Rebecca A Betensky
Rebecca Betensky
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Chair of the Department of Biostatistics
Professor of Biostatistics
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Professional overview
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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.
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Education
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AB, Mathematics, Harvard University, Cambridge, MAPhD, Statistics, Stanford University, Stanford, CA
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Areas of research and study
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BiologyBiostatisticsNeuroepidemiologyNeurologyNeurostatisticsTranslational science
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Publications
Publications
Local estimation of smooth curves for longitudinal data
AbstractBetensky, R. A. (n.d.).Publication year
1997Journal title
Statistics in MedicineVolume
16Issue
21Page(s)
2429-2445AbstractLongitudinal data are commonly analysed using mixed-effects models in which the population growth curve and individual subjects' growth curves are assumed to be known functions of time. Frequently, polynomial functions are assumed. In practice, however, polynomials may not fit the data and a mechanistic model that could suggest a non-linear function might not be known. Recent, more flexible approaches to these data approximate the underlying population mean curve or the individual subjects' curves using smoothing splines or kernel-based functions. I apply the local likelihood estimation method of Tibshirani and Hastie and estimate smooth population and individual growth curves by assuming that they are approximately linear or quadratic functions of time within overlapping neighbourhoods. This method requires neither complete data, nor that measurements are made at the same time points for each individual. For descriptive purposes, this approach is easy to implement with standard software. Inference for the resulting curve is facilitated by the theory of estimating equations. I illustrate the methods with data sets containing longitudinal measurements of serum neopterin in an AIDS clinical trial, measurements of ultrafiltration rates of high flux membrane dialysers for haemodialysis, and measurements of the volume of air expelled by individuals.Sequential analysis of censored survival data from three treatment groups
AbstractBetensky, R. A. (n.d.).Publication year
1997Journal title
BiometricsVolume
53Issue
3Page(s)
807-822AbstractIn this paper, we propose a simple means of designing and analyzing a sequential procedure for comparing survival data from three treatments with the goal of eventually identifYing the best treatment. Our procedure consists of the concatenation of two sequential tests, as is suggested by Siegmund (1993, Annals of Statistics 21, 464-483) for instantaneous normal responses. The first sequential test is a global test that attempts to detect an overall treatment effect. If one is found, the least promising treatment is eliminated and a second sequential test attempts to identify the better of the two remaining treatments. Although there are three different information time scales to consider corresponding to each pairwise comparison, we show that under certain conditions they may be approximated by a single time scale. This enables us to gain insight into the problem of censored survival data from the more easily understood case of instantaneous normal data. Also, it eliminates the need for intensive computations and simulations for the design and analysis of the procedure.An analysis of correlated multivariate binary data: Application to familial cancers of the ovary and breast
AbstractBetensky, R. A., & Whittemore, A. S. (n.d.).Publication year
1996Journal title
Journal of the Royal Statistical Society. Series C: Applied StatisticsVolume
45Issue
4Page(s)
411-429AbstractThe association between ovarian and breast cancer, both within and between family members, is examined using pooled data from five case-control studies. The occurrences of these diseases in sisters and mothers are analysed using a quadratic exponential model, which is an extension of the model of Zhao and Prentice for correlated univariate data. An advantage of this model is that the associations between pairs of diseases and pairs of relatives, which are of primary importance, are related to simple functions of its parameters. Also, the model applies to non-randomly sampled data, such as the case-control data, because it completely specifies the joint distribution of responses. A major weakness is that it is not immediately applicable to studies of families of different sizes. None-the-less, we find it to be useful under certain conditions, such as rare diseases. Our analysis of the data suggests that the risk of ovarian cancer is highly dependent on maternal history.An O'Brien-Fleming sequential trial for comparing three treatments
AbstractBetensky, R. A. (n.d.).Publication year
1996Journal title
Annals of StatisticsVolume
24Issue
4Page(s)
1765-1791AbstractWe consider a sequential procedure for comparing three treatments with the goal of ultimately selecting the best treatment. This procedure starts with a sequential test to detect an overall treatment difference and eliminates the apparently inferior treatment if this test rejects the equality of the treatments. It then proceeds with a sequential test of the remaining two treatments. We base these sequential tests on the stopping boundaries popularized by O'Brien and Fleming. Our procedure is similar in structure to that used by Siegmund in conjunction with modified repeated significance tests. We compare the performances of the two procedures via a simulation experiment. We derive analytic approximations for an error probability, the power and the expected sample size of our procedure, which we compare to simulated values. Furthermore, we propose a modification of the procedure for the comparison of a standard treatment with experimental treatments.Low-grade, latent prostate cancer volume: Predictor of clinical cancer incidence?
AbstractWhittemore, A. S., Keller, J. B., & Betensky, R. (n.d.).Publication year
1991Journal title
Journal of the National Cancer InstituteVolume
83Issue
17Page(s)
1231-1235AbstractWe hypothesize that each cell in lowgrade (Gleason grade 1-3) prostate cancer tissue is at risk of transformation into a cell which produces a highgrade (Gleason grade 4-5) clinical cancer after a short period of growth. As a consequence, the volume of low-grade, latent cancer tissue in the prostate glands of men at any age determines their incidence rate for high-grade, clinical cancer a few years later. Autopsy and incidence data for both white men and black men support this conclusion, with a tumor growth period of about 7 years. The transformation rate is similar for black men and for white men, about 0.024 high-grade cancers per year per cm3 of lowgrade latent cancer volume. Our hypothesis explains the infrequent occurrence of clinical cancer despite the high prevalence of latent cancer, the steep rise of clinical cancer incidence with age despite the slow rise of latent cancer prevalence with age, and the disparities in clinical cancer incidence among some populations despite their similar latent cancer prevalence. This hypothesis suggests that low-grade cancer volume is a critical determinant of clinical cancer risk. [J Natl Cancer Inst 83:1231-1235, 1991]Actual versus ideal weight in the calculation of surface area: Effects on dose of 11 chemotherapy agents
AbstractGelman, R. S., Tormey, D. C., Betensky, R., Mansour, E. G., Falkson, H. C., Falkson, G., Creech, R. H., & Haller, D. G. (n.d.).Publication year
1987Journal title
Cancer Treatment ReportsVolume
71Issue
10Page(s)
907-911AbstractThis study of 2382 breast, 182 rectal, 817 colon, and 351 lung cancer patients treated with combination chemotherapy on eight phase III Eastern Cooperative Oncology Group protocols indicates that 69% would receive a higher dose of at least one drug if surface area were calculated from actual weight rather than from the minimum of actual and ideal weight. Forty-eight percent of the patients would have at least a 10% increase in drug dose based on actual weight and only 8% would have at least a 25% increase in drug dose based on actual weight. Only on the premenopausal adjuvant breast cancer protocol and among women on the rectal adjuvant study do the differences in dose based on actual rather than ideal weight increase significantly with age. On the postmenopausal adjuvant breast study and on the lung cancer study, the differences in dose decrease significantly with age. For all age decades and both sexes within each protocol, the mean differences between dose based on actual and dose based on ideal weights were on the same order as the rounding factors for the 11 drugs studied. From the literature on the effects of doses of common chemotherapies on leukopenia, it appears that the percent of hematologic toxicity would not be raised to unacceptable levels by using actual weight to set doses.