ENAR Webinar Series (WebENARs)

Past Webinars


The Role of Statistics in Transforming EHR Data into Knowledge

Friday, June 19, 2020
10 a.m. to 12 p.m. Eastern

Rebecca Hubbard, PhD
Associate Professor of Biostatistics
University of Pennsylvania

Dr. Rebecca Hubbard is an Associate Professor of Biostatistics at the University of Pennsylvania. Her research focuses on development and application of statistical methods to improve the validity of analyses using real world data sources including electronic health records and claims data. These methods have been applied across a broad range of research areas including health services research, cancer epidemiology, aging and dementia, and pharmacoepidemiology.

The widespread adoption of electronic health records (EHR) as a means of documenting medical care has created a vast resource for research on health conditions, interventions, and outcomes. Informaticians have played a leading role in the process of extracting “real world data” from EHR, with statisticians playing a more peripheral part. However, statistical insights on study design and inference are key to drawing valid conclusions from this messy and incomplete data source. This webinar will describe the basic structure of EHR data, highlight key challenges to research arising from this data structure, and present an overview of some statistical methods that address these challenges. The discussion of issues related to the structure and quality of EHR data will include: data types and methods for extracting variables of interest; sources of missing data; error in covariates and outcomes extracted from EHR and claims data; and data capture considerations such as informative visit processes and medical records coding procedures. The overall goal of this webinar is to illustrate the unique contribution of statistics to the process of generating knowledge from EHR data and equip participants with some tools for doing so.

Purchase Webinar Recording (6/19/20)


Spatial Statistics for Disease Ecology

Friday, April 17. 2020
10 a.m. to 12 p.m. Eastern

Lance A. Waller, Ph.D.
Department of Biostatistics and Bioinformatics
Rollins School of Public Health
Emory University

Lance A. Waller, Ph.D. is a Professor in the Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University. He is a member of the National Academy of Science Board on Mathematical Sciences and Analytics and has served on National Academies Committees on applied and theoretical statistics, cancer near nuclear facilities, geographic assessments of exposures to Agent Orange, and standoff explosive technologies. His research involves the development of statistical methods for geographic data including applications in environmental justice, epidemiology, disease surveillance, spatial cluster detection, conservation biology, and disease ecology. His research appears in biostatistical, statistical, environmental health, and ecology journals and in the textbook Applied Spatial Statistics for Public Health Data (2004, Wiley). Dr. Waller has also lead two separate T32 training grants, one from NIGMS and the other from NIEHS, and served as the Director of the NHLBI Summer Institute for Research Training in Biostatistics (SIBS) site at Emory for the past 9 years.

The field of disease ecology involves exploration of the multiple, dynamic interactions between pathogens, hosts, and the environment that result in the transmission of disease. Many of these interactions involve spatial or spatiotemporal components that determine the course of an outbreak and may offer potential interventions to stem the extent and duration of an outbreak at the population level. In this webinar, we provide a brief overview of the field of disease ecology, the motivating questions of interest, the nature of data involved, and the interaction of statistical and mathematical modeling addressing these questions with available data. We offer two illustrations relating to the monitoring and analysis of zoonic disease, namely: identifying geographic drivers of the spread of raccoon rabies along the Eastern United States and utilizing environmental data to enhance animal surveillance for plague in California. Both examples utilize concepts and analytic tools from spatial statistics to better understand and monitor geographically-referenced zoonotic diseases in wild animal populations.

Purchase Webinar Recording (4/17/20)


Collaboration: Applications of RWE in drug development and methodologies for confounding control and A statistical roadmap for journey from real-world data to real-world evidence

Friday, February 28. 2020
10 a.m. to 12 p.m. Eastern

Hongwei Wang, PhD

Dr. Hongwei Wang is currently a Director of Global Medical Affairs Statistics, Data and Statistical Sciences at AbbVie. He held a PhD in Statistics from Rutgers University and his research interests include designing and analyzing real-world studies, network meta-analysis and advanced analytics. Before AbbVie, he worked at Merck and Sanofi.

Applications of RWE in Drug Development and Methodologies for Confounding Control: Real-world evidence (RWE) is playing an increasingly important role in drug development, from early in discovery throughout clinical development program to life-cycle management. RWE can augment randomized clinical trials for regulatory approval, establish the effectiveness and safety profile in routine clinical practice to support reimbursement decision, and constitute an integral part of scientific communication overall. Due to its noninterventional nature, a key challenge of robust RWE generation is to establish causal relationship between exposure and outcome. This talk focuses on several main methodologies for causal inference, consisting of IPTW, MLE, AIPTW, and TMLE using full data and matched data that is derived from propensity score matching, respectively. Following the RWE roadmap outlined in the first talk, practical considerations are given to facilitate the series of decisions for confounding control, such as defining estimand, usage of matching, and choice among different analytic frameworks.


Yixin Fang, PhD

Dr. Yixin Fang is director of Global Medical Affairs (GMA) Statistics at AbbVie. Since he joined AbbVie in January 2019, he has focused his research on real-world studies, comparative effectiveness research, and causal inference. After he received his PhD in statistics from Columbia University in 2006, he had been working in academia for 12 years, teaching young statisticians and doing research in different fields such as machine learning, high-dimensional data analysis, and big data analysis. Motivated by the research of Professor Mark van der Laan, he is promoting the applications of targeted learning in real-world data research, combining his experiences in both machine learning and causal inference.

A statistical roadmap for journey from real-world data to real-world evidence: Randomized controlled clinical trials (RCTs) are the gold standard for evaluating the safety and efficacy of pharmaceutical drugs, but in many cases their costs, duration, limited generalizability, and ethical or technical feasibility have caused some to look for real-world studies as alternatives. On the other hand, real-world data may be much less convincing due to the lack of randomization and the presence of confounding bias. In this presentation, we propose a statistical roadmap to translate real-world data (RWD) to robust real-world evidence (RWE). The roadmap consists of three main stations: (1) defining an estimand translating the research objective into a precise definition of the treatment effect that is to be estimated, (2) constructing an efficient estimator (minimum-variance unbiased estimator) for the estimation of the estimand, and (3) conducting sensitivity analysis to explore the robustness of the inference to deviation from the underlying no-unmeasured confounding assumption. The Food and Drug Administration (FDA) is working on guidelines, with a target to release a draft by 2021, to harmonize RWD applications and monitor the safety and effectiveness of pharmaceutical drugs using RWE. The proposed roadmap aligns with the newly released framework for FDA's RWE Program in December 2018 and we hope this statistical roadmap is useful for statisticians who are eager to embark on their journeys in the real-world research.

Purchase Webinar Recording (2/28/20)


The Central Role of Personalized Solution in the Era of Digital Health

Friday, January 10, 2020
10 a.m. to 12 p.m. Eastern

Haoda Fu
University of Wisconsin, Madison
Enterprise Lead of Machine Learning and Artificial Intelligence Team

Dr. Haoda Fu is a senior research advisor and a enterprise lead for Machine Learning, Artificial Intelligence, and Digital Connected Care from Eli Lilly and Company. Dr. Haoda Fu is a Fellow of ASA (American Statistical Association). He is also an adjunct professor of biostatistics department, Indiana university school of medicine. Dr. Fu received his Ph.D. in statistics from University of Wisconsin - Madison in 2007 and joined Lilly after that. Since he joined Lilly, he is very active in statistics methodology research. He has more than 90 publications in the areas, such as Bayesian adaptive design, survival analysis, recurrent event modeling, personalized medicine, indirect and mixed treatment comparison, joint modeling, Bayesian decision making, and rare events analysis. In recent years, his research area focuses on machine learning and artificial intelligence. His research has been published in various top journals including JASA, JRSS, Biometrics, ACM, IEEE, JAMA, Annals of Internal Medicine etc.. He has been teaching topics of machine learning and AI in large industry conferences including teaching this topic in FDA workshop. He was board of directors for statistics organizations and program chairs, committee chairs such as ICSA, ENAR, and ASA Biopharm session.

Digital health is an important pharmaceutical industry trend in recent years, and it can bring significant disruptive innovation to transform healthcare industry. In this talk, we will provide an introduction on digital health and associated analytics challenges and opportunities. In particular, we will focus on the central role of personalized intervention in the era of digital health.

Purchase Webinar Recording (1/10/20)


New Statistical Learning Methods for Optimizing Dynamic Treatment Decision Rules Leading Toward Personalized Health Care

Friday, December 6, 2019
10 a.m. to 12 p.m. Eastern

Lu Wang
Department of Biostatistics
University of Michigan

Dr. Lu Wang is Associate Professor of Biostatistics at the University of Michigan, Ann Arbor, Associate Editor for the Journal of the American Statistical Association. She received her Ph.D. in Biostatistics from Harvard University in 2008 and joined the faculty at the University of Michigan in the same year. Dr. Wang's research focuses on statistical methods for evaluating dynamic treatment regimes, personalized health care, nonparametric and semiparametric regressions, missing data analysis, functional data analysis, and longitudinal (correlated/clustered) data analysis. She has been collaborating with investigators at M.D. Anderson Cancer Center, University of Michigan Medical School, and Harvard School of Public Health during the past 12 years.

In this talk, we present recent advances and statistical developments for evaluating Dynamic Treatment Regimes (DTR), which allow the treatment to be dynamically tailored according to evolving subject-level data. Identification of an optimal DTR is a key component for precision medicine and personalized health care. Specific topics covered in this talk include several recent projects with robust and flexible methods developed for the above research area. We will first introduce a dynamic statistical learning method, adaptive contrast weighted learning (ACWL), which combines doubly robust semiparametric regression estimators with flexible machine learning methods. We will further develop a tree-based reinforcement learning (T-RL) method, which builds an unsupervised decision tree that maintains the nature of batch-mode reinforcement learning. Unlike ACWL, T-RL handles the optimization problem with multiple treatment comparisons directly through a purity measure constructed with augmented inverse probability weighted estimators. T-RL is robust, efficient and easy to interpret for the identification of optimal DTRs. However, ACWL seems more robust against tree-type misspecification than T-RL when the true optimal DTR is non-tree-type. At the end of this talk, we will also present a new Stochastic-Tree Search method called ST-RL for evaluating optimal DTRs.

Purchase Webinar Recording (12/6/19)


Subgroup Identification for Differential Treatment Effects

Friday, November 8, 2019
10 a.m. to 12 p.m. Eastern

Wei-Yin Loh
Department of Statistics
University of Wisconsin, Madison

Wei-Yin Loh is Professor of Statistics at the University of Wisconsin, Madison. He received his PhD from Berkeley in 1982. His major research interests are in bootstrap methods and classification and regression trees. He is a fellow of the American Statistical Association and the Institute of Mathematical Statistics and a consultant to government and industry.

Many subgroup identification methods exist but they have not been compared together. To better understand the relative strengths and weaknesses of the methods, we briefly review those with publicly available software (FindIt, GUIDE, Interaction Trees, MOB, Outcome Weighted Estimation, PRIM, ROWSi, Sequential Batting, SIDES, and Virtual Twins) and then compare their performance on seven criteria: (i) variable selection bias, (ii) probability of false discovery, (iii) probability of correct variable identification, (iv) bias in subgroup treatment effect estimates, (v) expected subgroup size, (vi) expected size of subgroup treatment effects, and (vii) subgroup stability. We conclude with a bootstrap solution to performing post-selection inference on the selected subgroups.

Purchase Webinar Recording (11/8/19)


Lessons and Strategies for a Career in Academia: A Conversation

Friday, December 14, 2018
10:00 am – 12:00 pm Eastern

Dr. Leslie McClure
Professor & Chair, Department of Epidemiology and Biostatistics
Dornsife School of Public Health at Drexel University

Dr. Elizabeth Stuart
Associate Dean for Education and Professor of Biostatistics, Mental Health, and Health Policy and Management
Johns Hopkins Bloomberg School of Public Health

As a Biostatistician, there are many paths to a successful career. Each has benefits and drawbacks and will depend on an individual's own skills and preferences. In this webinar, Drs. Elizabeth Stuart and Leslie McClure will host a conversation about their academic careers, including providing some strategies for success and describing some of the challenges they've faced. They'll consider important questions, such as: What to look for in a job? How to develop meaningful collaborations (and get out of those that are not productive)? How to prioritize activities with an eye towards promotion (e.g., collaborative and methodological projects)? How to balance teaching, research, and grant requirements? And how to balance all of that with things outside of work? However, the exact direction of the conversation will depend on the questions and engagement from webinar participants.

Purchase Webinar Recording (12/14/18)


Machine Learning for Health Care Policy

Friday, November 30, 2018
10:00 am – 12:00 pm Eastern

Dr. Sherri Rose
Associate Professor of Health Care Policy (Biostatistics)
Harvard Medical School

Health care research is moving toward analytic systems that take large health databases and estimate quantities of interest both quickly and robustly, incorporating advances from statistics, machine learning, and computer science. Pressing questions in prediction and causal inference are being answered with machine learning techniques. I will give an overview of the specific challenges related to developing and deploying these statistical algorithms for health policy, including examples from the areas of health plan payment, mental health outcomes, cancer staging, and medical devices. This webinar will be accessible for graduate students with most technical derivations provided in references

Purchase Webinar Recording (11/30/18)


Biostatistical Methods for Wearable and Implantable Technology

Friday, October 26, 2018
10:00 am – 12:00 pm Eastern

Dr. Ciprian Crainiceanu
Professor, Department of Biostatistics
Johns Hopkins University

Wearable and Implantable Technology (WIT) is rapidly changing the Biostatistics data analytic landscape due to their reduced bias and measurement error as well as to the sheer size and complexity of the signals. In this talk I will review some of the most used and useful sensors in Health Sciences and the ever expanding WIT analytic environment. I will describe the use of WIT sensors including accelerometers, heart monitors, glucose monitors and their combination with ecological momentary assessment (EMA). This rapidly expanding data eco-system is characterized by multivariate densely sampled time series with complex and highly non-stationary structures. I will introduce an array of scientific problems that can be answered using WIT and I will describe methods designed to analyze the WIT data from the micro- (sub-second-level) to the macro-scale (minute-, hour- or day-level) data.

Purchase Webinar Recording (10/26/18)


Sensitivity analysis in observational research: introducing the E-value

Friday, September 28, 2018
10:00 am – 12:00 pm Eastern

Dr. Tyler VanderWeele
Professor of Epidemiology
Harvard School of Public Health

Sensitivity analysis is useful in assessing how robust an association is to potential unmeasured or uncontrolled confounding. This webinar introduces a new measure called the "E-value," which is related to the evidence for causality in observational studies that are potentially subject to confounding. The E-value is defined as the minimum strength of association, on the risk ratio scale, that an unmeasured confounder would need to have with both the treatment and the outcome to fully explain away a specific treatment–outcome association, conditional on the measured covariates. A large E-value implies that considerable unmeasured confounding would be needed to explain away an effect estimate. A small E-value implies little unmeasured confounding would be needed to explain away an effect estimate. The speaker and his collaborators propose that in all observational studies intended to produce evidence for causality, the E-value be reported or some other sensitivity analysis be used. They suggest calculating the E-value for both the observed association estimate (after adjustments for measured confounders) and the limit of the confidence interval closest to the null. If this were to become standard practice, the ability of the scientific community to assess evidence from observational studies would improve considerably, and ultimately, science would be strengthened.

Reference: VanderWeele, T.J. and Ding, P. (2017). Sensitivity analysis in observational research: introducing the E-value. Annals of Internal Medicine, 167:268-274.
Online E-value Calculator: https://mmathur.shinyapps.io/evalue/

Purchase Webinar Recording (9/28/2018)


Evidence Synthesis for Clinical trials: Use of Historical Data and Extrapolation

Friday, June 22, 2018
10:00 am – 12:00 pm Eastern

Sebastian Weber
Associate Director SMC
Novartis Pharma AG

Satrajit Roychoudhury
Senior Director
Pfizer Inc.

A Bayesian approach provides the formal framework to incorporate external information into the statistical analysis of a clinical trial. There is an intrinsic interest of leveraging all available information for an efficient design and analysis of clinical trials. The use of external data in trials are nowadays used in earlier phases of drug development (Trippa, Rosnerand Muller, 2012; French, Thomas and Wang, 2012; Hueber et al., 2012), occasionally in phase III trials (French et al., 2012), and also in special areas such as medical devices (FDA, 2010a), orphan indications (Dupont and Van Wilder, 2011) and extrapolation in pediatric studies (Berry, 1989). This allows trials with smaller sample size or with unequal randomization (more subjects on treatment than control). In addition, the Bayesian statistical paradigm is a natural approach for combining information across heterogeneous sources, such as different trials or the adult and pediatric data. In this webinar we'll provide a statistical framework to incorporate trial external evidence with real life examples.

During the first part of the webENAR we will introduce the meta-analytic predictive (MAP) model (Neuenschwander, 2010). The MAP model is a Bayesian hierarchical model which combines the evidence from different sources (usually studies). The MAP model provides a prediction for a future study based on available information while accounting for inherent heterogeneity in the data. This approach can be used widely in different applications of biostatistics.

In the second part of the webENAR we will focus on three key applications of the MAP approach in biostatistics, which are (i) the derivation of informative priors from historical controls, (ii) probability of success and (iii) extrapolation from adult data to pediatrics. These applications will be demonstrated using the R package RBesT, the R Bayesian evidence synthesis tools, which are freely available from CRAN. The aim of the webinar is to teach the MAP approach and enable participants to apply the approach themselves with the help of RBesT.

Purchase Webinar Recording (6/22/2018)


Programming with hierarchical statistical models: An introduction to the BUGS-compatible NIMBLE system for MCMC and more

Friday, April 13, 2018
11:00 am – 1:00 pm Eastern

Chris Paciorek
Adjunct Professor, Statistical Computing Consultant
Department of Statistics
University of California, Berkeley

This webinar will introduce attendees to the NIMBLE system for programming with hierarchical models in R. NIMBLE
(r-nimble.org) is a system for flexible programming and dissemination of algorithms that builds on the BUGS language for declaring hierarchical models. NIMBLE provides analysts with a flexible system for using MCMC, sequential Monte Carlo and other techniques on user-specified models. It provides developers and methodologists with the ability to write algorithms in an R-like syntax that can be easily disseminated to users. C++ versions of models and algorithms are created for speed, but these are manipulated from R without any need for analysts or algorithm developers to program in C++.

While analysts can use NIMBLE as a drop-in replacement for WinBUGS or JAGS, NIMBLE provides greatly enhanced functionality in a number of ways. The webinar will first show how to specify a hierarchical statistical model using BUGS syntax (including user-defined function and distributions) and fit that model using MCMC (including user customization for better performance). We will demonstrate the use of NIMBLE for biostatistical methods such as semiparametric random effects models and clustering models. We will close with a discussion of how to use the system to write algorithms for use with hierarchical models, including building and disseminating your own methods.

Purchase Webinar Recording (4/13/2018)


Incorporating Patient Preferences into Regulatory Decision Making

Friday, December 1, 2017
11:00 am - 1:00 pm Eastern

Telba Irony, PhD
Center for Biologics Evaluation and Research

Regulatory authorities and patient advocacy groups have been paving the way towards engaging patients in medical product development and regulatory review. These efforts gave rise and relevance to the development of the Science of Patient Input, or SPI. SPI consists of scientifically valid qualitative and quantitative methods for capturing patient perspective information to incorporate it into product development and regulatory decision making. Two types of patient input, Patient Reported Outcomes (PRO) and Patient Preference Information (PPI) are expected to be captured in accordance with applicable scientific and statistical standards and best practices, and statisticians have a large role to play.

A PRO is a measurement based on a report of a patient’s health status that comes directly from the patient, without interpretation of the patient’s report by a clinician or anyone else. Some symptoms or other unobservable concepts known only to the patient, such as pain or fatigue, can only be measured by PRO measures.

PPI is a patient’s expression of desirability or value of one course of action or selection in contrast to others. It focuses on assessing the importance, or preferences, that patients place on the benefits, harms and other aspects of treatments.

In this Webinar we will introduce key elements concerning elicitation and use of patient preferences (PPI) to inform regulatory decision making. As an example, we will present a study commissioned by the FDA to elicit obese patients’ preferences in choosing weight-loss devices and show how these preferences can be used to inform regulatory decision making. We will describe the weight-loss device survey and present the survey results, which have been used to develop a decision-aid tool for regulatory reviewers. The tool provides estimates of patients’ benefit-risk tradeoff preferences and also stratifies patients according to their risk-tolerance. We will conclude the Webinar by sharing experiences in using patient preferences in the regulatory process and talking about best statistical practices for eliciting and using patient preference information.

Purchase Webinar Recording (12/1/17)


Basket Trials for Development of Targeted Treatment Regimens

Friday, November 17, 2017
11:00 am – 1:00 pm Eastern

Dr. Mithat Gönen
Chief, Biostatistics Service
Memorial Sloan Kettering Cancer Center

Cancer clinical trials have traditionally been designed specific to a disease site (breast, lung, colon etc). This paradigm is being challenged by the advent of targeted treatments, regimens targeting molecular alterations in cancer cells. Since targeted treatments are not site-specific the trials evaluating them increasingly include multiple sites where the target is expressed. These trials are often called basket trials. In this WebENAR we will present several possible designs for basket trials: parallel design, aggregation design and hierarchical model-based design; comparing their operating characteristics, strengths and weaknesses. Although their applications have mostly been in oncology so far, basket trials can be used in any disease where targeted treatments can be used in molecularly defined subgroups. We will give examples of publicly available software that can be used to design and analyze basket trials.

Purchase Webinar Recording (11/20/17)


Multi-state Models: Methods and Software

Friday, October 20, 2017
10:00 am – 12:00 pm Eastern

Dr. Christopher Jackson
Senior Statistician, MRC Biostatistics Unit, University of Cambridge
School of Clinical Medicine, Cambridge Institute of Public Health

Multi-state models are stochastic processes which describe how an individual moves between a set of discrete states in continuous time. They have been used for two broad classes of data. Firstly, for "panel data": intermittent observations of the state at a finite series of times, for a set of individuals, where transition times are not known. Secondly, for times to multiple events for a set of individuals, so that the state at any time is known. Combinations or slight variants of these two data types are also possible.

Purchase Webinar Recording (10/20/17)


Data-driven Decision Making with Application to Precision Medicine

Friday, June 30, 2017
10:00 am – 12:00 pm Eastern

Dr. Eric Laber
Associate Professor
North Carolina State University

In this webinar we will cover the methodological and computational underpinnings of data-driven decision making with application to precision medicine. Planned topics covered include: (i) formalizing optimal decision making through potential outcomes; (ii) regression and classification-based methods for single-stage decision problems; (iii) approximate dynamic programming and direct-search methods for multi-stage decision problems; (iv) interpretability and the research-practice gap; and (v) current research topics and open problems. We do not assume that participants have any prior exposure to these topics; however, it is assumed that participants have at least a masters-level understanding of biostatistics.

Purchase Webinar Recording (6/30/17)


The Overlap between Statisticians and Pharmacometricians in Clinical Drug Development and a Case Study

Friday, May 19, 2017
10:00 am – 12:00 pm Eastern

Kenneth G. Kowalski, MS
Kowalski PMetrics Consulting, LLC

Wenping Wang, PhD
Novartis Pharmaceuticals Corporation

This WebENAR will be presented in two parts. The first part will focus on a commentary presented by Ken Kowalski discussing the overlap between statisticians and pharmacometricians working in clinical drug development. Individuals with training in various academic disciplines including pharmacokinetics, pharmacology, engineering and statistics, to name a few, have pursued careers as pharmacometricians. While pharmcometrics has benefitted greatly from advances in statistical methodology, there is considerable tension and skepticism between biostatisticians and pharmacometricians as they apply their expertise to drug development applications. This talk explores some of the root causes for this tension and provides some suggestions for improving collaborations between statisticians and pharmcometricians. The talk concludes with a plea for more statisticians to consider careers as pharmacometrics practitioners. The second part of the WebENAR will highlight a case study presented by Wenping Wang illustrating the application of pharmacokinetic-pharmacodynamic modeling of the time to first flare to support dose justification of Canakinumab in a sBLA submission. The case study will conclude with some observations regarding team interactions between statisticians and pharmacometricians that resulted in a successful sBLA submission.

Purchase Webinar Recording (5/19/17)


Evaluation and Use of Surrogate Markers

Friday, April 21, 2017
11:00 am – 1:00 pm Eastern

Dr. Layla Parast
RAND Corporation

The use of surrogate markers to estimate and test for a treatment effect has been an area of popular research. Given the long follow-up periods that are often required for treatment or intervention studies, appropriate use of surrogate marker information has the potential to decrease required follow-up time. However, previous studies have shown that using inadequate markers or making inappropriate assumptions about the relationship between the primary outcome and the surrogate marker can lead to inaccurate conclusions regarding the treatment effect. Many of the available methods for identifying, validating and using surrogate markers to test for a treatment effect tend to rely on restrictive model assumptions and/or focus on uncensored outcomes. In this course, I will describe different approaches to quantify the proportion of treatment effect explained by surrogate marker information in both a non-survival outcome setting and censored survival outcome setting. One described approach will be a nonparametric method that can accommodate a setting where individuals may experience the primary outcome before the surrogate marker is measured. I will illustrate the procedures using an R package available on CRAN to examine potential surrogate markers for diabetes with data from the Diabetes Prevention Program.

Purchase Webinar Recording (4/21/17)


How Credible are Your Conclusions about Treatment Effect When There are Missing Data? Sensitivity Analyses for Time-to-event and Recurrent-event Outcomes

Friday, February 24, 2017
10:00 am – 12:00 pm Eastern

Dr. Michael O'Kelly
Dr. Bohdana Ratitch
Dr. Ilya Lipkovich
Center for Statistics in Drug Development

Most experiments have missing data. When there are missing data, it is useful to provide sensitivity analyses to allow the reader of the account of the research to assess the robustness to the missing data of any conclusions made. Using the pattern-mixture framework, a variety of assumptions can be implemented with regard to categories of missing outcomes. Assumptions that would tend to undermine the alternative hypothesis can be especially useful for assessing robustness of conclusions. Multiple imputation (MI) is one quite straightforward way of implementing such pattern-mixture approaches. While MI is a standard tool for continuous outcomes, recently researchers have come up with ways of implementing MI for other outcomes, such as time-to-event and recurrent-event outcomes. This webinar describes a number of these new applications of the MI idea. The strengths and weaknesses of these approaches are described and illustrated via examples and simulations.

This webinar was not recorded and is not available for on-demand purchase.


Introduction to Clinical Trial Optimization

Friday, February 3, 2017
10:00 am – 12:00 pm Eastern

Dr. Alex Dmitrienko
Founder & President
Mediana Inc.

This webinar focuses on a broad class of statistical problems related to optimizing the design and analysis of Phase II and III trials (Dmitrienko and Pulkstenis, 2017). This general topic has attracted much attention across the clinical trial community due to increasing pressure to reduce implementation costs and shorten timelines in individual trials and development programs.

The Clinical Scenario Evaluation (CSE) framework (Benda et al., 2010) will be described to formulate a general approach to clinical trial optimization and decision-making. Using the CSE approach, main objectives of clinical trial optimization will be formulated, including selection of clinically relevant optimization criteria, identification of sets of optimal and nearly optimal values of the parameters of interest, and sensitivity assessments. Key principles of clinical trial optimization will be illustrated using a problem of identifying efficient and robust multiplicity adjustment strategies in late-stage trials (Dmitrienko et al., 2009; Dmitrienko, D’Agostino and Huque, 2013; Dmitrienko, Paux and Brechenmacher, 2015).

Software tools for applying optimization methods will be presented, including R software (Mediana package) and Windows applications with a graphical user interface (MedianaFixedDesign application).

Purchase Webinar Recording (2/3/17)


Design and Analysis of Genome-Wide Association Studies

Friday, December 9, 2016
10:00 am – 12:00 pm Eastern

Dr. Nilanjan Chatterjee
Bloomberg Distinguished Professor
Department of Biostatistics, Bloomberg School of Public Health
Department of Oncology, School of Medicine
Johns Hopkins University

Decreasing cost of large scale genotyping and sequencing technologies is fuelling investigation of association between complex traits and genetic variants across the whole genome using studies of massive sample sizes. Recent genome-wide association studies (GWAS) focused on common variants have already led to the discoveries of thousands of genetic loci across variety of complex traits, including chronic diseases such as cancers, heart diseases and type-2 diabetes. Future studies of less common and rare variants hold further promise for discovery of new genetic loci and better understanding of causal mechanisms underlying existing loci. The webinar will provide brief review of some state of the art design and analysis issues faced in the field. The topics will include sample size requirement and power calculations, methods for single- and multi-marker association testing, estimation of heritability and effect-size distribution, techniques for pleiotropic and Mendelian randomization analyses and genetic risk prediction.

Purchase Webinar Recording (12/9/16)


Nonparametric Bayes Biostatistics

Friday, October 28, 2016
10:00 am – 12:00 pm Eastern

Dr. David Dunson
Arts & Sciences Professor of Statistical Science, Mathematics and Electrical & Computer Engineering
Duke University

This webinar will provide an introduction to the practical use of nonparametric Bayesian methods in the analysis and interpretation of data from biomedical studies. I will start with a very brief review of the Bayesian paradigm, rapidly leading into what is meant by "Nonparametric Bayes." I'll then describe some canonical nonparametric Bayes models, including Dirichlet process mixtures and Gaussian processes. Basic practical properties and approaches for computation will be sketched, and I'll provide a practical motivation through some biomedical applications ranging from genomics to epidemiology to neuroscience. I'll finish up by describing some possibilities in terms of more advanced models that allow the density of a response variable to change flexibly with predictors, while providing practical motivation and implementation details.


Pragmatic Trials in Public Health and Medicine

Friday, May 20, 2016
11:00 am- 1:00 pm Eastern

David M. Murray, Ph.D.
Associate Director for Prevention
Director, Office of Disease Prevention
Office of the Director
National Institutes of Health

This webinar will review key issues and their solutions for pragmatic trials in public health and medicine. Pragmatic trials are used increasingly in health care settings to help clinicians choose between options for care. They often involve group- or cluster-randomization, though alternatives to randomized trials are also available. Many current trials rely upon electronic health records as the major source for data. These studies face a variety of challenges in the development and delivery of their interventions, research design, informed consent, data collection, and data analysis. This webinar will review these issues both generally and using examples from the Health Care Systems Collaboratory. The HCS Collaboratory is an NIH funded consortium of nine pragmatic trials that address a variety of health issues and outcomes, all conducted within health care systems, all relying on electronic health records as their primary source of data, with most implemented as a group- or cluster-randomized trial.


Analytic Methods for Functional Neuroimaging Data

Friday, April 15, 2016
10:00 am- 12:00 pm Eastern

F. DuBois Bowman
Dr. Daniel Drake
Dr. Ben Cassidy
Department of Biostatistics, Mailman School of Public Health
Columbia University

Brain imaging scanners collect detailed information on brain function and various aspects of brain structure. When used as a research tool, imaging enables studies to investigate brain function related to emotion, cognition, language, memory, and responses to numerous other external stimuli, as well as resting-state brain function. Brain imaging studies also attempt to determine the functional or structural basis for psychiatric or neurological disorders and to examine the responses of these disorders to treatment. Neuroimaging data, particularly functional images, are massive and exhibit complex patterns of temporal and spatial dependence, which pose analytic challenges. There is a critical need for statisticians to establish rigorous methods to extract information and to quantify evidence for formal inferences. In this webinar, I briefly provide background on various types of neuroimaging data (with an emphasis on functional data) and analysis objectives that are commonly targeted in the field. I also present a survey of existing methods aimed at these objectives and identify particular areas offering opportunities for future statistical contribution.


Bayesian Population Projections

Friday, February 12, 2016
10:00 am - 12:00 pm Eastern

Adrian E. Raftery
Professor of Statistics and Sociology
University of Washington

Projections of countries' future populations, broken down by age and sex, are widely used for planning and research. They are mostly done deterministically, but there is a widespread need for probabilistic projections. I will describe a Bayesian statistical method for probabilistic population projections for all countries. These new methods have been used by the United Nations to produce their most recent population projections for all countries.


Regulatory Perspective on Subgroup Analysis in Clinical Trials

December 4, 2015
10:00 am - 12:00 pm Eastern

Dr. Mohamed Alosh & Dr. Kathleen Fritsch
Division of Biometrics III, Office of Biostatistics, OTS, CDER, FDA

For a confirmatory clinical trial that established treatment efficacy in the overall population, subgroup analysis aims to investigate the extent of benefits from the therapy for the major subgroups. Consequently, findings from the subgroup analysis play a major role in interpreting the trial results. This presentation focuses on two areas related to subgroup analysis in a confirmatory clinical trial: (i) investigating consistency of treatment effect across subgroups, and (ii) designing a clinical trial with the objective of establishing treatment efficacy in a targeted subgroup in addition to the overall population. The presentation also outlines the regulatory guidelines for subgroup analysis in such trials and provides examples of clinical trials where subgroup analysis played a role in determining the population for treatment use.


Reproducible Research: The Time is Now

Friday, November 20, 2015
10:00 am - 12:00 pm Eastern

Dr. Keith Baggerly
The University of Texas MD Anderson Cancer Center

The buzz phrase "Reproducible Research" refers to studies where the raw data and code supplied are enough to let a new investigator exactly match the reported results without a huge amount of effort. "Replicable Research" refers to studies whose methods, when applied to new data, give rise to qualitatively similar results. Particularly as experiments get bigger, more involved, and more expensive, reproducibility should precede replication. Unfortunately, more attention is now being focused on such issues due to some high-profile failures.

In this talk, we first illustrate the issues with some case studies from oncology showing the types of things that can go wrong, the simple nature of the most common mistakes, and what the implications can be: e.g., treating patients incorrectly. We then give some point estimates of how widespread the problems of reproducibility and replicability are thought to be, and discuss some additional problems associated with the replication. We survey tools introduced in the past few years which have made assembling reproducible studies markedly easier, discuss considerations to be applied when considering replication, and give pointers to some resources for further information.


The Statistical Analysis of fMRI Data

Friday, June 26, 2015
10:00 am to 12:00 pm Eastern

Martin Lindquist
Department of Biostatistics
Johns Hopkins University

Functional Magnetic Resonance Imaging (fMRI) is a non-invasive technique for studying brain activity. During the past two decades fMRI has provided researchers with an unprecedented access to the inner workings of the brain, leading to countless new insights into how the brain processes information. The field that has grown around the acquisition and analysis of fMRI data has experienced a rapid growth in the past several years and found applications in a wide variety of areas. This webinar introduces fMRI and discusses key statistical aspects involved in the analysis of fMRI data. Topics include: (a) an overview of the acquisition and reconstruction of fMRI data; (b) overview of the physiological basis of the fMRI signal; (c) common experimental designs; (d) pre-processing steps; (d) methods for localizing areas activated by a task; (e.) connectivity analysis; and (f.) prediction and brain decoding.


Sparse Logical Models for Interpretable Machine Learning

Friday, May 8, 2015
10:00 am to 12:00 pm Eastern

Cynthia Rudin, PhD, Associate Professor of Statistics, MIT CSAIL and Sloan School of Management, Massachusetts Institute of Technology

Possibly *the* most important obstacle in the deployment of predictive models is the fact that humans simply do not trust them. If it is known exactly which variables were important for the prediction and how they were combined, this information can be very powerful in helping to convince people to believe (or not believe) the prediction and make the right decision. In this talk I will discuss algorithms for making these non-black box predictions including:

  1. "Bayesian Rule Lists" - This algorithm builds a decision list using a probabilistic model over permutations of IF-THEN rules. It competes with the CART algorithm for building accurate-yet-interpretable logical models. It is not a greedy algorithm like CART.

  2. "Falling Rule Lists" - These are decision lists where the probabilities decrease monotonically along the list. These are really useful for medical applications because they stratify patients into risk categories from highest to lowest risk.

  3. "Bayesian Or's of And's" - These are disjunctions of conjunction models (disjunctive normal forms). These models are natural for modeling customer preferences in marketing.

  4. "The Bayesian Case Model" - This is a case-based reasoning clustering method. It provides a prototypical exemplar from each cluster along with the subspace that is important for the cluster.


Statistical Issues in Comparative Effectiveness Research

Friday, February 20, 2015
11:00 am to 1:00 pm EST

Sharon-Lise Normand, Department of Health Care Policy, Harvard Medical School & Department of Biostatistics, Harvard School of Public Health

Comparative Effectiveness Research (CER) refers to a body of research that generates and synthesizes evidence on the comparative benefits and harms of alternative interventions to prevent, diagnose, treat, and monitor clinical conditions, or to improve the delivery of health care. The evidence from CER is intended to support clinical and policy decision making at both the individual and the population level. While the growth of massive health care data sources has given rise to new opportunities for CER, several statistical challenges have also emerged. This tutorial will provide an overview of the types of research questions addressed by CER, review the main statistical methodology currently utilized, and highlight areas where new methodology is required. Inferential issues in the "big data" context are identified. Examples from cardiology and mental illness will illustrate methodological issues.


Statistical Challenges in Genomics High Throughput Data

Friday, January 30, 2015
11:00 am to 1:00 pm EST

Presenter: Rafa Irizarry, PhD
Professor of Biostatistics and Computational Biology at the Dana Farber Cancer Center
Professor of Biostatistics at the Harvard School of Public Health

In this webinar I will give an overview of genomics technologies and the challenges arising when analyzing the data they produce. Specifically, I will focus on microarrays and next generation sequencing technologies. We will cover statistical issues related to preprocessing, normalization, detecting differential expression, and dealing with batch effects.


An Introduction to Dynamic Treatment Regimes

December 5, 2014
11:00 am to 1:00 pm (EST)

Presenter: Marie Davidian, PhD North Carolina State University

Description: Treatment of patients with chronic diseases or disorders in clinical practice involves a series of decisions made over time. Clinicians adjust, change, modify, or discontinue therapies based on the patient's observed progress, side effects, compliance, and so on, with the goal of "personalizing" treatment to the patient in order to provide the best care. The decisions are typically based on synthesis of the available information on the patient using clinical experience and judgment.

A "dynamic treatment regime," also referred to as an "adaptive treatment strategy," is a set of sequential rules that dictate how to make decisions on treatment of a patient over time. Each rule corresponds to a key decision point at which a decision on which treatment action to take from among the available options must be made. Based on patient information, the rule outputs the next treatment action. Thus, a dynamic treatment regime is an algorithm that formalizes the way clinicians manage patients in practice.

In this presentation, we introduce the notion of a dynamic treatment regime and an appropriate statistical framework in which treatment regimes can be studied. We demonstrate how statistical inference may be made on the effects of different regimes based on data from so-called sequential, multiple assignment, randomized trials (SMARTs). We conclude with a discussion of current challenges, including the development of "optimal" treatment regimes. The material presented is ideal background for the shortcourse on personalized medicine and optimal dynamic treatment regimes to be offered at the ENAR Spring Meeting in March 2015.