Apr612:00pm - 1:00pm
Daniel Almirall, PhD, Associate Professor, Co-Director, Data Science for Dynamic Intervention Decision-making Laboratory (d3lab), Survey Research Center, Institute for Social Research; Department of Statistics, College of Literature Sciences and the Arts, University of Michigan
Bio: Daniel Almirallis Associate Professor in the Institute for Social Research and the Department of Statistics at the University of Michigan. He is a methodologist and statistician who develops methods to form evidence-based adaptive interventions. Adaptive interventions can be used to inform individualized intervention guidelines for the on-going management of chronic illnesses or disorders such as drug abuse, depression, anxiety, autism, obesity, or HIV/AIDS. More recently, Dr. Almirall has been developing methods to form adaptive implementation interventions, to inform how best to tailor sequences of organizational-level strategies to improve the implementation of evidence-based practices. His work includes the development of approaches related to the design, execution, and analysis of sequential multiple assignment randomized trials (SMARTs) which can be used to build adaptive interventions, and of clustered SMARTs to build adaptive implementation interventions. He is particularly interested in applications in child and adolescent mental health research.
Abstract to be posted
Mar93:00pm - 4:00pm
Sumithra J. Mandrekar, PhD, Professor of Biostatistics and Oncology, Mayo Clinic
Clinical Trial Designs for Personalized Medicine in Oncology
Clinical trial design strategies have evolved as a means to accelerate the drug development process so that the right therapies can be delivered to the right patients. Basket, umbrella, SMART and adaptive enrichment strategies represent a class of novel designs for testing targeted therapeutics, and individualizing treatment in oncology. Umbrella trials include a central infrastructure for screening and identification of patients, and focus on a single tumor type or histology with multiple sub trials, each testing a targeted therapy within a molecularly defined subset. Basket trial designs offer the possibility to include multiple molecularly defined subpopulations, often across histology or tumor types, but included in one cohesive design to evaluate the targeted therapy in question. Adaptive enrichment designs offer the potential to enrich for patients with a particular molecular feature that is predictive of benefit for the test treatment based on accumulating evidence from the trial. A SMART design strategy is appropriate when comparing adaptive interventions, which are defined by an individual’s sequence of treatment decisions and guided by intermediate outcomes, such as response to therapy. This talk will focus on the fundamentals of these design strategies, the underlying statistical framework, the logistical barriers of implementation, and, ultimately, the interpretation of the trial results, using some case-studies including the National Cancer Institute’s precision medicine initiative trials.
Dr. Mandrekar received her interdisciplinary Ph.D. in Biostatistics, Psychology, Internal Medicine and Biomedical Engineering from the Ohio State University, Columbus OH in December 2002, and joined Mayo Clinic as a research associate in January 2003. She is currently Professor of Biostatistics and Oncology at the Mayo Clinic, Rochester MN, and Section Head for the Cancer Center Statistics within Mayo Clinic Department of Health Sciences Research. She is the Group Statistician for the Alliance for Clinical Trials in Oncology, which is one of the 4 NCI-funded national clinical trials networks for the conduct of phase II and III clinical trials in adult cancer. Dr. Mandrekar also holds an adjunct Professor of Biostatistics appointment at the School of Public Health, University of Minnesota, and at the University of Gainesville, FL.
Dr. Mandrekar’s primary research interests include adaptive dose-finding early phase trial designs, designs for predictive biomarker validation, and general clinical trial methodology related to conduct of clinical trials and identification of alternative Phase II cancer clinical trial endpoints. Dr. Mandrekar has co-authored over 140 original papers; several book chapters and editorials; and has given numerous lectures, invited presentations and workshops on these topics.
Her primary collaborative areas are lung cancer and leukemia. She is the faculty statistician for the national adjuvant lung cancer trial, ALCHEMIST, an approximately 8000 patient trial, which is part of the NCI precision medicine initiative. She was the primary statistician on the Phase III C10603 trial that led to the FDA approval of Midostaurin for AML patients with FLT3 mutations.
Dr. Mandrekar is a voting member of the NCI thoracic malignancies steering committee, past president of the society for clinical trials (2018-2019), voting member on the clinical trials transformation initiative on master protocols, member of the ASCO mCODE executive council, voting member of the international RECIST working group and the biostatistics editor for the Journal of Thoracic Oncology.
She has a national and international presence as an expert in clinical trial design as evidenced by her bibliography and membership on various national and international committees.
Dec93:00pm - 4:00pm
Ludovic Trinquart PhD, Assistant Professor of Biostatistics, Boston University School of Public Health
“Counterfactual mediation analysis with illness-death model for right-censored surrogate and clinical outcomes”
We introduce a counterfactual-based mediation analysis for surrogate outcome evaluation when both the surrogate and clinical endpoint are subject to right-censoring. We use a multistate model for risk prediction to account for both direct transitions towards the clinical endpoint and transitions through the surrogate endpoint. We use the counterfactual framework to define natural direct and indirect effects and the proportion of the treatment effect on the clinical endpoint mediated by the surrogate endpoint. We define these quantities for the cumulative risk and restricted mean survival time. We illustrate our approach using 18-year follow-up data from the SPCG-4 randomized controlled trial of radical prostatectomy for prostate cancer. We assess time to metastasis as a potential surrogate outcome for all-cause mortality.
Oct73:00pm - 4:00pm
Fan Li, PhD, Associate Professor, Department of Statistical Science, Duke University
“Introducing the Overlap Weights in Causal Inference”
Covariate balance is crucial for confounding adjustment in causal studies with observational data. We propose a unified framework —the balancing weights– to balance the weighted distributions of the covariates between treatment groups. These weights incorporate the propensity score to weight each group to an analyst-selected target population, and include several commonly used weighting schemes such as inverse-probability weight and trimming as special cases. We derive the large-sample results on nonparametric estimation based on these weights. We further propose a new weighting scheme, the overlap weights, in which each unit’s weight is proportional to the probability of that unit being assigned to the opposite group. The overlap weights are bounded, and minimize the asymptotic variance of the weighted average treatment effect among the class of balancing weights. We prove a small-sample exact balance property of the overlap weights. We apply the method the Framingham Heart Study to evaluate the effect of statins on health outcomes. Extension to multiple treatments will also be discussed.
Sep303:00pm - 4:00pm
Prasanta Pal, PhD, Research Project Director, U. Mass. Medical School, Adjunct Assistant Professor of Behavioral and Social Sciences, School of Public Health, Brown University
Bio: I trained as a Physicist at Indian Institute of Technology, Kharagpur, India and Yale University School of Engineering, New Haven, CT. Starting with computer simulation on super-conducting quantum computer at Yale , my PhD work was focused on large scale computer simulations of soft matter particle systems. I briefly worked as a postdoc at Yale MRI center to study blood flow dynamics in human heart. Then I head started the technological platform of the startup company “Mind sciences” to build mobile applications like “Craving to Quit” for addiction treatment through mindfulness interventions. In parallel I built high-density real-time EEG neuro-feedback systems to provide mental-training to different population groups. This has been part of large scale clinical trials and demonstrated in commercial program like CBS 60 minutes. Currently I’m developing tools for big-data, 4D visualization of human mind and its quantification using AI and computational geometry.
Abstract: “Chasing the Mind”
Background: the quest to understand the mind is age-old and unresolved. Although the “Mind” is central to nearly all human activity, the quest for a complete scientific understanding of “how the human mind works” remains unresolved. In science, much progress has been made to understand the nature and function of the mind using various techniques. Useful models of mental and emotional function have emerged, with an increasingly detailed picture of the brain-body connection and its many roles in health and survival.
Opportunity: Mind-body research reveals inner order, while technology opens frontiers for analysis. More recently, mystics of the east and clinicians of the west have come to recognize the common platform through which they seek to promote mental well being. Increasingly, the exploration of one’s inner self (e.g., through inner query and mental-training) can complement external tools like thermometers and drug regimens. The old notion of body-based medicine has evolved into mind-body medicine. Clinically, successful programs like MBSR (Mindful Based Stress Reduction) developed by Jon Kabat Zinn are geared towards harmonizing the physical and mental dimensions of human existence. Importantly, the inner order and wholeness perceived by mystics is gaining solid scientific footing and paving the way for a revolutionary new age of medicine.
On the other hand, the rapidly evolving knowledge-base of machine-learning, data science and technology are collapsing the boundaries between traditional academic disciplines. As a result, commercially available devices like MUSE for meditation and EEG sleep monitors are device incarnations to play with “Mind dynamics”.
Challenge: Reductionist frameworks miss hidden order in the mind’s complexity. The human brain is by far the most sophisticated computing-device known to science. High quality data measured from the human brain via EEG like sensors can effectively be utilized to create the cartography of the human mind.
Although simplistic reductionist models have very successfully permeated the domains of physical sciences, it is often limited in scope to explain the human mind. As an alternative, we introduce a more holistic approach to pave the cartographic building blocks of the human mind using high dimensional data collected through EEG sensors attached to the human brain.
Our work: New imaging and computational techniques to visualize the mind’s wholeness. As data scientists and technologists, our work is to build tools to visualize and investigate the human mind with as much scientific and mathematical rigor as complex fields like rocket science. The holy grail is to map out the deeper-most thought patterns in the human mind on our favorite mobile device. What has been inner can be unfolded to the outer.
In this work, we describe our unique journey to understand the human mind starting with a simple noisy one-dimensional time series plot of a small EEG data segment and, from there, building models with infinite-dimensional function space to characterize and visualize the experientially active human mind. We demonstrate that the inner journey of the self is also reciprocally, a number crunching exercise. With these tools, when fully developed, we can transform soulless-selfies to the speckles of our blissful inner-selves.
Sep233:00pm - 4:00pm
Lisa LaVange, PhD
bio: Lisa LaVange, PhD, is Professor and Associate Chair of the Department of Biostatistics in the Gillings School of Global Public Health at the University of North Carolina at Chapel Hill. She is also director of the department’s Collaborative Studies Coordinating Center (CSCC), overseeing faculty, staff, and students involved in large-scale clinical trials and epidemiological studies coordinated by the center. From 2011 to 2017, Dr. LaVange was director of the Office of Biostatistics in the United States Food and Drug Administration (FDA) Center for Drug Evaluation and Research (CDER). There, she oversaw more than 200 statisticians and other staff members involved in the development and application of statistical methodology for drug regulation. She was a leader in developing and assessing the effectiveness and appropriateness of innovative statistical methods intended to accelerate the process from drug discovery to clinical trials to FDA approval and patients’ benefit, with a particular focus on rare diseases. Prior to her government and academic experience, she spent 16 years in non-profit research and 10 years in the pharmaceutical industry. Dr. LaVange is an elected fellow of the American Statistical Association (ASA) and was the 2018 ASA President.
Abstract: Real-world Data, Statistical Innovation, and Evidence Generation
As a result of the 21st Century Cures Act and the Prescription Drug User’s Fee VI Act, both passed into law in 2016, the FDA launched several important initiatives. The first involves real-world evidence generation from real-world data in both the pre- and post-market settings. This initiative reflects the desire to use existing health care data and data from other non-standard sources in lieu of or to supplement data collected as part of a research study to support product approval or expansion to new indications. Patient advocacy groups and medical researchers alike view the rich data streams that are becoming increasingly available as too big to ignore in terms of providing insight into a therapy’s safety and effectiveness. The second initiative reflects a desire to use innovative or otherwise non-traditional trial designs to generate substantial evidence in support of regulatory submissions. This initiative was intended to counter the belief that FDA reviewers were not often accepting of novel or complex trial designs, as shown by the lack of available guidance on the topic or examples that could be pointed to as successes. The third initiative reflects a desire to incorporate pharmacometric modeling into a drug development program to reduce the amount of data required to be collected in new clinical studies. The goal is to more effectively leverage what is already known about a drug, or able to be predicted with a high level of confidence, in planning the later stages of clinical development or expansion to new indications. In this seminar, I will review these initiatives, based on my involvement while at FDA in their development and roll-out, and discuss their impact on clinical research today. I will also discuss some recent initiatives at ASA that dove-tail nicely with the FDA initiatives and relate to opportunities for statisticians to take a leadership role in both finding and advocating for appropriate use of innovative solutions to problems in public health research.
Eloise Kaizar, PhD
Associate Professor of Statistics
Co-Vice Chair for Undergraduate Studies and Administration
Ohio State University
“Watching your Weights: Generalizing from a Randomized Trial to a ‘Real World’ Target Population”
Randomized controlled trials are often thought to provide definitive evidence on the magnitude of treatment effects. But because treatment modifiers may have a different distribution in a real world population than among trial participants, trial results may not directly reflect the average treatment effect that would follow real world adoption of a new treatment. Recently, weight-based methods have been repurposed to more provide more relevant average effect estimates for real populations. In this talk, I summarize important analytical choices involving what should and should not be borrowed from other applications of weight-based estimators, make evidence-based recommendations about confidence interval construction, and present conjectures about best choices for other aspects of statistical inference.