Online Program
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Mon, Sep 16 |
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SC1 Overview, Hurdles, and Future Work in Adaptive Design |
09/16/13 |
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Organizer(s): John Scott, FDA CBER/OBE/DB |
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Instructor(s): Chris Coffey, University of Iowa |
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In recent years, there has been substantial interest in the use of adaptive or novel randomized trial designs. Adaptive clinical trial designs provide the flexibility to make adjustments to aspects of the design of a clinical trial based on data reviewed at interim stages. Although there are a large number of proposed adaptations, all generally share the common characteristic that they allow for some design modifications during an ongoing trial. Unfortunately, the rapid proliferation of research on adaptive designs, and inconsistent use of terminology, has created confusion about the similarities, and more importantly, the differences among the techniques. Furthermore, the implementation of adaptive designs to date does not seem consistent with the increasing attention provided to these designs in the statistical literature. This course will first provide some clarification on the topic and describe some of the more commonly proposed adaptive designs. It will focus on some specific barriers that impede the use of adaptive designs in the current environment. Finally, there will be a discussion on future work that is needed to ensure that investigators can achieve the promised benefits of adaptive designs. The course presenter will be Chris Coffey, Ph.D., Professor, Department of Biostatistics and Director, Clinical Trials Statistical and Data Management Center, University of Iowa. |
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SC2 Survival Analysis: Overview of Parametric, Nonparametric and Semiparametric approaches and New Developments using SAS Software |
09/16/13 |
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Organizer(s): Cristiana Mayer, Johnson & Johnson Pharmaceutical Research and Development |
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Instructor(s): Joseph Gardiner, Michigan State University |
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Duration and severity data arise in several fields including biostatistics, demography, economics, engineering and sociology. The course presents an overview of survival analysis methods comprising parametric, nonparametric and semiparametric approaches and their recent developments. These new techniques extend their reach to include analyses of multiple failure times, time-dependent covariates, recurrent events, frailty models, Markov models and use of Bayesian methods. Methods include Kaplan-Meier estimation, accelerated life-testing models, and the ubiquitous Cox model. The course will use examples from biostatistics. Topics include modeling hazard rates, multivariate outcomes of mixed types, and quantile estimation. Applications will be paired with syntax for SAS procedures LIFETEST, LIFEREG, PHREG, RELIABILITY and QUANTLIFE for analyzing real data sets drawn from easily accessible sources. |
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SC3 Unsettled Issues In Clinical Trial Data Monitoring |
09/16/13 |
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Instructor(s): Susan Ellenberg, Univesity of Pennsylvania; Steve Snapinn, Amgen |
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The use of independent data monitoring committees to oversee the accruing data in a clinical trial has become much more common over the past two decades, both in industry- and government-sponsored clinical trials. Some basic principles—avoiding conflicts of interest, maintaining confidentiality of interim data, having a formal charter to describe DMC operations, establishing boundaries to guide early stopping decisions—are widely accepted. The implementation of these principles, however, is often not straightforward and multiple perspectives have emerged in regard to optimal practices. In this short course we will address issues relating to independence (what constitutes a conflict of interest; role of sponsor staff; the independent statistician; use of “non-independent” DMCs); appropriate criteria for early termination (both for efficacy and for futility); blinding (what should be blind, and to whom); DMCs in studies using adaptive designs; content and format of DMC reports; and special challenges for DMCs for multinational trials. |
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SC4 Enrichment Strategies in Adaptive Clinical Trials: Theory and Implementation |
09/16/13 |
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Organizer(s): Vlad Dragalin, Aptiv Solutions; Anastasia Ivanova, University of North Carolina at Chapel Hill |
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Instructor(s): Vlad Dragalin, Aptiv Solutions; Anastasia Ivanova, University of North Carolina at Chapel Hill |
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This course will offer recent developments in methodology and case studies of adaptive trials with patient population enrichment. We will cover some specific to adaptive issues highlighted in the recently issued FDA Draft Guidance on Enrichment Strategies for Clinical Trials. The goal is to help the participants better understand the methodological issues and challenges in implementation. We will review designs for trials with potentially high placebo response including placebo run-in and the sequential parallel comparison design. We will also review randomized withdrawal design and introduce designs that combine parallel comparison, randomized withdrawal and placebo run-in strategies. We will examine recent methodology developments in population enrichment designs with predefined sub-populations. Testing strategies for combining data from before and after sub-population selection, multiplicity tests for intersection hypotheses, sub-population selection rules and related sample size re-estimation methods will be reviewed. |
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SC5 A Practical Guide to Prevention and Treatment of Missing Data |
09/16/13 |
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Organizer(s): Devan Mehrotra, Merck; Lei Xu, Biogen Idec |
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Instructor(s): Craig Mallinckrodt, Eli Lilly and Company; Russ Wolfinger, SAS |
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This short course will focus on practical approaches to the prevention and treatment of missing data in longitudinal clinical trials. The course will be taught by internationally recognized experts as representatives from the Drug Information Association’s Scientific Working Group on missing data. Participants will have free access to, and hands-on experience using, sensitivity analysis tools developed by DIA working group. Recent decades have brought advances in statistical theory, which, combined with advances in computing ability, have allowed implementation of a wide array of analyses. In fact, so many methods are available that it can be difficult to ascertain when to use which method. A danger in such circumstances is to blindly use newer methods without proper understanding of their strengths and limitations, or to disregard all newer methods in favor of familiar approaches. Moreover, the complex discussions on how to analyze incomplete data have overshadowed discussions on ways to prevent missing data, which would of course be the preferred solution. Therefore, preventing missing data through appropriate trial design and conduct is given significant attention in this course. Nevertheless, missing data will remain an ever-present problem and analytic approaches will continue to be an important consideration. Recent research has fostered an emerging consensus regarding the analysis of incomplete longitudinal data, especially in regards to the need for sensitivity analyses. Theoretical concepts underpinning this consensus will be covered. An example data set will be used to illustrate a structured framework for choosing estimands, estimators, and sensitivity analyses. Participants will be guided through the analysis of a second example data set using the SAS macros developed by the DIA Working Group. Participants will have access to other training material related to missing data and the use of the sensitivity tools. A few highlights of this short course that differentiate itself from previous ones include (1) emphasizing “practical” and providing hands-on software programs for immediate implementations (2) a comprehensive range of actionable approaches to implement and leverage the 2010 National Academy of Sciences report on the handling missing data in clinical trials (3) targeting to facilitate real-life problem solving as well as regulatory-industry interactions. |
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SC6 Issues and Methods for Multi-Regional Clinical Trials From an Industry and Regulatory Perspective |
09/16/13 |
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Organizer(s): Bruce Binkowitz, Merck and Co. Inc.; Joshua Chen, Merck and Co. Inc. |
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Instructor(s): Joshua Chen, Merck and Co. Inc.; James Hung, FDA; Sue Jane Wang, FDA |
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Part 1: Industry Perspectives and Experiences Part 2: A Regulatory Perspective and Experiences While the concept of a multiregional clinical trial is not new, the prevalence of these trials has been growing over the last few decades. MRCTs are most often conducted as a single trial focusing on an overall result or set of results, but when such trials are submitted to health authorities, the scope and concern often broaden to include the ""local"" results. It is readily accepted that studying patients from many different regions within a single trial under a single protocol is an efficient method of trial design. However, such trials come with their own special issues during design, operation, analysis, and interpretation. This short course will introduce the statistician to these issues, and describe methods to help handle such issues. Topics to be covered include specific MRCT concerns at the design stage, including defining region and the impact of lack of regulatory harmonization on therapeutic area guidances. The impact of the number of regions and the sample size configuration across the regions on the required total sample size, the estimation of between-region variability and type I error rate control for the overall treatment effect will also be discussed. Analysis and interpretation will be discussed including graphics and methodology to evaluate consistency of effect. Methods of assessing consistency of results across regions will be described and the properties compared, and additional methods involving adaptive designs and Bayesian methods will be described. Both fixed and random effect models will be discussed with application to the MRCT setting. Case studies will be presented and the methods introduced will be applied to those case studies. This course will be taught by FDA statisticians who have done research and published on this topic, as well as Industry statisticians who were members of the PhRMA MRCT Working Group. |
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Tue, Sep 17 |
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GS1 Keynote Presentation |
09/17/13 |
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Waiting on text from Rubin and Lilly |
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GS2 Plenary Panel Session: Innovation and Best Practices for Clinical Trials |
09/17/13 |
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Organizer(s): Bruce Binkowitz, Merck and Co. Inc.; Lilly Yue, FDA |
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TL01 Logistics and Implementation of Adaptive Trial Designs |
09/17/13 |
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Chair(s): Eva R Miller, Quality Data Services |
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It has been almost 4 years since February 2010 and the release of the FDA Draft Guidance for Industry: Adaptive Design Clinical Trials for Drugs and Biologics. Statisticians who would like to share some of their experiences in implementing these adaptive clinical trials and working with their study teams to adopt these new methodologies will discuss their experiences and the differences between adaptive trial designs and the more traditional trials. Impact on teams and team structures will also be discussed. Newcomers to adaptive trial design are welcome. Questions to be covered include but are not limited to: (1) What additional planning, teamwork and communication is required for successful implementation of adaptive trial designs? (2) How do simulations play a role in the development of effective adaptive trial designs? (3) What is required for randomization schemes, drug supply management, and clean data within adaptively designed studies? |
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TL02 Borrowing from Informative Priors |
09/17/13 |
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Chair(s): Pablo E. Bonangelino, FDA/CDRH |
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While not as common as non-informative priors in Bayesian trials submitted to FDA, borrowing from informative priors is possible in a regulatory setting. A critical question in these cases is how much to discount a given prior. This can be viewed as an example of elicitation from experts, with all of the attendant considerations. The purpose of this roundtable is to share experiences with informative borrowing and to discuss some of the difficulties with doing so including issues related to expert elicitation. |
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TL03 Utility of Bayesian Methods in the Analysis of Safety Data in the Pre-Market Setting |
09/17/13 |
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Organizer(s): Caiyan Li, Takeda; Melvin Slaighter Munsaka, Takeda Global Research and Development, Inc |
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Chair(s): Caiyan Li, Takeda; Melvin Slaighter Munsaka, Takeda Global Research and Development, Inc |
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There are many statistical challenges encountered in the analysis of safety data. Statistical methodologies and their applications to safety data have not yet been fully realized, though various approaches have been discussed in the literature. This creates room for improving in the analysis and reporting of safety data. Bayesian methods present a potential and viable approach to enhance the analysis of safety data. Potential applications of Bayesian methods for analysis of safety data include the ability to incorporate prior information that may be pertinent to the safety question being investigated or perhaps incorporating clinical judgment and other factors related to safety. Bayesian estimation methods also allow for parameter uncertainty and are especially useful because of the high dimensionality of safety data and can be used to combine information across different safety studies. In this round table discussion, the utility of Bayesian methods will be discussed within the premarketing setting in the context of the following questions: (a) What is your experience using Bayesian approaches in the analysis of safety data? (b) Why should be we use Bayesian approaches in the analysis of safety data? (c) Can Bayesian methods be used in routine analysis of safety within the testing and estimation contest? If so what approaches are available and what are the challenges? (d) Can Bayesian methods be used in routine modeling and prediction of safety data, analysis of rare events, safety data synthesis, and in addressing multiplicity questions? The discussion will highlight the relevant methodological approaches and also discuss how these compare with the frequentist methods and the advantage of using such methods. |
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TL04 Long Term Safety Follow up - Challenges |
09/17/13 |
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Chair(s): Vipin Arora, AbbVie, Inc. |
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Long term follow up especially for Safety is needed to ensure that the risk beneift profile is not shifted for treatments of chronic conditions. Challenges in designing long term safety studies and ensuring sufficient participation is a challenge. The discussion to highlight challenges and how to help Safety Team to ensure designing and implementing realistic studies/follow up that are feasible and have interim checks (built in) to ensure appropiate flexibility is available and agreed upon due to long term follow up type of these studies. |
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TL05 Assessment of Benefit Risk in Pivotal Studies |
09/17/13 |
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Chair(s): Suchitrita Sarkar Rathmann, AbbVie |
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Benefit-risk assessment of a treatment under review has become one of the primary concerns of Regulatory Agencies over the last few years. Quantifying Benefit-risk profile of any treatment is not an easy task, especially if multiple endpoints are involved in the pivotal study. We would like to discuss different methods of quantifying the benefit-risk profile of a treatment under consideration in accordance with review practices of regulatory agencies and look at examples from a pivotal study with multiple endpoints (which included both categorical and continuous variables). This could lead to further discussions on whether the benefit-risk profile can be compared to treatments already available in the market. |
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TL06 Implementation of Biomarker Development and then Validation at the Intercection of Research and Clinical Development |
09/17/13 |
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Chair(s): Maha Karnoub, Celgene |
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In this roundtable, we would review what the FDA guidelines are on what constitutes a biomarker worthy of inclusion in a submission for a given claim and we would discuss then models of collaboration between Research and Clinical Development. The collaboration would ensure - Reproducibility of the work done in Discovery Research - That we understand the characteristics of the biomarkers that were developed - That we understand the objectives from this biomarker as it now enters a clinical trial - That we have the necessary information to answer the questions from the objectives and to have that biomarker considered within the submission. |
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TL07 Criteria for Biosimilar approval |
09/17/13 |
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Organizer(s): Peter A Lachenbruch, Oregon State University (retired) |
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Chair(s): Eric Chi, AMGEN; Peter A Lachenbruch, Oregon State University (retired) |
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Biosimilars, or Follow on Biologics, have attracted much interest in recent years. FDA has been developing standards and the pharmaceutical industry is also doing so. Some issues are the large molecules that must be shown to be ‘similar’ – and this isn’t easy. In addition, many variables may be required to be similar. This roundtable will examine some of the issues 1. What are appropriate levels of similarity? 2. How many variables need to be shown to be similar? a. Which variables should be considered? b. How should safety and efficacy variables be treated? |
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TL08 Biomarker |
09/17/13 |
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Organizer(s): Steven Bai, FDA |
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Chair(s): Grace Xiuping Liu, Johnson & Johnson - Janssen Research Development ; Lixia Pei, Jenssen Research & Development |
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Increase understanding of a disease mechanism, thereby provide better choice of drug targets for the future study becomes very important in the clinical studies. Clinical biomarker tests that enable the characterization of patients population and aid in making new drugs reach the intended target for a better treatment decisions play an important role in clinical trials. Definitive evaluation of the clinical utility of these biomarkers requires understanding the concept of biomarkers, type of biomarkers, validation of biomarkers and the application in the clinical trials design. This round table will focus on discussion those questions. The Key aspects of the discussion include biomarkers selection in early trials to confirm a treatment’s activity and select a dose for further testing via the Prognostic biomarkers; biomarkers application for predicting the treatment effect on the clinical outcome; surrogate biomarkers in clinical endpoint to evaluate the effect of a specific treatment. Another application will be for the bridging biomarkers and the clinical data study supports the third country submission. Questions to discuss: 1. Have you done some biomarker analyses in clinical trials? Please share your experience. 2. Do you have any experience of applying noval surrogate endpoint in the regulatory setting? |
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TL09 Ideas on Establishing an Independent Supervisory Body for Data Monitoring Committee Processes |
09/17/13 |
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Chair(s): Yeh-Fong Chen, US Food and Drug Administration; Paul Gallo, Novartis Pharmaceuticals |
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For clinical trials which incorporate interim analyses of unblinded data, an independent Data Monitoring Committee (DMC) is commonly constituted to review the results, to ensure patient safety and protect trial integrity. Whether or not the DMC will function effectively and provide sound recommendations to the sponsor depends on the DMC’s qualifications and experiences, and the degree of pre-planning. Recently, there have been proposals for establishing an independent party to provide oversight or supervision to a DMC, or with whom the DMC can interact, in the hope of enhancing the process. We will discuss pros and cons of such operational models. |
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TL10 Operational Models for Data Monitoring Committees |
09/17/13 |
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Chair(s): William Coar, Axio Reserach; Lynn Navale, Amgen Inc. |
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Data Monitoring Committees (DMCs) are now standard for many types of clinical studies, and have numerous roles such as protecting patient safety and ensuring quality conduct of the study. This is done through interim assessment of data throughout the course of a clinical trial and involves the Sponsor organization, an Independent Statistical Center (ISC), and the DMC. The interaction of these three entities is often clearly defined in a DMC Charter document, but the operational procedures may not be. There is a spectrum of ways in which the ISC and Sponsor organization may arrange responsibilities. One model commonly used is for the ISC to perform all programming of reports provided to the DMC. A very different model is for the Sponsor organization to send the ISC a complete set of validated programs and CRF data. Other arrangements may occur that are between these two extremes. The focus of this round table will be to discuss the merits for and against each of these operational models. |
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TL11 How Can Statisticians Show Leadership in Improving Data Quality? |
09/17/13 |
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Chair(s): Michael D. Hale, Amgen |
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Studies of medical interventions cost billions of dollars annually, and involve enormous numbers of volunteer patients. What should we be doing to better ensure those efforts provide high quality data, and enable better decisions? Error-rate reduction in trial data may get a disproportionate amount of resource and attention, with diminishing returns as one strives for ZERO. This measure of quality is about dataset FIDELITY. Discussion point 1 is about "fit for purpose" data quality Vs a zero-error-rate perspective. What would we need to change to move to a fit for purpose paradigm of quality? Would this involve a risk-based approach to data quality, considering probability and impact of error for various data categories? Discussion point 2 addresses a different dimension, that of RELEVANCE: are we measuring all the things needed to well-inform decisions related to medical interventions? Perhaps a more thorough approach to relevance assessment (pre- & post-study) is needed to adopt a fit for purpose approach? How else would we categorize data regarding impact of error? |
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TL12 Demographic Criteria for Diagnostic and Device Clinical Trials |
09/17/13 |
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Organizer(s): Hope Knuckles, Abbott |
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Chair(s): Richard Kotz, FDA |
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A draft guidance was issued for gender and other demographic consideration in device trials where treatment may be affected by these potential covariates. There is a wide variance in device and diagnostic trials, and a discussion on the application of this guidance on the clinical trial design will be discussed. |
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TL13 Practical issues in the application of propensity score methodology for designing pivotal, non-randomized studies of medical device |
09/17/13 |
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Chair(s): Jianxiong Chu, Food and Drug Administration |
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Many therapeutic device studies involve highly complicated surgeries and sometimes it is impractical to conduct a multi-center, randomized clinical trial. Therefore, it is not uncommon that a prospective concurrently controlled but non-randomized multi-center study is proposed to collect clinical data to support a future 510K or PMA submission. In order to ensure a fair comparison between the investigative group and the control group, propensity score methodology is often proposed. In this roundtable session, we will discuss several practical issues for designing such non-randomized device studies, such as sample size estimation, pre-specification of the models for calculating propensity score (in particular, how to handle the center-level covariates), pre-specification of the primary analysis method using the calculated propensity score. |
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TL14 Data poolability |
09/17/13 |
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Chair(s): Chul H Ahn, FDA/CDRH |
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Data from different groups are combined to obtain an overall estimate of an outcome variable. Different groups may consist of different centers, different studies, different patient populations, different device models, and so on. We will discuss some of the related issues with emphasis on pooling data from multi-centers. The following are the questions we may be interested in discussing. How do we demonstrate data are poolable? How do we handle non-poolable data? How do we determine the treatment effect if centers are homogeneous with respect to protocol and study execution, but heterogeneous with respect to results? Why do some sites perform better than the others – any potential sources of site heterogeneity? |
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TL15 Study design and imprecision estimation in precision study for diagnostic devices |
09/17/13 |
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Chair(s): Qin Li, FDA |
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Imprecision is a summary measure indicating the extent of disagreement or variability of a set of replicated measurements. How to design and estimate imprecision depends on whether a test is continuous, semiquantitative, or qualitative. CLSI guideline EP5 discusses precision studies for quantitative assays performed on homogeneous biological specimens. I/LA28 A2 Appendix 1 provides statistical suggestions on how to evaluate the precision of immunohistochemistry assays which can be qualitative, semiquantitative or quantitative. Evaluation of qualitative test including assessing its reproducibility is also introduced in EP12. This roundtable session intends to share experiences and suggestions on study design and variance estimation approaches among industrial, academic and regulatory researchers. |
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TL16 Bias adjustment following group sequential design |
09/17/13 |
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Chair(s): Qi Zhang, Eli Lilly and Company |
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The group sequential design (GSD) has been used to allow potential early trial termination while preserving the type I error rate, however, it can also cause bias in estimating treatment effect. As demonstrated by literature, the GSD tend to overestimate the true treatment effect size at early interim analysis, although the exact cause and effect of such a phenomenon, and the ways for bias adjustment is not well-understood by many in clinical trial practice. In this session, at first, magnitude of bias and its impact on the assessment of study effect size in some simulation studies in the literature will be discussed; secondly, existing approach for bias adjustment will also be shared; lastly, appropriate approach to display the final data after GSD will be discussed, especially to allow an appropriate assessment of magnitude of treatment effect across multiple studies during an NDA submission. |
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TL17 Analysis of Overrun Data in Group Sequential Trials |
09/17/13 |
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Chair(s): Paul Thomas DeLucca, Merck and Co., Inc. |
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When a group sequential clinical trial is stopped by a data monitoring committee data often continue to accumulate from the time of database lock until patients can be brought back for an end of study visit (overrun data). In accordance with the intention-to-treat principle all data should be included in a final analysis of study data. A relevant question is whether the final analysis including the overrun data can be tested at an unadjusted alpha level or whether some adjustment is required to preserve the type I error. Questions to be discussed at this roundtable include: Under the null hypothesis once a study has stopped at an interim analysis and a type I error has been committed is it possible to make a type I error at a final analysis including overrun data? Do regulatory agencies expect an alpha adjustment at the final analysis? Do sponsors typically test at an adjusted alpha and if so, how is the adjusted alpha determined? Methods of estimating treatment effect and confidence intervals, such as repeated confidence intervals and median unbiased estimates will also be discussed. |
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TL18 Is interim futility analysis a free lunch? |
09/17/13 |
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Chair(s): Julie Cong, Boehringer Ingelheim Pharmaceuticals Inc |
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In recent years, it has become more common to assess futility of an investigational product during interim analyses in randomized clinical trials. Futility assessment is either considered the sole purpose for the interim analysis or together with efficacy claim. An attractive feature of futility analysis at the interim is that it typically does not inflate the type I error rate. This roundtable session aims to discuss a few statistical and practical issues when planning for futility analysis in randomized trials: 1) under what circumstances, a futility analysis should be needed or useful; 2) scenarios when type I error rate needs to be adjusted; 3) impact on power; 4) practical issues in the timing, stopping rules and interpretation of the trial results; 5) use of conditional power and Bayesian predictive probability in futility analysis. |
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TL19 Regulatory Issues in Meta Analysis of Safety Data |
09/17/13 |
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Chair(s): Aloka G Chakravarty, US FDA; Brenda Crowe, Eli Lilly & Company |
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Meta analysis has been used in regulatory decision making, especially in safety evaluations, quite extensively. Meta-analyses conducted in the regulatory context and for safety evaluation have unique issues compared with traditional meta-analyses. In particular, use in the regulatory context requires high levels of rigor, robustness, and transparency. Concepts such as well-defined objectives, pre-specification, blinding, clear exposure definitions, good outcome ascertainment, appropriate statistical methodology, data quality, and clear and thorough reporting are very important. In addition, the sparseness and possible imbalance of the outcomes creates both procedural and methodological challenges. In this roundtable discussion, we discuss the use of meta-analysis of randomized trials conducted in the regulatory context for the evaluation of safety. We will discuss key design, methodological, and reporting issues. We will also discuss real-life examples that had regulatory consequences in light of these issues. |
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TL20 Planning for Clinical Trials with Recurrent Event Data: When the Poisson Modeling Assumption may Not Hold |
09/17/13 |
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Chair(s): Judy Li, FDA; Jerry Weaver, Novartis Pharmaceuticals Corporation |
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Recurrent events are often encountered in clinical trials. Some examples include the number of seizures in epilepsy, the number of relapses in multiple sclerosis, the number of bleeding episodes in coagulopathies, and the number of exacerbations in pulmonary diseases such as chronic obstructive pulmonary disease and asthma. The traditional design and analysis approach for these situations involves using the Poisson model under the assumption of a homogeneous Poisson sampling process. However, the homogeneous Poisson process may not hold leading to a problem of over-dispersion and thus incorrect inferences. While there are many alternative analysis methods for addressing the non-homogeneous Poisson process using the negative binomial model or semi-parametric multiple time-to-event methods (Anderson and Gill [1982]; Prentice, Williams, and Peterson [1981]; Wei, Lin, and Weissfeld [1989]), appropriately sizing recurrent event trials of this type in practice seems to be directed more towards the negative binomial model (Keene, et al [2007]) since there exists a closed form solution. Topics for discussion: 1) In what situations would time-to-first event be more favorable compared to the negative binomial or multiple time-to-event methods? 2) How robust is the negative binomial model when the homogeneous Poisson process is not violated?; 3) What additional assumption still exists when using the closed form sample size solution for the negative binomial model (Keene, et al [2007]), how could this impact power, and how should we possibly address this violation?; 4) How would one go about sizing the study using the semi-parametric multiple time-to-event models, and should we consider sample size re-estimation methods to revise our initial sample size estimates? |
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TL21 Predictive Modeling In Observational Studies |
09/17/13 |
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Chair(s): Rui Li, Quintiles Outcome; Zhaohui Su, Quintiles Outcome |
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PURPOSE: Observational studies collect many clinical and patient characteristic variables. The objective of this session is to present statistical methods for building predictive models of patient outcomes as a function of patient characteristics and baseline clinical variables. DESCRIPTION: Predictive models focus on identifying significant predictors of outcomes rather than making inferences about pre-selected variables after adjusting for covariates. Statistical considerations include variable selection, variable forms, multicollinearity, missing data, clinical input and model validation. Variable-selection algorithms in current packaged programs, such as conventional stepwise regression, can easily lead to invalid estimates and tests of effects. Models should be constructed taking into account clinical reason or understanding. This session starts with an overview of existing model building methods, followed by detailed discussions of model-building steps and aforementioned statistical considerations. Univariate and multivariate regression, correlation analysis, bundling, variable reduction, goodness of fit, and bootstrap validation will be illustrated. The concepts and methods will be explained with the use of real-world observational study data, when possible. |
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TL22 Reallocation of Type I Error to Doses not Eliminated Due to Prospectively Defined Objective Safety Criteria |
09/17/13 |
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Chair(s): Anthony James Rodgers, Merck & Co, Inc. |
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Studies with more than one study dose versus a comparator have an inherrent issue of multiplicity which could be mitigated using objective safety criteria that if prospectively defined could preclude certain doses from being subjected to a multiplicity adjustment for efficacy. Type I error for testing the efficacy could be allocated only to those doses that are not excluded from consideration due to safety. However, the criteria for exclusion based on safety would need to be objectively assessed and prospectively planned. If objective futility criteria are employed this may also provide for an opportunity to prospectively plan to reallocate alpha in the event doses are eliminated from consideration. Specific examples will be discussed. |
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TL23 Cohort sampling design in risk assessment markers |
09/17/13 |
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Chair(s): Yuying Jin, FDA; Rong Tang, FDA |
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The study of risk assessment markers in rare disease or slow progressing disease requires studies with a very large sample size or a study that spans many years. A prospective population based cohort study may not be practical for evaluating such markers. Some cohort sampling designs such as nested case control and case cohort sampling have been widely used in epidemiological studies. And they can be effectively used in the risk assessment marker studies under certain circumstances. These cohort sampling designs may be used with both retrospective as well prospective study populations. However for such cohort sampling designs, appropriate sampling scheme should be considered to ensure the samples are representative of the intended use population. Furthermore, appropriate statistical method need to be utilized to eliminate the bias of the performance estimates. The roundtable discussion will provide the participants an opportunity to share the experience and the challenges with the cohort sampling studies. Question: what is your experience with cohort sampling design? Can you think of examples where these methods can be helpful? What should one watch out for when these methods are used? |
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TL24 Evaluating The Methodological Quality of Randomized Clinical Trials |
09/17/13 |
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Chair(s): Vance William Berger, NIH |
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There are many hazards facing those who plan, conduct, and review randomized clinical trials, and the road to validity is rather narrow. For example, it is not enough that a study be randomized; the precise manner of randomization is also crucial in determining if the study is valid or not. The choice of endpoints, masking, allocation concealment, enrichment, alpha preservation, and the types of statistical analysis are also key determinants of trial quality. We will discuss all of these features, and possibly others as well, to better help participants recognize quality trials, and to suggest how all trials might be held to higher standards. |
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TL25 Pros and Cons of Discriminate Analysis in Animal Health Dose Titration Studies |
09/17/13 |
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Chair(s): Theresa M Real, Novartis Animal Health |
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The analysis of tissue pathology data can be accomplished using many different statistical methods ranging from separate analyses for every variable to canonical discriminate analysis to provide information about the contribution of the variables included in the analysis. Separate analyses for each variable provide information only on that variable for each treatment group and may lead to equivocal results. The discriminate analysis provides weights based on those variables that have a high influence and may remove variables that have a minor contribution. A fixed index, based on input from a pathologist, generally keeps all variables and weights each variable’s importance by its incidence and severity. Discussion will center on the pros and cons of each method. |
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TL26 Recognizing and avoiding common bias problems in clinical trials |
09/17/13 |
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Chair(s): Jonathan Siegel, Bayer HealthCare Pharmaceuticals Inc. |
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In this session we will discuss common bias problems in clinical trials and ways to avoid or mitigate them. Our aim is prevention, and the focus is on practical, low-tech, hands-on, shoe-dirt trial design strategies to recognize and avoid common problems and pitfalls, rather than sophisticated post-hoc analyses to adjust for them. Some issues may appear very simple yet are surprisingly pervasive. The extent of the problem will be illustrated with examples of results from biased trials published in leading journals, with discussion on how the trials could have been improved to avoid them. Examples will focus on problems particular to cancer trials, but issues are relevant to virtually all classes of trials and input from all backgrounds and therapeutic areas is welcome. Problems discussed will include assessment schedules which are inadequate to observe the dynamics of the phenomenon being modeled; assessments that depend on treatment timing; patient dropout patterns that differ among treatment arms; the use of continuous methods for highly discrete assessments; crossover issues; and more. This session would be of value to clinicians, regulators, and medical professionals as well as statisticians. |
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TL27 Randomization Metrics: How do you measure the goodness of a randomization scheme? |
09/17/13 |
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Chair(s): Dennis E Sweitzer, Medidata Solution |
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By what statistical criteria, if any, do statisticians decide randomization methods and parameters to use for a given study? While most randomization methods usually seem to work well in published literature, is there any concern about worst-case performance of the methods, or performance under real world conditions (e.g., large differences in numbers of patients in subgroups or centers). Randomization methods generally are designed to be both unpredictable and balanced between treatment allocations overall and within strata. However, when planning studies, little consideration is given to measuring these characteristics, nor are they examined jointly, and published comparisons between methods often are not useful. In order to compare randomization performance, I simulated various covariate-adjusted randomization methods (such as permuted block, minimization, and urn methods), and compared efficiency & unpredictability graphically and statistically. Predictability was measured with a modified Blackwell-Hodges potential selection bias in which an observer guesses the next treatment to be one that previously occurred least in a strata, reflecting a game theory model pitting observers versus statistician, and is easy to calculate and interpret. Efficiency was calculated using Atkinson’s method because: (1) The main impact of imbalances is a loss of statistical power; (2) Even if treatments are balanced overall, imbalances within small strata can have a disproportionate impact on efficiency; (3) It is conveniently interpretable as lost sample size. |
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TL28 Statistical Significance vs. Clinical Significance |
09/17/13 |
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Chair(s): Melissa Simones, Boston Scientific; Jack Zhou, FDA |
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For a clinical trial to be successful, the result usually has to meet both statistical and clinical success. Statistical success is usually measured by a statistically significant result, which can be demonstrated by having the lower bound of the confidence interval for the primary endpoint greater than zero (for endpoints with higher values being better). Clinical success, on the other hand, is often interpreted as the observed point estimate of the primary endpoint being greater than a certain value, often the Minimum Clinically Important Difference (MCID). This frequently creates a problem if one is not careful. Suppose the study is powered at 80%, assuming the true distribution of the primary endpoint is normally distributed with the mean of MCID. By the end of the study, there is only a 50% chance that the OBSERVED point estimate of the measure will be greater than the MCID. In other words, even though the study has 80% power to reach statistical success, it only has 50% power to reach clinical success. There is a 30% chance the study will be statistically successful but clinically unsuccessful. Clinical trial sponsors and regulatory agencies should be aware of this issue. Have you ever had a study like this where the statistical significance was reached but the clinical significance was questioned? How was the study result interpreted? What do you think that can be done to properly align statistical significance with clinical significance? |
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TL29 Prediction of event times in randomized clinical trials |
09/17/13 |
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Chair(s): Misha Salganik, Cytel Inc |
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In a short presentation we will illustrate a simple prediction method that is based on a combination of previous knowledge of the time to event distribution with the data accumulated in the trial. We will discuss the advantages of the prediction methods that do not require unblinding of the treatment assignments. We will ask the participants about their experiences. |
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TL30 Impact of missing data on the approval of potentially efficacious therapies |
09/17/13 |
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Chair(s): Xiaohong Huang, Vertex Pharmaceuticals; Abdul J Sankoh, Vertex Pharmaceuticals |
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There are a number of operationally preventive measures and some statistical approaches with seemingly remedial properties in the clinical and statistical literature that if appropriately implemented during the design, conduct and analysis of clinical trial data, should mitigate the occurrence and thus the detrimental impact of missing data on trial outcome, and subsequent approval of potentially safe and efficacious new drugs. Yet a number of drugs with potential therapeutic benefit have failed to gain approval due to serious missing data issue. This roundtable discussion will probe into the challenges pose by missing data in clinical trials and the utility of current approaches in minimizing the non-approval chances of potentially efficacious new therapies. |
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TL31 Missing Data Handling |
09/17/13 |
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Chair(s): David Li, Pfizer; Jin Xu, Merck |
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Plenty methods have been used to handle the missing data problem. Some of them are based on the maximum likelihood function while others are based on multiple imputation; some of them are valid only when the data are missing at random; others may be more complicated but useful when the missing data are non-ignorable. None of these methods are universally best. Selecting a proper and most efficient method to analyze the data from your particular clinical trial takes a broad knowledge of the methods and an in-depth understanding of your data. How to put forth a convicing argument for the MAR assumption? How to select meaningful sensitivity analyses to validate the conclusion drawn from your primary analysis? This roundtable discussion will gather a group of statisticians to share their best practices and view points, and for all to learn from each other’s experiances. |
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TL32 A Comprehensive Review of Sample Size Determinations for the Wilcoxon-Mann-Whitney Test |
09/17/13 |
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Chair(s): Gary Lynn Kamer, FDA; Dewi (Gabriela) Rahardja, FDA |
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The determination of the required sample size for a clinical study is always an important step in assuring that the study results provide meaningful information. It's not a pleasant occurence when a statistically underpowered study results in a failure to establish a definitive answer to the question posed by the hypotheses. Money and time have been wasted. Sample size estimation is even more important when the less-than-definitive study results in not only the waste of time and money, but also in the failure to establish the marketability of a new medical device, for example. For most parametric statistical tests, the primary concerns are the selection of the anticipated rates or means and the choice of statistical parameters. Various easy to use sample size estimation programs are readily available. Also, reasonably simple sample size formulae usually exist. But, what if your study is expected to result in data which are not normally distributed? The Wilcoxon-Mann-Whitney Test is a powerful non-parametric test for which we will present sample size formulae for continuous data where data from two groups are available, for continuous data where only data from one group is available along with summary statistics from a second group, and for continuous data where only summary statistics are available for both groups. Also, we will present sample size formulae for ordinal data where those data satisfy the proportional odds assumption, and for where those data may not satisfy the proportional odds assumption. |
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TL33 Sample size estimation for multi-regional clinical trials |
09/17/13 |
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Chair(s): Kimberly M Cooper, Janssen Research & Development |
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Multi-regional clinical trials (MRCTs) have been widely used for increased cost and time efficiency of global new drug development. One challenge of these trials concerns the impact of ethnic factors on clinical outcome and treatment effect. The question becomes what sample size is sufficient to show consistency of results across a number of regions. Guidelines exist to assist in determining the necessary sample size, for example the two methods proposed by Japan’s Ministry of Health, Labour, and Welfare (MHLW), but often feasibility issues arise. Questions: What approach do you take in determining sample size requirements for a region? What are your experiences with regulatory agencies in acceptance or rejection of sample size determination methods? Do approaches vary depending on therapeutic area? |
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TL34 Assessment of Multiple Endpoints in Phase 3 Preventive Vaccine Clinical Trials |
09/17/13 |
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Chair(s): Karen Lynn Goldenthal, Bethesda Biologics Consulting, LLC; Amelia Dale Horne, Division of Biostatistics/OBE/CBER/FDA |
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Multiple endpoints are often needed to adequately characterize the effectiveness of a product. There may be multiple primary as well as secondary endpoints, and these should be clearly defined in the protocol, with appropriate plans for Type 1 error control. This roundtable session will cover these issues for Phase 3 trials of preventive vaccines for infectious disease indications. Specifically, the focus will be immunogenicity and efficacy endpoints intended to support product labeling claims. (a) One example for discussion will be a trial with multiple immune response endpoints, all of which are important for labeling or related regulatory purposes. Sources of multiplicity for vaccine immunogenicity trials may include the assessment of combination vaccines, simultaneous administration of the new vaccine with previously licensed vaccines, clinical lot consistency, etc. (b) Another example for discussion will be a vaccine efficacy trial with multiple primary and secondary clinical disease endpoints. (c) In addition, attendees are encouraged to share their perspectives about issues regarding Type 1 error control for multiple endpoints in vaccine trials. |
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TL35 Non-inferiority studies for both human and veterinary medicine |
09/17/13 |
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Chair(s): Anna Nevius, FDA/CVM |
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Non-inferiority studies represent an important class of designs for studying efficacy in the drug approval process. Many of the design issues are the same for human and animal medicine. However, veterinary medicine presents unique challenges including the choice of a control group, sample size, experimental unit, and choice of primary variable endpoint. The implications of related guidance from FDA and other institutions will be discussed. |
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TL36 Analyzing very large retrospective claims databases |
09/17/13 |
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Chair(s): C V Damaraju, Janssen Research and Development, LLC |
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Availability of rapidly growing insurance and medical claims databases promise to offer unique insights into post-marketed experience with regulated products. While standard data mining techniques for broad statistical summaries are useful, analyses involving models to better understand the relationships between treatment exposure and outcomes require proper specification to avoid inherent biases and confounding. In this discussion, we will exchange ideas and experience about analyzing large observational datasets, common pitfalls in model specifications and best practices used. Two main questions are as follows - 1) What specific considerations would you take into account when developing a statistcal analysis plan? 2) How do you plan to use results obtained from observational analyses in comparing with clinical studies in terms of key rates or statistics? |
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TL37 Pharmacokinetic Dose Proportionality studies |
09/17/13 |
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Chair(s): Jaya Natarajan, Janssen Research & Development LLC |
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Pharmacokinetic (PK) dose proportionality (DP) is a desired property of a compound. Thus, evaluating DP in a properly designed study is a crucial part of drug development. However, in the absence of a regulatory guidance, the analysis of data from a DP study varies significantly between sponsors, ranging from descriptive statistics and graphical evaluation to mixed effects modeling. Some of the statistical techniques that have been used are: (1) ANOVA modeling of dose-normalized PK parameters and Bioequivalence testing between each pair of doses; (2) Linear regression modeling of PK parameters vs. dose where intercept = 0 for DP; (3) Power model or log-linear modeling of logarithm of PK parameter vs. logarithm of dose where slope = 0 for DP.1 Bioequivalence criterion can also be applied to regression models to yield a corresponding criterion on the slope of regression line.2 In crossover DP studies, the subject-specific random effect terms (subject-specific random intercept and random slope) can also be added to the regression model. But, with limited number of doses studied (3 or 4), the model may not always converge. Following are the questions for discussion: 1. In your company, is dose proportionality evaluated only from early development studies? Do you conduct a separate pharmacokinetic dose proportionality study for most compounds? 2. What are the study designs is utilized for DP studies? Parallel group, crossover, single-sequence? 3. How is the sample size determined? Estimation (precision) approach or hypothesis testing (power) approach? 4. What is the analysis methodology? (a) Descriptive statistics and graphs only; (b) Hypothesis testing? If hypothesis testing, what is the model used for analysis? 5. What are the pros and cons for each analysis method? 6. Do you use random intercept, random slope model for analyzing data from crossover studies? What are the issues with convergence of the model? Reference: 1. Chow and Liu: Design and Analysis of Bioavailability and Bioequivalence Studies, Chapman &Hall/CRC, 2009. 2. Smith Bp, et.al. Confidence Interval Criteria for Assessment of Dose Proportionality. Pharmaceutical Research, vol. 17, pp 1278 - 1283. |
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TL38 Vaccine Antigen Overages |
09/17/13 |
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Chair(s): Louis George Luempert, Novartis Animal Health |
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All vaccines should be released at a titer high enough to assure that the last vaccination is sufficiently potent to meet the proposed claim (protection, etc). Methods are being developed to assure that vaccinates will receive a dose that is at or above the Minimum Protective Dose for live vaccines. In order to assure that live vaccine products will be released at a titer that takes into consideration vial/assay variation (termed ‘Overage 1’) and live titer loss throughout dating (termed ‘Overage 2’) two methods are being considered: 1) a 2s or 3s overage based on initial vial/assay variation, and 2) Replacing Overage 1 and Overage 2 with a single overage that ensures a sufficient proportion of the population of doses in a serial are above the MPD throughout the dating period. This would eliminate the need to characterize specific components of the required antigen overage. |
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TL39 Safety Evaluation During Clinical Drug Development – Size and Extent of Population Exposure |
09/17/13 |
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Chair(s): Bradley McEvoy, CDER, FDA; Elena Polverejan, Janssen R&D, Johnson & Johnson |
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The ICH E1 guideline provides a set of principles for the development of a sufficiently large safety database for drugs that are given for more than six months for non-life-threatening diseases. The guideline suggests 300-600 patients treated for six months, 100 patients treated for one year, with a total number of 1500 patients exposed to the drug, including short-term exposure. The guideline recognizes a few exceptions to these rules, such as when there is concern the drug will cause late developing adverse events or there is need to quantify the occurrence rate of an expected low-frequency adverse event. These exceptions require a larger or longer-term safety database, employing the use of various statistical methodologies and corresponding criteria. The participants will share their views and experience on these topics. Key Discussion Questions: - What statistical criteria are most commonly used to determine the size or the extent of exposure of a safety database? - Under what circumstances could interim results pertaining to a long-term safety database be used for a submission? When are post-approval commitments possible? |
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TL40 Challenges in Post-Marketing Safety Assessment by Using Spontaneous Reports |
09/17/13 |
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Chair(s): Brent Burger, Cytel; Rongmei Zhang, FDA |
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Post-marketing safety assessment provides essential information in characterizing a drug’s safety profile as pre-marketing studies are often underpowered to detect rare and possibly serious adverse events. Spontaneous reports systems, such as FDA Adverse Event Reporting System (FAERS, formerly AERS) or Poison Control Centers, are two separate datastreams that allow the evaluation of drug safety after the drug has been approved. In the case of FAERS, the challenge is that while a large number of records for suspected adverse drug reactions exists, the data have numerous limitations including substantial underreporting and potential misclassifications. In this roundtable, we are going to discuss different statistical approaches to formalize the signal generation from spontaneous reports. Key Discussion Questions: - For the numerator-based data such as FAERS, what are traditional approaches? What Bayesian approaches (or other newer methods) are available and what are their advantages compared with traditional approaches? - When denominators are available such as the number of prescriptions for each drug, can and should this additional information be used in the analysis of spontaneous report? If so, what statistical approaches can be used? |
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TL41 Some Challenges of Using Open-Source Software in a Regulatory Environment |
09/17/13 |
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Chair(s): Jae Brodsky, FDA |
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Open source software offers affordable alternatives to commonly used proprietary tools in both regulatory agencies and industry. We will discuss how open source software can aid both FDA and industry statisticians, review some of the open source software that is already in use at the FDA, and discuss how industry statisticians can use open source software in submissions to the FDA. As a case example, R is often used as an alternative to proprietary statistical analysis software such as SAS, in industry, academia, and at the FDA. We will discuss our experiences using R at the FDA. |
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TL42 Effective Use of Table, Figure and Listing in the Study Report of a Clinical Trial |
09/17/13 |
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Chair(s): Wei Wang, Eli Lilly and Company |
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Given that the cost of running clinical trials continues to rise, various efforts have been made to reduce this cost. One such area explored has been the reduction of the number of TFLs in study reports. As statistical leaders, we may be able to reduce the number of Tables, Figures and Listings (TFLs) and thereby streamline the relevant information and improve the way data is presented in the study report. 1. On average, how do you feel the amount of TFLs generated for the study reports in your company? Is it too much? About right? 2. What can statisticians do to reduce the amount of TFLs and deliver a more concise and more informative study report? Will (interactive) review tools help reduce the TFLs? |
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TL43 Statistical Analysis Plan (SAP) or Robust Statistical Methods - What does the FDA need for Protocol Evaluation |
09/17/13 |
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Chair(s): Janet Elizabeth McDougall, McDougall Scientific Ltd. |
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A well written and robust statistical methods section is required for a protocol to be fairly evaluated by Regulatory (e.g. FDA), local Ethics Review Boards and the investigational team. Recently this has been translated into requiring the SAP be part of the IND (or CTA in Canada) submission. Is it time for a guidance (even ICH) on the contents of the statistical methods for a protocol and the statistical analysis plan? The SAP, signed prior to data unblinding, is an essential document reflecting the agreed to contents of the protocol and knowledge gained (e.g. blinded data review, emerging analysis trends) across the life of the trial. Providing a draft SAP prior to the acceptance of the protocol can diminish the role of both the statistical methods section in the protocol and the SAP. - Agree or Disagree. |
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TL44 Development of Statistical Analysis Plans (SAPs) in Observational Studies |
09/17/13 |
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Chair(s): Ari D Marcus, Quintiles Outcome |
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The SAP as a required element of clinical trials is well established and standardized. However, the importance of developing clear and detailed analysis plans for observational studies is less well understood. Observational studies contain unique challenges not addressed in randomized controlled trials that need to be addressed in the SAP (such as the lack of a prescribed study schedule, bias due to the non-randomized, uncontrolled nature of the study), and different statistical methods (with a focus on precision or prediction rather than hypothesis testing). This session starts with an illustration of the benefit of having clear, detailed SAPs in observation al studies, followed by a discussion of specific elements of the observational SAP that may differ from a SAP for clinical trials, timing of the SAP and considerations for SAP revisions. |
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TL45 Recent Developments in the Clinical Trials for Alzheimer’s Disease |
09/17/13 |
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Chair(s): Jingyu Luan, FDA; Chengjie Xiong, Washington University |
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Alzheimer’s Disease (AD), a progressive neurodegenerative disorder, is a common cognitive disorder of the elderly population. AD begins with memory loss and progresses to severe impairment of activities of daily living, leading to death approximately 8 years on average from time of diagnosis of dementia. The prevalence of AD in people over the age of 65 is 5-10%, increasing up to 50% in those over the age of 85. Alzheimer's Disease affected approximately 4.5 million people in the United States. It is predicted that 13.8 million people in the U.S. will have AD by 2050. Accumulating research evidence suggests that neurodegenerative processes associated with AD begin years prior to the symptomatic onset of AD when the disease is clinically at the early prodromal stage or even the latent stage. To date, there are no pharmaceutical treatments that reverse the pathological processes of AD. Hence, it will be critically important to design randomized clinical trials for individuals at the earliest clinical stages since targeted therapies may have the greatest chance of preserving normal brain function for this group of individuals. This roundtable will discuss the recent developments in the clinical trials for Alzheimer’s Disease. Key discussion questions: What are the challenges in the design and analysis of early and prodromal AD trials? What is the role of biomarkers in the design of early and prodromal AD trials? What cognitive and functional outcome measures should be used in early and prodromal AD trials? What is the current status of Dominantly Inherited Alzheimer’s Network (DIAN) and the DIAN trials? |
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TL46 Sensitivity Analyses of PFS in Oncology Trials |
09/17/13 |
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Chair(s): Biao Xing, Onyx Pharmaceuticals |
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In oncology trials with progression-free survival (PFS) endpoint, sensitivity analyses are typically required to demonstrate the robustness of the primary analysis results to potential alternative ways of assessing disease outcomes, censoring data, or conducting analyses. The FDA and EMEA have released guidance on designing and analyzing trials with PFS endpoint. Differences exist in the regulatory views as well as in the practices across the pharmaceutical industry. This roundtable will discuss the regulatory views, survey the industry practices, and share the lessons learned. |
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