Prentice Science and Statistics Integrative Seminars

Biostatistics Program


Upcoming Seminars


mHealth:
State of the Science and Research Opportunities

Wednesday, April 4, 4:00-5:30

Sze Conference Room, D1-080/084, Thomas Building, Fred Hutch Campus

A series of talks about mobile health technologies. Sponsored by Ying Chen.

UW-Fred Hutch Shuttle Schedule

 


Scheduled Presentations


mHealth Technologies for Smoking Cessation: Are We Just Blowing Smoke?

Jonathan Bricker, Fred Hutchinson Cancer Research Center

Every year, 4 million Americans and many millions more worldwide download a smartphone app to help them quit smoking cigarettes— a mobile health technology that has been evolving since 2007. These aim of these apps is to help people stop the single most preventable cause of cancer and a third of all cancer deaths. While these cutting-edge technologies have high population level reach and low cost to the end user, the fundamental question for public health science is: Do they work? Following a review of the common features of these apps, the talk will closely review the evidence from scientific trials on their effectiveness, impact on processes of behavior change, and applications across the cancer prevention and care continuum. I will also highlight a novel statistical method for analyzing e-health intervention usage data, with implications for improving the effectiveness of smartphone apps. The talk will conclude with highlighting future directions for smartphone app research.

 

Methods for Mobile Device Data in Precision Public Health Research

Ying Qing Chen, Fred Hutchinson Cancer Research Center

Mobile and wearable devices revolutionize information exchange and are re-shaping social topology and networking at an unprecedented rate. Their impact is profound. In particular for prevention research the mobile devices become a means for investigators and health practitioners to collect an incredible amount of real-time, high-resolution, high-dimensional data. The data collected can be used to better understand every aspect of public health research, including risk assessment, disease monitoring, and innovative prevention interventions on an ultra-large scale. In this talk, we will discuss several key analytic challenges for modeling and analyzing mobile device data in public health research, and introduce a few principled approaches to tackling these challenges.

 

Statistical Considerations in mHealth: Promises and Challenges 

Chongzhi Di, Fred Hutchinson Cancer Research Center

With rapid advances in technology, mHealth tools have been increasingly adopted in healthcare and biomedical research. For example, wearable devices such as accelerometers are utilized to objectively measure physical activity and sleep, electrocardiogram patch monitors allow continuous monitoring of heart rhythm unobtrusively, while smartphone apps enable innovative interventions for behavior changes, e.g., smoking cessation. Mobile and wearable devices often collect high resolution and high dimensional data with massive size and complex structures, which provide both huge promises in improving precision in public health research and unique challenges for statistical analysis. This talk will focus on statistical considerations on a few important issues in mHealth, including measurement validity, representativeness and model flexibility and robustness. Common uses and misuses of mHealth data will be discussed. A few interpretable and robust analytic tools in functional data analysis and compositional data analysis will be introduced, with their usefulness illustrated by the example of accelerometry in physical activity epidemiology.

 

Mobile Health Biomarkers: Working Toward Validation through Crowdsourcing to Detect Personalized Fluctuations

Larsson Omberg, Sage Bionetworks

Abstract to come.

 


Speakers


Dr. Jonathan Bricker, PhD,is founder and leader of the Health And Behavioral Innovations in Technology (HABIT) Group. A licensed clinical psychologist, he is a Full Member (equivalent to Full Professor) in the Division of Public Health Sciences at the Fred Hutchinson Cancer Research Center. He is also an Affiliate Professor in the Department of Psychology and Member of the Graduate School Faculty at the University of Washington in Seattle, Washington. His research focuses on developing and testing innovative interventions for health behavior change, especially those delivered in technology platforms. He has been applying this expertise to smoking cessation and soon will be adding a focus on obesity. He has served as principal investigator or co-investigator on a variety of NIH research projects. Among his current research grants, he is PI for a total of $14 million in NIH grants for the “WebQuit”, "iCanQuit", and "TALK" study of Acceptance and Commitment Therapy for adult smoking cessation, comparing Acceptance & Commitment Therapy with traditional Cognitive Behavioral Therapy via web, smartphone app, and telephone. He has published over 70 peer-reviewed research articles in major scientific journals. Currently, he serves as Senior Editor of the journal Addiction - the highest impact substance abuse journal and was recently a regular member of NIH Community-Level Health Promotion (CLHP) Study Section and Chair of the Tobacco Centers of Regulatory Science (TCORS) Study Section. Dr. Bricker received his Ph.D. in Clinical Psychology from the University of Washington.

Dr. Ying Qing Chen is a Full Member of Biostatistics at the Fred Hutchinson Cancer Research Center, and an Affiliate Professor of Biostatistics at the University of Washington. His research focuses on the development of statistical methods for analysis of survival and longitudinal data from clinical trial and other sources. Mobile and wearable devices revolutionize information exchange and are re-shaping social topology and networking at an unprecedented rate. Their impact is profound. In particular for prevention research the mobile devices become a means for investigators and health practitioners to collect an incredible amount of real-time, high-resolution, high-dimensional data. The data collected can be used to better understand every aspect of public health research, including risk assessment, disease monitoring, and innovative prevention interventions on an ultra-large scale. In this talk, we will discuss several key analytic challenges for modeling and analyzing mobile device data in public health research, and introduce a few principled approaches to tackling these challenges.

Dr. Chongzhi Di is an Associate Member of Biostatistics at the Fred Hutchinson Cancer Research Center, and also an Affiliate Associate Professor in the Department of Biostatistics at the University of Washington. His research focuses on the analysis of functional and longitudinal data, statistical inference for complex models, and their application in physical activity epidemiology and other mHealth studies. Motivated by ancillary studies of the Women’s Health Initiative, his group has been developing cutting-edge statistical methods for accelerometer-measured physical activity data, including novel metrics for activity intensity, frequency, and duration, as well as flexible functional data analysis and compositional analysis tools for quantifying dose-response relationships between activity patterns and health outcomes. Recently, he has also been involved in other mHeath studies that utilize mobile and wearable devices for improved measurement, monitoring, and intervention, including smartphone based behavior interventions and wearable electrocardiogram monitoring patch for assessment of cardiac rhythm abnormalities.

Dr. Larsson Omberg is the VP Systems Biology at Sage Bionetworks overseeing a research agenda that focuses on two areas – using remote sensors and mobile phones to measure disease; and collaborative genomic research. As a graduate student and postdoctoral researcher at the University of Texas and Cornell University, Dr. Omberg developed machine learning and statistical approaches for extracting genomic phenotypes and disease signals from system level biological data. As a principal scientist at Sage Bionetworks, his expertise was applied to coordinating data integration and integrative analysis for the TCGA Pancancer collaboration, The Progenitor Cell Biology Consortium as well as the international ICGC/TCGA Whole Genome Pan-Cancer Analysis among other projects. Currently, his group focuses heavily on open and team-based science to get a large number of external partners to collaborate on data-intensive problems. Dr. Omberg received a Ph.D. in physics from the University of Texas at Austin.

 


Past Seminars


Wednesday, February 7, 3:30-5:00

Pelton Auditorium, Fred Hutch Campus

UW-Fred Hutch Shuttle Schedule

Profiling the Immune System: From Statistics to the Clinic

A series of four presentations on vaccines and immunotherapy. Researchers are learning how to empower a patient’s own immune system to do what it does naturally — fight disease. They continue discovering new ways to give the immune cell army the upper hand against cancer and learning how immune cells respond to disease and how to safely enhance immune responses to better control, cure and potentially prevent cancers and other serious diseases. 

Organized by Raphael Gottardo.

Vaccine Speakers

Ollivier Hyrien

Dr. Hyrien's research focuses include 

  • Statistical methods for bioassays (e.g., flow cytometry, sequencing)
  • Machine learning (e.g., nonparametric clustering and classification)
  • Dynamic modeling (e.g., B-cell repertoire)

Steve DeRosa 

Dr. De Rosa has worked with the HIV Vaccine Trials Network (HVTN) for the past eight years, and leads the flow cytometry laboratory. In this role, he oversees the operation and maintenance of the flow cytometer analyzers and sorters, directly supervises HVTN Research & Development technicians in the development and optimization of new flow cytometry-based assays, and consults with the Endpoints Laboratory manager concerning the performance and analysis of the validated flow cytometric assays conducted on clinical trial samples. Following graduation from medical school and some brief clinical training, he has pursued a full-time career in basic and applied research. He trained as a postdoctoral fellow in the Herzenberg Laboratory at Stanford, the laboratory that first developed flow cytometry technology 40 years ago. He then continued his training with Mario Roederer, an internationally-recognized flow expert, at the NIH Vaccine Research Center (VRC). While at the VRC, he implemented intracellular cytokine staining assays for evaluation of vaccine-induced T cells. He later implemented and validated similar assays within the HVTN. He is internationally recognized for this work in implementing and validating functional assays using cutting-edge technology for high throughput analysis of clinical trial samples.

Immunotherapy Speakers

Raphael Gottardo

Dr. Gottardo's research interests include

  • Developing methods and tools for high throughput, high dimensional experiments with applications in vaccine research and immunology
  • Flow cytometry, peptide microarrays, next generation sequencing
  • Bayesian inference and computation
  • Statistical computing

Kelly Paulson

Dr.  Paulson is a senior hematology/oncology fellow in the Chapuis Lab of Fred Hutch's Clinical Research Division. Her research is focused on the development of combination T cell immunotherapies for Merkel cell carcinoma.  Dr. Paulson's research includes the application of cutting-edge technologies to answer relevant biological and clinical questions and the development of effective prognostic tools, 

Tuesday, December 4, 2017

Panel Discussion: What's New in Clinical Trials?

Mike LeBlanc, Cathy Tangen, Yingqi Zhao, and Megan Othus will give brief overviews of some of their work within SWOG.  Mike will give some background on the NCI cooperative group structure, Cathy will talk about statistical challenges in large prevention trials, Yingqi Zhao will talk about challenges with with developing personalized trial designs in the cooperative group settings, and Megan Othus will talk about using SWOG historical data to gain insight on endpoints in future clinical trials.  Light refreshments will be served.

Panel Members:

Mike LeBlanc

Dr. LeBlanc's  research interests include the design and analysis of clinical trials, methods for exploratory analysis of survival data and adaptive non-parametric regression. Most of his collaborative research focuses on the design, analysis and conduct of therapeutic clinical trials. As Head of the SWOG Statistical Center, he investigates design and analysis methods for targeting patient subgroups appropriate for Phase II and Phase III clinical trials. He also  studies adaptive regression methods and their application to data arising from clinical trials, developing extensions or alternatives to tree-based methods to yield simple prognostic decision rules. He recently developed an algorithm called Extreme Regression for constructing either high- or low-risk outcome groups. He is currently working on methods that allow specification of genetic structure into the high dimensional regression problem.

 

Megan Othus

Dr. Othus' collaborative research focuses on the design, conduct and analysis of cancer studies, specifically the Southwest Oncology Group’s phase 2 and phase 3 melanoma and leukemia clinical trials. Othus is also interested in developing statistical methods for correlated survival data. Correlated survival data arise in many types of biomedical research. For example, data from population-based studies can be geographically correlated or data from multi-center clinical trials can be correlated within an institution. 

 

Catherine M. Tangen

Dr. Tangen's research interests include the design and analysis of clinical trials and non-parametric covariance adjustment, particularly for survival data. She is the statistician for Phase II, Phase III and ancillary studies for genitourinary cancers (e.g., prostate, bladder, renal, testes) in the Southwest Oncology Group. In addition to therapeutic trials, she is also the primary statistical investigator for the Prostate Cancer Prevention Trial (PCPT). The PCPT is a large (18,800 men), randomized, double-blind trial whose primary objective is to test the difference in biopsy-proven period prevalence of carcinoma of the prostate between a group of participants treated with finasteride and a group treated with placebo for seven years. The primary results, which were reported in June 2003, showed a 25% reduction in the period-prevalence of prostate cancer among those on finasteride relative to placebo. However, there was also a small but statistically significant increase in the rate of high grade cancer on the finasteride arm. A subsequent paper related to the prevalence of prostate cancer in men with normal PSA levels was published in the summer of 2004. A number of other manuscripts addressing the high grade issue and other topics are planned or currently underway.

 

Yingqi Zhao

Yingqi Zhao received her PhD in biostatistics from the University of North Carolina, Chapel Hill in 2012.  She is currently an Assistant Member at the Fred Hutchinson Cancer Research Center.  Her research focus includes methodologies for personalized medicine, dynamic treatment regimes, observational studies and machine learning.  Specific applications of these work include cancer treatment and prevention, health care delivery for complex type II diabetes patients and childhood obesity surveillance.  Her work in personalized medicine is particularly notable for these applications, which has been the basis for much subsequent work on developing biomarker-based treatment rules.  She is actively engaged with collaborations with the SWOG clinical oncology cooperative group.