
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Evaluation of a Candidate Response Biomarker in Oncology: Challenges Extrapolating from Early Phase Studies
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Study Designs for Prognostic Marker Validation
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TH1 Panel Discussion: Challenges in the Design of Preclinical and Prodromal Alzheimer’s Disease Clinical Trials |
09/13/12 |
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Organizer(s): Steven Edland, University of California, San Diego; Julia Luan, FDA; Nandini Raghavan, Janssen Pharmaceutical Companies of Johnson & Johnson |
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Chair(s): Julia Luan, FDA; Nandini Raghavan, Janssen Pharmaceutical Companies of Johnson & Johnson |
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Strategies to disrupt the biology of Alzheimer’s Disease (AD) with disease-modifying agents fundamentally impact many aspects of trial design for AD. Industry is actively pursuing trial designs targeting patients in stages preceding AD where intervention could have the biggest biological impact. Successful early trials will require newer methodologies to capture subtle changes in early disease states. This session explores methodological issues in the design of prevention and prodromal AD trials, including developing sensitive outcome measures for early disease states; methods to reduce variability; and the challenges and potential utility of biomarkers at various stages of the trial, including endpoints. We bring together an expert group of panelists, both clinicians and statisticians, from the FDA, industry and academia, to discuss these issues and provide regulatory perspectives. The format of the session will be a panel discussion with pre-selected topics. Audience members will also have an opportunity to ask questions. Panelists: Marilyn Albert, Ph.D. (Johns Hopkins University) Gerald Novak, M.D. (Janssen Research & Development) Christy Chuang-Stein, Ph.D. (Pfizer) Steven D. Edland, Ph.D. (Univ. of California, San Diego) Nicholas Kozauer, M.D. (FDA) Sue-Jane Wang, Ph.D. (FDA) |
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TH2 Diagnostics Townhall Meeting |
09/13/12 |
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Organizer(s): Estelle Russek-Cohen, FDA CBER/OBE Division of Biostatistics |
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This is meant to be an interactive session. The audience will have a chance to comment and ask questions and have a dialogue with both industry and FDA present. The session will open with a brief discussion of which center regulates which diagnostic products presented by FDA. Companion diagnostics will also briefly be discussed and how the two centers work together. FDA will also discuss released guidance documents (draft or final). FDA will also point out how guidance documents are released (by center or centers, by product or products, etc.) and the fact that they are not regulations but advice. CLSI guidance documents will also be discussed. Previously submitted questions will be addressed by panelists from both FDA and Industry. This session will also act as an open-mike session in which general discussion from anyone involved in diagnostics can participate. The session will be informal with a goal of knowledge/idea sharing on several different diagnostic topics. |
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TH3 Town Hall on Late Breaking Person Reported Outcomes Topics: PCORI Methods Report and Other Issues Related to Efficacy and Safety Endpoints |
09/13/12 |
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Organizer(s): Yao Huang, FDA/HHS; Laura Lee Johnson, National Center for Complementary and Alternative Medicine (NCCAM); Lisa Kammerman, FDA/HHS; Arlene Susan Swern, Celgene Corp. |
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Chair(s): Shankar Srinivasan, Celgene Corporation |
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The Patient Centered Outcomes Research Institute (PCORI) Methods Report and lessons learned in the use of person-reported outcomes (PROs) to assess outcomes commonly reported by LASIK patients will be the focus of this interactive town hall. The intent of this session is to provide lessons learned from both the FDA’s perspective and that of Industry in the use of PROs for approval of a medical product and in communication with the public regarding medical product related safety issues. Experienced representatives from Industry and the FDA will be present for discussion of the issues. The overall lesson is that early interactions with the FDA are vital, both for statisticians in developing, selecting, and analyzing PROs, and for sponsors when PROs play a role in a study. Patient, clinician, and observer reported outcomes have always been important as efficacy endpoints in a subset of medical product clinical trials but have become increasingly more important as primary, secondary, and safety outcomes. PROs may discriminate between therapies which otherwise perform similarly on traditional efficacy measures such as overall survival and may elucidate significant quality of life and safety issues. Additionally, PROs are important in reimbursement and the clinician-patient interaction in some parts of the world, including specific practice fields in the US. Join us for a lively interactive town hall discussion. |
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TH4 Current Topics of Concern to Animal Health Statisticians |
09/13/12 |
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Organizer(s): Anna Nevius, FDA/CVM |
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Chair(s): Anna Nevius, FDA/CVM |
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The town hall meeting will provide an opportunity for CVM, industry, and academic animal health statisticians to network and discuss issues of concern. The procedures used by CVM for graphing Target Animal Safety study data will be demonstrated along with information about the programming language R, which is used in these procedures. After the presentation, we will have open discussions on topics of interest to the group. |
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Current Topics of Concern to Animal Health Statisticians
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Current R Use in CVM
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R Package Development and Use in the Center for Veterinary Biologics Statistics Section
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TH5 Therapeutic and Aesthetic Medical Devices Open Forum |
09/13/12 |
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Organizer(s): Gregory Campbell, Food and Drug Administration |
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This is an open forum to discuss any issues related to non-diagnostic medical devices and their regulation. Everyone is welcome. Members of the Statistical Interest Group for Medical Devices and Diagnostics (SIGMEDD) are encouraged to attend this town hall or the diagnostics one. |
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Fri, Sep 14 |
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PS4a Predictive Genomics Biomarker: From Lab to Medical Practice |
09/14/12 |
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Organizer(s): Xue Lin, FDA; Jingyi Liu, Eli Lilly |
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Chair(s): Jingjing Ye, Food and Drug Administration; Boguang Zhen, FDA |
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The advancement in pharmacogenomics in the past decade makes it possible to tailor the treatment according to patients' genetic makeup, leading to the promise of personalized medicine. Genetic biomarkers have been used in selection of drug responders as well as identification of patients with high risk of serious adverse drug reactions. However, challenges remain in discovery and validation of predictive genomic biomarkers and its incorporation in drug development. Speakers from regulatory agency, industry and academia will share their perspectives and experiences on study design, execution and interpretation. |
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A Prediction Model for Patient Classification for Personalized Medicine
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Bridging clinical efficacy results from a laboratory assay to a validated IVD kit - a case study
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Statistical Issues in the Development of Clinically Useful Gen(omic) Tests for Prognosis and Therapy Selection
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PS4b Early Phase Adaptive Designs - Build More Confidence for Late Phase Studies? |
09/14/12 |
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Organizer(s): Dennis Cosmatos, ReSearch Pharmaceutical Services, Inc.; Min Annie Lin, FDA/CBER |
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Chair(s): Jessica Hu, FDA/CBER; Alan Wu, Celgene Corporation; Hui Zhi, GSK |
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Although the industries may seriously implement adaptive designs in confirmatory trials as a result of the release of the FDA draft guidance on adaptive designs, the true practice in past several years is in early phase trials, such as adaptive dose-ranging studies and biomarker adaptive trials. Despite the regulator’s ethical concerns, utilizing adaptive approaches in designing phase I and II studies is considered to be able to obtain more information when there is little knowledge at the beginning of a trial. Thus, the chance of a successful confirmatory trial may be higher than using traditional designs in early stage studies. However, how much more confidence can gain through early phase adaptive designs deserves discussion. This session intends to focus on identifying the challenges in early phase adaptive designs from regulatory, industrial and academic perspectives. Discussions will include adaptive methods used, issues encountered, impacts gained on the late phase trials and conclusions drawn from experiences. |
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Early Phase Adaptive Designs in biological product Development -- Regulatory perspective and experience
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Response Adaptive Allocation Combining Two Novel Compounds for the Treatment of Cancer
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Biomarkers in Early Phase Drug Devlopment – Some Industry Experience
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Phase I/II Adaptive Design in Early Phase Clinical Development
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PS4c Benefit-Risk Assessment in Clinical Development and New Drug Application |
09/14/12 |
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Organizer(s): Huanyu Chen, The US FDA; Weili He, Merck & Co., Inc. |
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Chair(s): Greg Soon, FDA |
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Evaluation of clinical proof of concept (POC), optimal dose selection, and phase III probability of success (POS) as a benefit-risk assessment has traditionally been conducted by subjective and qualitative assessment of efficacy and safety data separately. This, in part, was responsible for the numerous failed phase III programs in the past. To extend the concept further, to market a certain product, pharmaceutical companies are increasingly pressed to demonstrate not simply efficacy and safety of their products, but a superior benefit-risk profile compared to alternative treatments. However, different efficacy and safety endpoints, with varying timescales and degree of impact on patients, make quantitative approaches difficult. To address the issue, the speakers in this session are invited to illustrate and discuss their approaches towards benefit-risk assessment. One discussant from FDA will share his insights and point of view from the regulatory perspective. This topic is very timely as the proposed approaches will provide clinical trialists with useful tools that can be utilized for different phases of their clinical development program for a benefit-risk assessment. |
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Some Thoughts on Benefit:Risk Assessment
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Pragmatic Considerations for Endpoint Selection and Display in Benefit-Risk Assessment
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Discussant(s): Robert Temple, FDA/CDER |
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PS4d Novel Statistical Methods for Noninferiority/Equivalence Testing |
09/14/12 |
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Organizer(s): Sujit Kumar Ghosh, North Carolina State University; Lei Nie, The US FDA; Muhtar Osman, Celgene Corporation; Zhiwei Zhang, The US FDA |
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Chair(s): Zhiwei Zhang, The US FDA |
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One of the primary goals in any clinical trial is to demonstrate that a new treatment is at least superior to placebo. Because a placebo arm is not included in the noninferiority trial, demonstrating the new treatment is superior to placebo can only rely on indirect methods which in turn lead to some unresolved issues. In this session, a diverse group of researchers from academia, federal institutions and industries would first present the most recent developments proposed to address some of these problems including bio-creep, error rate inflation through shared clinical evidence, and covariate adjustment approach. Such classical methodologies would then be followed us by some recent Bayesian approaches on noninferiority testing that have appeared in statistical literature. For the settings with composite null hypothesis as in noninferiority trials, these Bayesian methods have appealing features including their flexibility, ability to utilize reliable prior information based on historical data, and the advantage to make finite sample based inference as an alternative to those that are based on asymptotic methods. |
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Non-inferiority and superiority trials in the context of diagnostic devices
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A Noninferiority Testing Procedure based on Bayes factor and Total Weighted Error Criterion
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Calibration of treatment effect size through propensity score ratio reweighting
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Discussant(s): Sujit Kumar Ghosh, North Carolina State University |
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PS4e Practical considerations for statistical approaches to drug combination studies |
09/14/12 |
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Organizer(s): Yongman Kim, The US FDA; Satrajit Roychoudhury, Novartis Oncology; Lanju Zhang, MedImmune |
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Chair(s): Lanju Zhang, MedImmune |
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Combination drugs have attracted increasing attention in drug research as synergistic combinations can overcome toxicity and other side effects associated with high doses of single drugs. The recently-released FDA guidance on combining exploratory drug candidates will further fuel such studies. There is some statistical literature on study design and synergy assessment, but rigorous statistical methods are rarely applied in practice. In preclinical studies, improvised methods for synergy assessment are frequently found in publications. The increasing complexity of phase I trial designs poses new challenges in synergy evaluation. Evaluation of Phase 3 trials for combination drugs raises distinct regulatory and statistical issues, particularly when the combination is claimed to have a superior safety profile to one or both components. The combination strategy should be evaluated not only at the study level, based on a specific hypothesis, but also as part of the projected development path for the program. In this session, speakers will examine the applicability of available statistical methods and find ways to promote formal methods in drug combination studies at different stages. |
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Discovery and Characterization of Novel Synergistic Combinations Through In-vitro Cell Line Profiling
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Evaluating Drug Combination Synergy in In-Vitro and In-Vivo Studies
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Bayesian dose-escalation models for combination phase I trials in oncology
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Statistical and Regulatory Issues for Drug-Drug Combinations
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PS5a Statistical methods and pitfalls in application of biomarker-based diagnostics in clinical trials and practice |
09/14/12 |
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Organizer(s): Hope Knuckles, Abbott; Xiongce Zhao, National Institutes of Health |
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Chair(s): Thomas Gwise, FDA |
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A biomarker may provide useful guidelines in prognosis, disease assessment, and treatment. There has been considerable progress in the study of statistical issues in the evaluation of biomarkers involved in the design and analysis of trials in the past decades, although many challenges remain both in statistical methodology and practical processing of microarray data that prohibit one from extracting reliable and reproducible scientific information. In this session, we will focus on discussing the current status of several statistical challenges in clinical trials involving biomarker, such as optimization of study design that incorporate biomarker detection, statistical issues in data preprocessing, validation of analytic methods and prediction models, etc. We hope to inform the clinical researchers and drug developers who are interested about the challenges, more importantly, also stimulate the thinking of the expert. |
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Statistical Validation and Clinical Trial of Predictive Biomarker Models for Cancer Therapeutics
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Imaging in Biomarker Qualification
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An adaptive Bayesian hierarchical design approach for a multi-histology oncology trial targeting specific pathways and genetic signatures rather than a histological subtype
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PS5b Evaluating Clinical Benefit and Risk: Current Approaches and Future Directions |
09/14/12 |
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Organizer(s): Mike Wright Colopy, UCB Pharma; Freda Cooner, The US FDA; C V Damaraju, Janssen Research and Development, LLC; Shiling Ruan, FDA/CDRH |
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Chair(s): Mike Wright Colopy, UCB Pharma; Shiling Ruan, FDA |
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Pharmaceutical drugs and devices are increasingly evaluated by quantitative tools that combine benefit and risk. Data needs underlying some of these tools indicate considerable heterogeneity and add subjectivity to overall decision making process. It is important to identify robust methods that better characterize the joint distributions of benefit and risk across different therapeutic indications and treatment populations. Recent regulatory guidance to establish safety and risk management strategies for various pharmaceutical products and medical devices indicate the need to evolve a concerted approach in this direction. In addition, utility of these methods to establish comparative effectiveness of two or more competing products from the perspectives of payers, physicians and patients needs sound exploration. In this session, we will invite expert opinions (representing the academia, industry and regulatory) on the novel statistical methods to aid in the benefit-risk determination for pharmaceutical products and medical devices, underlying data needs, utility for comparative effectiveness objectives, and future directions. |
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Benefit-Risk Assessment: A Brief Review and An Example of A Quantitative Approach
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A Framework for Joint Modeling and Joint Assessment of Efficacy and Safety Endpoints for Probability of Success Evaluation and Optimal Dose Selection
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The Decision Analysis Initiative at the Center for Devices and Radiological Health
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PS5c Fitness-for-Use in CMC Assays |
09/14/12 |
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Organizer(s): ; Meiyu Shen, The US FDA |
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Chair(s): ; Meiyu Shen, The US FDA |
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CMC assays are developed alongside of product to facilitate process understanding and to bridge materials throughout the development lifecycle. Assays related to safety, efficacy, and stability of the product become key components of the product control strategy. Development and early manufacturing experience is utilized to establish specifications for the product. Other assays are used to monitor process flow and to facilitate process improvements and product investigations. During development CMC assays should have adequate accuracy and precision to minimize business risk, while assays supporting product specifications should satisfy regulators in their ability to ensure safe and effective product to the customer. Nonclinical statisticians play a role together with their non-statistical partners in designing, developing and validating assay methods, and for devising an assay control strategy which helps assure continued product quality. This session will bring together non-clinical statisticians and laboratory scientists from both FDA and industry to present and discuss opportunities to assure CMC assay reliability throughout the product and assay lifecycle. |
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The Golden Thread of Variability Reduction
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Using Fitness-for-Use to Define Design Space for Analytical Methods
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PS5d Design and Analysis Challenges in Pediatric Clinical Trials |
09/14/12 |
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Organizer(s): Theodore Lystig, Medtronic, Inc.; Alvin Van Orden, FDA |
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Drugs and medical devices are often used off-label in pediatrics, perhaps due to unique burdens in conducting sound clinical trials for regulatory approval of medical products in children. Clinical trials performed using pediatric subjects have special challenges. For example, diseases can have a low incidence in pedatrics, and informed consent might be more difficult for pediatric subjects. This can lead to smaller sized trials that lack power and are prone to variability. Also, for device trials, an approved active control might not be available, and placebo or sham surgery could be unethical. This can lead to the lack of a suitable control group for pediatric trials. Missing data issues might also be exacerbated when studying pediatric subjects as opposed to adult subjects. Despite these challenges, it is important to have approval and proper labeling of medical products for children. The session will contain presentations where issues in the design and analysis of pediatric clinical trials are discussed, and potential solutions are offered in the form of special study designs and statistical modeling. Potential speakers: L Thompson(CDRH),R Tiwari(CDER), Speaker(Industry) |
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Decision Analysis and Bayesian Statistics for Medical Device Studies
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Bayesian Hierarchical Models in Adaptive Clinical Trials Aiming to Generate Pediatric Efficacy Data
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PS5e FDA Guidance, Statistical Methods and Issues Related to NI Trials with Binary Outcomes |
09/14/12 |
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Organizer(s): George Y.H. Chi, Jassen Research and Development, L.L.C.; Daniel Rubin, The US FDA |
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Chair(s): Ivan S.F. Chan, Merck Research Laboratories; Kooros Mahjoob, FDA |
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The purpose of this session is to review some of the highlights of the recently issued FDA guidances on anti-infective drug products including the guidance on antibacterial, acute bacterial, skin and skin structure, hospital acquired pneumonia, ventilation acquired pneumonia and community acquired pneumonia and other related guidances and some related issues. New methods and issues pertaining to the choice of metrics, their NI margins and their associated test statistics for NI trials with binary outcomes will be discussed and illustrated with examples. Session Chair: Ivan Chan (Merck) and Kooros Mahjoob (FDA) Coordinator: Daniel Rubin (FDA) Invited Speakers: John Powers (NIH) Thamban Valappil (FDA) George Chi (Janssen R&D) Discussant: Tom Fleming (U Washington) |
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Clinicans understanding (and misunderstanding) of non-inferiority trials
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Non-inferiority design: Issues and challenges in anti-infective drug trials for severe bacterial infections
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Inferiority Index and Margin in NI Trials with Binary Outcomes
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Discussant(s): Thomas Fleming, University of Washington |
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PS6a Hidden Traps of Analysis Assumptions – How Robust are the Results? |
09/14/12 |
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Organizer(s): Bruce Binkowitz, Merck and Co., Inc.; Steven Bird, Merck and Co., Inc.; Terri Johnson, The US FDA; Qiang Xu, The US FDA |
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Chair(s): Steven Bird, Merck and Co., Inc. |
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Randomized clinical trials are the backbone of all clinical development programs. Statistical analyses and conclusions for clinical trial data need to be robust, and not unduly influenced by implicit or explicit assumptions. Standard analyses can be unwittingly distorted by unnecessary and/or untenable assumptions, potentially elevating the sponsor and/or consumer risk. This session will feature contemporary topics that illustrate and exemplify hidden traps in analytic assumptions embedded in a variety of common settings. Simple and efficient ways to potentially strengthen statistical analyses by weakening or eliminating assumptions will be presented, and illustrated using case study examples and realistic simulations. This session will be of interest to a broad audience, and hence appropriate as a plenary session. |
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Wilcoxon Signed-Rank Test in Statistical Literature and Practical Use
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Clinical Trials: Is the "Standard" Analysis Misleading You?
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Discussant(s): Vernon Michael Chinchilli, Penn State Hershey |
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PS6b Fitness-for-Use in Clinical Assays |
09/14/12 |
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Organizer(s): Martha Lee, U.S. Food and Drug Administration; Harry Yang, MedImmune |
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Chair(s): Martha Lee, U.S. Food and Drug Administration; Harry Yang, MedImmune |
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The development of clinical assay requires validation of the assay performance for its intended use. As clinical assays are often used to measure efficacy/safety outcomes or surrogate endpoints of clinical studies, validation of assay performance must be scientifically sound to ensure the data quality of clinical studies and the interpretability of study results. However, despite the importance of clinical assays in the drug R&D process, there is lack of regulatory guidance on clinical assay development and validation. In this session, issues and challenges related to clinical assay development and validation are discussed from both industry and regulatory perspectives. Also provided are practical and statistical advices that may help develop robust validation strategies. Several real-life examples are presented to highlight the use of such strategies. |
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Vaccines, Antibodies, Biomarkers and Correlates of Protection
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Statistical strategy for assay validation
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Assays for Use in Vaccine Clinical Studies: Statistical Issues and Challenges
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PS6c Use of the Kenward-Roger Adjustment in Mixed Models Analysis |
09/14/12 |
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Organizer(s): Linda Roycroft, Novartis; Veronica Nell Taylor, FDA/CVM |
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Chair(s): Jean Recta, FDA/CVM |
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Analyses of simple balanced mixed models produce exact F-test statistics, while analyses of unbalanced designs require approximate F-tests and methods to estimate denominator degrees of freedom. Kenward and Roger (KR) proposed an approximate F-test for Gaussian linear models which has become established practice in small sample inference. The KR method has been generalized to mixed effect models, including repeated measures, and availability as an option in SAS has made its application easier. For mixed models with unbalanced designs, complicated covariance structures, and small sample sizes, KR produces less biased estimators of precision and valid degrees of freedom. KR implementation and appropriate use and comparison to other approximation methods in SAS (e.g., containment, between-within, and Satterthwaite) for different model settings will be presented. We will also discuss difficulties that can arise when the model fit is problematic. Caveats of KR in the face of a non-linear covariance structure will be illustrated and motivates the discussion of the latest development in the Kenward-Roger approach. Speakers will be from industry, SAS and CVM. |
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Kenward-Roger Method for Small Sample Inference in Mixed Models
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Kenward-Roger Adjustment's Application in Multicenter Clinical Trial Study
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Problems with the Analysis of Balanced Data with Small Samples
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PS6d Go/No Go Decisions of Phase 3 trials in Clinical Drug Development |
09/14/12 |
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Organizer(s): Dongyue Fu, MedImmune, Inc; Nicole Li, Merck & Co.; Yun Wang, FDA/CDER; Lijun Zhang, FDA |
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Chair(s): Shengyan Hong, MedImmune, Inc |
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The phase 3 go/no go decision is perhaps the most critical decision point during the course of clinical drug development. Given the unsustainably high failure rate (~60%) of phase 3 trials and skyrocketing cost of phase 3 programs experienced in pharmaceutical industry, there has been increasing demand from company stakeholders to improve this critical decision by better quantifying the risk of conducting a large and costly phase 3 trial based on phase 2 data. It is particularly challenging in oncology drug development where oftentimes the primary endpoint for the phase 3 trial is overall survival (OS) but the phase 2 trial is powered only for an early endpoint, typically progression-free survival (PFS), whose relationship to OS is often unclear. This session will present recent research work in quantifying the probability of phase 3 success based on phase 2 data, especially when phase 2 and phase 3 primary endpoints are different, as in oncology trials. The topics will likely include predictive power, modeling and simulation methods. |
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Optimal Go/No Go decisions based on early endpoint data from phase 2
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Predictive Reasoning in Phase II-III Decision Making
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Discussant(s): Rajeshwari Sridhara, FDA |
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PS6e Advances in the Planning and Assessment of Non-Inferiority/Biosimilarity Trials |
09/14/12 |
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Organizer(s): Pilar Lim, Janssen R&D; Peiling Yang, FDA; Jialu Zhang, FDA |
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Chair(s): Isaac Nuamah, Janssen R&D |
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Planning and assessment of non-inferiority trials still remains one of the very challenging areas of clinical research. One contentious area is the specification of the margin with the accompanying assumptions of constancy and assay sensitivity. In this session, we will address some fundamental aspects of choosing non-inferiority margins as well as methods of statistical analysis. In particular, presenters will share a predictive bound approach for determining non-inferiority margins, an evidential approach for analyzing non-inferiority trials, as well as an asymmetrical margins approach for biosimilarity trials. Presenters: Qing Liu (Janssen R&D) on predictive bound approach; Jeffrey Blume (Vanderbilt University) on evidential approach; Yulan Li (Novartis) on asymmetrical margins approach Organizers:Pilar Lim, Janssen R&D; Peiling Yang, FDA; Isaac Nuamah, Janssen R&D |
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An evidential approach to non-inferiority clinical trials
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On the Lower Predictive Bound Approach for Non-Inferiority Clinical Trials with Binary Data
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Statistical Considerations in Biosimilar Clinical Efficacy Trials with Asymmetrical Margins
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PS7a Analysis of Recurrent Events in Medical Product Evaluation |
09/14/12 |
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Organizer(s): Jiajun Liu, Merck & Co.; Yue Shentu, Merck & Co.; Yunling Xu, Division of Biostatistics, OSB/CDRH/FDA; Yu Zhao, CDRH/FDA |
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Chair(s): Yue Shentu, Merck & Co. |
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What are the available statistical methods for recurrent events analysis in the presence of death for medical products regulatory approval? This session will cover the most recent developments in this filed: marginal and joint models. And relevant statistical and regulatory considerations will be discussed with examples emphasizing their clinical implications. |
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Latent Class Model of Recurrent Events
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Forward, backward and time-adjusted recurrent event processes in the presence of a failure event
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Recurrent Event Endpoints - Some Regulatory Experience
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PS7b ADAPT-IT: An FDA Funded Initiative in Regulatory Science |
09/14/12 |
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Organizer(s): Brenda Gaydos, Lilly; Anthony James Rodgers, Merck & Co, Inc.; John Scott, FDA / CBER |
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Chair(s): John Scott, FDA / CBER |
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In 2010, FDA announced a partnership with NIH to help advance regulatory science. ADAPT-IT, one of four projects funded to date, seeks to identify optimal approaches to the development and implementation of confirmatory adaptive clinical trials, and was highlighted in a Science news article entitled “FDA’s $25 Million Pitch for Improving Drug Regulation.” A goal of ADAPT-IT is to design at least four neurology trials from an adaptive Bayesian perspective, including a Bayesian adaptive “shadow design” for one ongoing trial and adaptive designs for additional proposed trials. This session will be a chance to hear about confirmatory adaptive designs in action. In addition, the resources FDA and NIH have devoted to this project underscore its importance to the statistical community, clinical trialists, and regulators. Plan: * Overview of ADAPT-IT and discussion of ICECAP trial (Scott Berry) * Discussion of ESETT Trial (Jason Connor) * Response from industry perspective (Brenda Gaydos) * Perspective from FDA/NIH ADAPT-IT Advisory Committee (Bob O’Neill) * Floor discussion and questions for the speakers |
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A Presentation of the ADAPT-IT Project
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A Bayesian Adaptive Trial for CER: Case Study in Status Epilepticus
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ADAPT-IT: Removing Barriers
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Discussant(s): Bob O'Neill, FDA/CDER |
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PS7c Current and Future Challenges in Moving Forward with Precision Medicine in Oncology |
09/14/12 |
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Organizer(s): Chia-Wen Ko, FDA; Pandu Kulkarni, Lilly; Mark Rothmann, FDA |
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Chair(s): Brent Burger, Cytel, Inc. |
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From the early era of local-regional therapy to the era of nonspecific therapy to the era of targeted therapy, the evolution of treatment in Oncology has seen dramatic advances. These advances are paving the way for “precision medicine” – the use of genomic, epigenomic, exposure, and other data to define individual patterns of disease, potentially leading to better individual treatment. Such advances are being coupled with plans to consider ways in which the process of evaluation can be expedited – e.g. in situations where striking results are seen early and thus providing early signs of potentially large improvements. This session will present a review of key clinical advances made in Oncology, their impact upon the regulatory approval process and clinical trial designs, and issues and challenges for the future. |
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Translational Science and the Changing Landscape of Cancer Drug Development
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Challenges in Development of Targeted Therapies in Oncology
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Potential Approaches for Large Treatment Effects Seen Early in Development
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PS7d Open Source Software in Drug Development: Challenges and Opportunities |
09/14/12 |
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Organizer(s): Joan Buenconsejo, FDA; Xiang Ling, The US FDA; Nandini Raghavan, Janssen Pharmaceutical Companies of Johnson & Johnson |
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Chair(s): José Pinheiro, Johnson & Johnson PRD |
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Utilization of open source software, such as R and OpenBUGS, in clinical drug development conducted by the biopharmaceutical industry is, by and large, limited to simulations for trial/program design and exploratory analyses not included in regulatory submissions. A number of factors account for that, but chief among them is the (incorrect) perception that open source software cannot be validated and, therefore, is not accepted by regulatory agencies for analyses included in submission packages. Because research in statistical methodology increasingly makes its way into software via the open source route, this perception creates further hurdles for the utilization of novel statistical methods in an industry badly in need of innovative designs and analysis methods. This session will discuss the challenges, perceived and real, to the broader utilization of open source software in clinical drug development, and opportunities for addressing those challenges. Speakers: Mat Soukup and Paul Schuette, CDER/FDA; Frank Harrell, Vanderbilt University; Keaven Anderson, Merck; Seth Berry, Quintiles |
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Perspectives on the Use of Open Source Software at the US FDA.
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Open-Source Software Qualification and Validation: An Industry Case Study
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PS7e Fitness-for-Use in In Vitro Diagnostic (IVD) Assays: Mysteries Revealed! |
09/14/12 |
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Organizer(s): Kristen Meier, FDA; Vicki Petrides, Abbott Labs |
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Chair(s): Vicki Petrides, Abbott Labs |
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You just need the lab results for the subjects in your study. Does it really matter which product is used to measure the analyte? Come on, a troponin assay is a troponin assay, right!?! WRONG!!! Not all assays are created equally, but how do you compare them? How do IVD manufacturers set specifications and develop lab tests? How does FDA evaluate IVD product performance? What guidelines are available to assist in assessing IVDs? What characteristics define a “good” assay? Join statisticians and engineers from industry and FDA in this session as the mysteries behind developing IVD assays that are fit-for-use are revealed. You will leave the session truly understanding why no assay is really the same as another. |
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Developing a Clinical Laboratory Test
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Evaluation Guidelines for Clinical Laboratory Tests
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Analytical Performance at Low Levels
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Key Dates
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April 30 - May 22, 2013
Invited Abstract Submission Open -
June 4, 2013
Online Registration Opens -
August 9 - August 23, 2013
Invited Abstract Editing -
August 23, 2013
Short Course materials due from Instructors -
August 26, 2013
Housing Deadline -
September 9, 2013
Cancellation Deadline and Registration Closes @ 11:59 pm EDT -
September 16 - September 18, 2013
Marriott Wardman Park, Washington, DC
