Department of Biostatistics
Neurostatistics Working Group

2014 - 2015

Coordinators: Dr. Rebecca Betensky and Dr. Folefac Atem

Schedule: Wednesdays, 12:30-1:30 p.m.
HSPH2, Room 426 (unless otherwise notified)

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Seminar Description
This working group provides a forum for presentation and discussion of completed, ongoing, or planned statistical analyses of neurological data. Such data include, for example, in vivo human brain images (anatomic, functional and spectroscopic magnetic resonance imaging), gene expression studies of human and non-human animal brain tissue (brightfield and immunofluorescence microscopy, DNA microarrays, laser micro-dissection), in vivo micro-dialysis, clinical trials data for a variety of neurologic diseases, and genetic data from family studies. Non-statistical presentations of neurological, psychiatric and technological background material will also be included. Through this seminar, statisticians will gain exposure to the statistical issues that arise in the broad field of neurology and brain imaging psychiatry and to the diverse ongoing research in this area throughout Harvard and the world. A main goal of the seminar is to stimulate statistical interest in neuroscience and neurology and to develop strategies for collaboration within these fields.

October 8

Sharon Xiangwen Xie, Ph.D.*
Associate Professor of Biostatistics at the Hospital of the University of Pennsylvania (HUP), University of Pennsylvania Perelman School of Medicine

Survival Analysis with Uncertain Endpoints Using an Internal Validation Subsample"
ABSTRACT: When a true survival endpoint cannot be assessed for some subjects, an alternative endpoint that measures the true endpoint with error may be collected, which often occurs when obtaining the true endpoint is too invasive or costly. We develop nonparametric and semiparametric estimated likelihood functions that incorporate both uncertain endpoints available for all participants and true endpoints available for only a subset of participants. We propose maximum estimated likelihood estimators of the discrete survival function of time to the true endpoint and of a hazard ratio representing the effect of a binary or continuous covariate assuming a proportional hazards model. We show that the proposed estimators are consistent and asymptotically normal and develop the analytical forms of the variance estimators. Through extensive simulations, we also show that the proposed estimators have little bias compared to the nave estimator, which uses only uncertain endpoints, and are more efficient with moderate missingness compared to the complete-case estimator, which uses only available true endpoints. We illustrate the proposed method by estimating the risk of developing Alzheimer's disease using data from the Alzheimer's Disease Neuroimaging Initiative.

*Joint work with Jarcy Zee.
October 29

Yael Reijmer, Ph.D.
Postdoctoral Researcher, J. Philip Kistler Stroke Research Center, Massachusetts General Hospital

"Brain Network Disruption and Cognitive Impairment in Small Vessel Disease"
ABSTRACT: Small vessel disease (SVD) is an important risk factor for cognitive impairment and dementia. The mechanisms linking SVD to cognitive impairment are not well understood. We hypothesized that multiple small, spatially distributed vascular lesions affect cognition through disruption of brain connectivity. We therefore examined local and global network alterations in patients with SVD and examined the relationship between network efficiency, markers of SVD burden on MRI and PET, and potential clinical consequences.

November 12

Jing Qian, Ph.D.
Assistant Professor, Department of Biostatistics, University of Massachusetts - Amherst

"Talk Title TBD"
ABSTRACT: None Given

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