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Discovering the causes of cancer and the means of prevention

Biostatistical Methods for Wearable and Implantable Technology (WIT) - Dr. Ciprian Crainiceanu

DCEG Seminar

September 10, 2019 | 10:30 AM – 11:30 AM

NCI Shady Grove 6E032/034 Rockville, M.D.

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Biostatistics Branch Seminar Series 


Ciprian Crainiceanu, Ph.D.
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.


1:00PM – 2:00PM
Location: Shady Grove 3W032/034
Moderator: Charles Matthews, Ph.D. (Senior Investigator, NCI, DCEG, Metabolic Epidemiology Branch) 
Title: “Physical Activity and Cancer:  Unraveling Important Relationships with Better Exposure Assessment”


2:00PM– 3:00PM
Location: Shady Grove 3W032/034
Moderator:  Nicolas Wentzensen, M.D., Ph.D., M.S. (Senior Investigator, NCI, DCEG, Clinical Genetics Branch)
Title: “Rationale and design for a cohort study nested in integrated healthcare systems: Connect”

**The mission of the Biostatistics Branch (BB) is to be an outstanding biostatistics unit that can contribute to the understanding of cancer etiology and to improve public health by the development and application of quantitative methods.  The BB Investigators develop statistical methods and data resources to strengthen observational studies, intervention trials, and laboratory investigations of cancer.**