Rebecca A Betensky

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

Genome-wide comparison of paired fresh frozen and formalin-fixed paraffin-embedded gliomas by custom BAC and oligonucleotide array comparative genomic hybridization: Facilitating analysis of archival gliomas

Mosaic amplification of multiple receptor tyrosine kinase genes in glioblastoma

Primary CNS lymphoma in children and adolescents: A descriptive analysis from the International Primary CNS Lymphoma Collaborative Group (IPCG)

Reactive glia not only associates with plaques but also parallels tangles in Alzheimer's disease

Assessing Population Level Genetic Instability via Moving Average

Remote supervision of IV-tPA for acute ischemic stroke by telemedicine or telephone before transfer to a regional stroke center is feasible and safe

Spatial relation between microbleeds and amyloid deposits in amyloid angiopathy

A classic twin study of external ear malformations, including microtia

A latent class model with hidden markov dependence for array CGH data

Bone involvement predicts poor outcome in atypical meningioma: Clinical article

Genetic determinants of hearing loss associated with vestibular schwannomas

Genomic profiling distinguishes familial multiple and sporadic multiple meningiomas

Genomic profiling of atypical meningiomas associates gain of 1q with poor clinical outcome

High-dose methotrexate for elderly patients with primary CNS lymphoma

Matrix metalloproteinase inhibition reduces oxidative stress associated with cerebral amyloid angiopathy in vivo in transgenic mice

Microbleeds versus macrobleeds: Evidence for distinct entities

Polysomy for chromosomes 1 and 19 predicts earlier recurrence in anaplastic oligodendrogliomas with concurrent 1p/19q loss

A penalized latent class model for ordinal data

Desantis, S. M., Houseman, E. A., Coull, B. A., Stemmer-Rachamimov, A., & Betensky, R. A. (n.d.).

Publication year

2008

Journal title

Biostatistics

Volume

9

Issue

2

Page(s)

249-262

10.1093/biostatistics/kxm026

Abstract

Abstract

Latent class models provide a useful framework for clustering observations based on several features. Application of latent class methodology to correlated, high-dimensional ordinal data poses many challenges. Unconstrained analyses may not result in an estimable model. Thus, information contained in ordinal variables may not be fully exploited by researchers. We develop a penalized latent class model to facilitate analysis of high-dimensional ordinal data. By stabilizing maximum likelihood estimation, we are able to fit an ordinal latent class model that would otherwise not be identifiable without application of strict constraints. We illustrate our methodology in a study of schwannoma, a peripheral nerve sheath tumor, that included 3 clinical subtypes and 23 ordinal histological measures.

Analysis of familial aggregation studies with complex ascertainment schemes

Matthews, A. G., Finkelstein, D. M., & Betensky, R. A. (n.d.).

Publication year

2008

Journal title

Statistics in Medicine

Volume

27

Issue

24

Page(s)

5076-5092

10.1002/sim.3327

Abstract

Abstract

Familial aggregation studies are a common first step in the identification of genetic determinants of disease. If aggregation is found, more refined genetic studies may be undertaken. Complex ascertainment schemes are frequently employed to ensure that the sample contains a sufficient number of families with multiple affected members, as required to detect aggregation. For example, an eligibility criterion for a family might be that both the mother and daughter have disease. Adjustments must be made for ascertainment to avoid bias. We propose adjusting for complex ascertainment schemes through a joint model for the outcomes of disease and ascertainment. This approach improves upon previous simplifying assumptions regarding the ascertainment process.

Estimating time-to-event from longitudinal ordinal data using random-effects Markov models: Application to multiple sclerosis progression

Mandel, M., & Betensky, R. A. (n.d.).

Publication year

2008

Journal title

Biostatistics

Volume

9

Issue

4

Page(s)

750-764

10.1093/biostatistics/kxn008

Abstract

Abstract

Longitudinal ordinal data are common in many scientific studies, including those of multiple sclerosis (MS), and are frequently modeled using Markov dependency. Several authors have proposed random-effects Markov models to account for heterogeneity in the population. In this paper, we go one step further and study prediction based on random-effects Markov models. In particular, we show how to calculate the probabilities of future events and confidence intervals for those probabilities, given observed data on the ordinal outcome and a set of covariates, and how to update them over time. We discuss the usefulness of depicting these probabilities for visualization and interpretation of model results and illustrate our method using data from a phase III clinical trial that evaluated the utility of interferon beta-1a (trademark Avonex) to MS patients of type relapsing-remitting.

Immunohistochemical analysis supports a role for INI1/SMARCB1 in hereditary forms of schwannomas, but not in solitary, sporadic schwannomas

Predicting clinical progression in multiple sclerosis with the magnetic resonance disease severity scale

Prognostic value of tumor microinvasion and metalloproteinases expression in intracranial pediatric ependymomas

Simultaneous confidence intervals based on the percentile bootstrap approach

Topotecan as salvage therapy for relapsed or refractory primary central nervous system lymphoma