Department of Biostatistics
Neurostatistics Working Group
2014 - 2015
Rebecca Betensky and Dr.
Schedule: Wednesdays, 12:30-1:30 p.m.
HSPH2, Room 426 (unless otherwise notified)
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.
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 naïve 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.
Yael Reijmer, Ph.D.
Postdoctoral Researcher, J. Philip Kistler Stroke Research Center, Massachusetts General Hospital
"Brain Connectivity and Vascular Cognitive Impairment"
ABSTRACT: None Given