Division Meeting is 1:00-2:00 pm on the third Wednesday of each month in MTF 168

Journal Club is 3:00-4:00 pm on the second and fourth Fridays of each month in Moores Cancer Center Room 3079

Methodological Opportunities and Challenges with Advancing and N-of-1/Small Data Paradigm

Eric B. Hekler, PhD

5/1/2019 1:00 PM – 2:00 PM
MTF (Medical Teaching Facility) 168

BACKGROUND: There is great interest in and excitement about the concept of Precision Medicine and, in particular, advancing this vision via various "big data" efforts. While these methods are necessary, they are insufficient for achieving the full promise. A rigorous, complementary "small data" paradigm that can function both autonomously from and in collaboration with big data is also needed.  By "small data" I am building on Estrin's formulation referring to the rigorous use of data by and for a specific N-of-1 unit  (i.e., a single person, clinic, hospital, healthcare system, community, city, etc) to facilitate improved individual-level description, prediction, and, ultimately, control for that specific unit.

PURPOSE: The purpose of talk is to provide a rationale for the need for a small data paradigm and to illustrate, via two N-of-1/small data projects, methodological challenges and opportunities.

OUTLINE: The talk will begin with a brief overview of the rationale for a small data paradigm, including both articulation of initial epistemological assumptions and also pragmatic principles that guide my work towards the production of usable evidence and corresponding useful and usable health tools.  Following this, I will discuss my colleagues and my work in advancing the use of control systems engineering methods for advancing individualized tools for advancing health behavior change.  I will then describe the work my colleagues and I have been advancing when using N-of-1 methods for positive psychology/public health interventions. Within both, I will use them to illustrate methodological opportunities and challenges that arise, with interest in leaving sufficient time for discussion about the challenges posed.

BIO:  Eric Hekler, PhD, is an Associate Professor in the Department of Family Medicine and Public Health at UCSD. He is also Director of the Center for Wireless & Population Health Systems and faculty member of the Design Lab at UCSD. His research focuses on facilitating individualized behavior change for fostering long-term health and well-being via digital health tools. Prior to UCSD, Dr. Hekler was a faculty member at Arizona State University.  He completed his postdoctoral training at Stanford University and received his Ph.D. in Clinical Health Psychology from Rutgers University.

Adaptive Enrichment Designs for Reproducible Confirmatory Clinical Trials in the Era of Precision Medicine

Tze Leung Lai, PhD

2/13/19 11:00 AM – 12:00 PM
MET (Medical Education Telemedicine Building) 223

ABSTRACT: After an overview of FDA's 2012 draft guidance on enrichment strategies for clinical trials to support drug/biologic approval, we describe subsequent advances in adaptive enrichment designs in this direction. We also provide a concrete application in the enrichment design of the DEFUSE 3 trial comparing a new endovascular treatment with standard of care for ischemic stroke patients.

BIO: Dr. Tze Leung Lai is the Ray Lyman Wilbur Professor of Statistics and, by courtesy, of Biomedical Data Science and of the Institute of Computational and Mathematical Engineering (ICME) at Stanford University. He is also Co-director of the Center for Innovative Study Design (CISD) at the Stanford University School of Medicine. Dr. Lai has supervised 73 Ph.D. theses and seven postdoctoral trainees. He published over 300 papers, many of which are in clinical trial design and analysis, population pharmacokinetics and pharmacodynamics, survival analysis, longitudinal data analysis, multiple endpoints, biomarkers, adaptive methods, sequential analysis and time series.

Missing and Modified Data in Nonparametric Statistics 

Sam Efromovich, PhD

1/9/2019 1:00PM – 2:00 PM
MTF 168

ABSTRACT: After a short introduction to topics in nonparametric curve estimation, covered in my new 2018 Chapman & Hall book with the same title as the talk, three specific problems will be considered. The first one is non-parametric regression with missing at random (MAR) responses. It will be explained that a complete case approach is optimal in this case. The second problem is a nonparametric regression with missing at random (MAR) predictors. It will be explained that in general a complete case approach is inconsistent for this type of missing and a special procedure is needed for efficient estimation. The last explored problem is devoted to survival analysis, specifically to efficient estimation of a hazard rate function for truncated and censored data. Time permitted, several recent results and open problems will be highlighted.

BIO: Dr. Sam Efromovich is the Endowed Professor of Mathematical Sciences and the Head of Actuarial Program at the University of Texas at Dallas, where he has worked since 2006. Before he was a Professor at the University of New Mexico, Albuquerque (since 1991). Sam has PhD in Information Theory and Statistics (1978) and Control Theory and Industrial Statistics (1986). He wrote more than 200 papers and two books: "Nonparametric Curve Estimation" (Springer, 1999) and "Missing and Modified Data in Nonparametric Estimation" (Chapman and Hall, 2018), which will be discussed at this seminar.