American Statistical Association
New York City
Metropolitan Area Chapter

Memorial Sloan Kettering Cancer Center
Department of Epidemiology and Biostatistics
Biostatistics Seminar Series


Yuanjia Wang, Ph.D.
Associate Professor, Biostatistics
Columbia University

IDENTIFYING BIOMARKER SIGNATURES FOR NEURODEGENERATIVE DISEASES
FROM LARGE-SCALE (ASYNCHRONOUS)
BIOMARKER MEASURES WITH NETWORK STRUCTURE

Potential disease-modifying therapies for neurodegenerative disorders need to be introduced prior to the symptomatic stage in order to be effective. However, current diagnosis of neurological disorders mostly rely on measurements of clinical symptoms and thus only identify symptomatic subjects in their late disease course. Thus, it is of interest to select and integrate biomarkers that may reflect early disease-related pathological changes for earlier diagnosis and recruiting pre-symptomatic subjects in a prevention clinical trial. In many clinical studies of neurological disorders, researchers collect measurements of both static and dynamic biomarkers over time (e.g., clinical assessments or neuroimaging biomarkers) to a build time-sensitive prognostic model. An emerging challenge is that due to resource-intensive or invasive (e.g., lumbar puncture) data collection process, biomarkers may be measured infrequently and thus not available at every observed event time point. Leveraging all available, infrequently measured dynamic biomarkers to improve the prognostic model of event occurrence is an important and challenging problem. In this paper, we propose a kernel-smoothing based approach to borrow information across subjects to remedy infrequent and unbalanced biomarker measurements under a time-varying hazards model. A penalized pseudo-likelihood function is proposed for estimation and accommodate network structure among biomarkers, and an efficient augmented penalization minimization algorithm is adopted for computation. We apply the proposed method to a recently completed natural history study of Huntington's disease to predict time to disease conversion using structural change at huntingtin gene and longitudinal, whole brain structural magnetic resonance imaging biomarkers. Lastly, we discuss an approach to estimate causal networks using high-dimensional biomarkers with an application to discover protein signaling network from human immune T-cell data.


Date: Wednesday, May 3, 2017
Time: 4:00 - 5:00 P.M.
Location: Memorial Sloan Kettering Cancer Center
Department of Epidemiology and Biostatistics
485 Lexington Avenue
(Between 46th & 47th Streets)
2nd Floor, Conference Room B
New York, New York

**Outside visitors please email celeat@mskcc.org for building access.
You must be on the security list to enter the floor.

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