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Rebecca A Betensky

Rebecca Betensky

Rebecca Betensky

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Chair of the Department of Biostatistics

Professor of Biostatistics

Professional overview

Prior to NYU, Dr. Betensky was Professor of Biostatistics at the Harvard T.H. Chan School of Public Health. She was director of the Harvard Catalyst (Clinical and Translational Science Award) Biostatistics Program; director of the Data and Statistics Core for the Massachusetts Alzheimer’s Disease Research Center; and director of the Biostatistics Neurology Core at Massachusetts General Hospital. Previously, she was the Biostatistics Program Leader for the Dana-Farber/Harvard Cancer Center.

Dr. Betensky’s research focuses on methods for the analysis of censored and truncated outcomes and covariates, which frequently arise from the subsampling of cohort studies. She has a long-time interest in clinical trials, and has written on the evaluation of biomarkers and the use and interpretation of p-values. She has collaborated extensively in studies in neurologic diseases, and serves as statistical editor for Annals of Neurology.

Dr. Betensky was awarded, and directed for 15 years, an NIH T32 training program in neurostatistics and neuroepidemiology for pre- and post-doctoral students in biostatistics and epidemiology and for clinician-scientists. She previously directed Harvard’s Biostatistics programs to promote and support diversity at all levels in the field of quantitative public health. She was also a member of the BMRD Study Section for review of NIH statistical methodology grants; on committees for the Institute of Medicine; and a co-chair of the technical advisory committee for the scientific registry of transplant recipients.

Dr. Betensky an elected Fellow of the American Statistical Association and of the International Statistical Institute, and is a past recipient of the Spiegelman Award from the American Public Health Association. She currently serves as a member of the Board of Scientific Counselors for Clinical Science and Epidemiology at the National Cancer Institute.

Education

AB, Mathematics, Harvard University, Cambridge, MA
PhD, Statistics, Stanford University, Stanford, CA

Areas of research and study

Biology
Biostatistics
Neuroepidemiology
Neurology
Neurostatistics
Translational science

Publications

Publications

Age-Dependent Prognostic Effects of Genetic Alterations in Glioblastoma

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Combining gene expression profiles and clinical parameters for risk stratification in medulloblastomas

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Imaging correlates of molecular signatures in oligodendrogliomas

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Power Calculations for Familial Aggregation Studies

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The borderline or weakly positive Hybrid Capture II HPV test : A statistical and comparative (PCR) analysis

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Treatment of relapsed central nervous system lymphoma with high-dose methotrexate

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Analysis of a molecular genetic neuro-oncology study with partially biased selection.

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BCL-6 expression predicts improved survival in patients with primary central nervous system lymphoma

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Commentary : Failure-rate functions for doubly-truncated random variables

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Gene expression-based classification of malignant gliomas correlates better with survival than histological classification

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Hazard regression for interval-censored data with penalized spline

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Immunophenotypic and viral (human papillomavirus) correlates of vulvar seborrheic keratosis

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Malignant peripheral nerve sheath tumor : The clinical spectrum and outcome of treatment

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Neurolymphomatosis

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The Leapfrog Volume Criteria May Fall Short in Identifying High-Quality Surgical Centers

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A local likelihood proportional hazards model for interval censored data

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C455R notch3 mutation in a Colombian CADASIL kindred with early onset of stroke

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DMBT1 polymorphisms : Relationship to malignant glioma tumorigenesis

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Histopathological-molecular genetic correlations in referral pathologist-diagnosed low-grade "oligodendroglioma"

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Influence of unrecognized molecular heterogeneity on randomized clinical trials

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Local likelihood analysis of the latency distribution with interval censored intermediate events

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Sample size re-estimation in cluster randomization trials

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Testing for dependence between failure time and visit compliance with interval-censored data

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The use of frailty hazard models for unrecognized heterogeneity that interacts with treatment : Considerations of efficiency and power

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A comparison of models for clustered binary outcomes : Analysis of a designed immunology experiment

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Contact

rebecca.betensky@nyu.edu 708 Broadway New York, NY, 10003