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
15th Annual University of Pennsylvania conference on statistical issues in clinical trial/advances in time-to-event analyses in clinical trials (afternoon panel discussion)
AbstractBebu, I., Betensky, R., & Fay, M. P. (n.d.).Publication year
2024Journal title
Clinical TrialsVolume
21Issue
5Page(s)
612-622Abstract~A boundary crossing probability for the bessel process
AbstractBetensky, R. (n.d.).Publication year
1998Journal title
Advances in Applied ProbabilityVolume
30Issue
3Page(s)
807-830AbstractAnalytic approximations are derived for the distribution of the first crossing time of a straight-line boundary by a d-dimensional Bessel process and its discrete time analogue. Themain ingredient for the approximations is the conditional probability that the process crossed the boundary before time m, given its location beneath the boundary at time m. The boundary crossing probability is of interest as the significance level and power of a sequential test comparing d + 1 treatments using an O Brien–Fleming (1979) stopping boundary (see Betensky 1996). Also, it is shown by DeLong (1980) to be the limiting distribution of a nonparametric test statistic for multiple regression. The approximations are compared with exact values from the literature and with values from a Monte Carlo simulation.A boundary crossing probability for the Bessel process
AbstractBetensky, R. (n.d.).Publication year
1998Journal title
Advances in Applied ProbabilityVolume
30Issue
3Page(s)
807-830AbstractAnalytic approximations are derived for the distribution of the first crossing time of a straight-line boundary by a d-dimensional Bessel process and its discrete time analogue. The main ingredient for the approximations is the conditional probability that the process crossed the boundary before time m, given its location beneath the boundary at time m. The boundary crossing probability is of interest as the significance level and power of a sequential test comparing d + 1 treatments using an O'Brien-Fleming (1979) stopping boundary (see Betensky 1996). Also, it is shown by DeLong (1980) to be the limiting distribution of a nonparametric test statistic for multiple regression. The approximations are compared with exact values from the literature and with values from a Monte Carlo simulation.A classic twin study of external ear malformations, including microtia
AbstractArtunduaga, M. A., Quintanilla-Dieck, M. D., Greenway, S., Betensky, R., Nicolau, Y., Hamdan, U., Jarrin, P., Osorno, G., Brent, B., Eavey, R., Seidman, C., & Seidman, J. G. (n.d.).Publication year
2009Journal title
New England Journal of MedicineVolume
361Issue
12Page(s)
1216-1218Abstract~A comparison of models for clustered binary outcomes : Analysis of a designed immunology experiment
AbstractBetensky, R., Williams, P. L., & Lederman, H. M. (n.d.).Publication year
2001Journal title
Journal of the Royal Statistical Society. Series C: Applied StatisticsVolume
50Issue
1Page(s)
43-61AbstractThe lymphocyte proliferative assay (LPA) of immune competence was conducted on 52 subjects, with up to 36 processing conditions per subject, to evaluate whether samples could be shipped or stored overnight, rather than being processed on fresh blood as currently required. The LPA study resulted in clustered binary data, with both cluster level and cluster-varying covariates. Two modelling strategies for the analysis of such clustered binary data are through the cluster-specific and population-averaged approaches. Whereas most research in this area has focused on the analysis of matched pairs data, in many situations, such as the LPA study, cluster sizes are naturally larger. Through considerations of interpretation and efficiency of these models when applied to large clusters, the mixed effect cluster-specific model was selected as most appropriate for the analysis of the LPA data. The model confirmed that the LPA response is significantly impaired in individuals infected with the human immunodeficiency virus (HIV). The LPA response was found to be significantly lower for shipped and overnight samples than for fresh samples, and this effect was significantly stronger among HIV-infected individuals. Surprisingly, an anticoagulant effect was not detected.A computationally simple bivariate survival estimator for efficacy and safety
AbstractScholtens, D., & Betensky, R. (n.d.).Publication year
2006Journal title
Lifetime Data AnalysisVolume
12Issue
3Page(s)
365-387AbstractBoth treatment efficacy and safety are typically the primary endpoints in Phase II, and even in some Phase III, clinical trials. Efficacy is frequently measured by time to response, death, or some other milestone event and thus is a continuous, possibly censored, outcome. Safety, however, is frequently measured on a discrete scale; in Eastern Cooperative Oncology Group clinical trial E2290, it was measured as the number of weekly rounds of chemotherapy that were tolerable to colorectal cancer patients. For the joint analysis of efficacy and safety, we propose a non-parametric, computationally simple estimator for the bivariate survival function when one time-to-event is continuous, one is discrete, and both are subject to right-censoring. The bivariate censoring times may depend on each other, but they are assumed to be independent of both event times. We derive a closed-form covariance estimator for the survivor function which allows for inference to be based on any of several possible statistics of interest. In addition, we derive its covariance with respect to calendar time of analysis, allowing for its use in sequential studies.A computationally simple test of homogeneity of odds ratios for twin data
AbstractBetensky, R., Hudson, J. I., Jones, C. A., Hu, F., Wang, B., Chen, C., & Xu, X. (n.d.).Publication year
2001Journal title
Genetic EpidemiologyVolume
20Issue
2Page(s)
228-238AbstractIt is of interest to compare measures of association of binary traits among samples of bivariate data. One example is the comparison of association within a sample of monozygotic (MZ) twins to that within a sample of dizygotic (DZ) twins. A larger association in the MZ twins suggests that the trait of interest may have a genetic component. The Bivariate data in this example are binary traits for the twins in each pair. Another example is the comparison of a measure of Hardy-Weinberg disequilibrium across several populations. The bivariate data in this example are the two alleles comprising the genotype of interest. We propose using derived logistic regression equations from the full exponential model for the bivariate outcomes to test for homogeneity. We adjust for correlation among outcomes via generalized estimating equations. This modeling approach allows for adjustment for individual-level and pair-level covariates and thereby allows for testing for gene x environment interactions. Further, we extend the model to allow for simultaneous analysis of two diseases, which allows for testing for a genetic component to the coaggregation of two diseases. In contrast to approaches proposed by previous authors, no special software is required; our approach can be easily implemented in standard software packages. We compare our results to those of other methods proposed in the literature for data from the Vietnam Era Twin Study. We apply our methods also to the Anqing Twin Study and data on major depression and generalized anxiety disorder from the Virginia Twin Register.A computationally tractable multivariate random effects model for clustered binary data
AbstractCoull, B. A., Houseman, E. A., & Betensky, R. (n.d.).Publication year
2006Journal title
BiometrikaVolume
93Issue
3Page(s)
587-599AbstractWe consider a multivariate random effects model for clustered binary data that is useful when interest focuses on the association structure among clustered observations. Based on a vector of gamma random effects and a complementary log-log link function, the model yields a likelihood that has closed form, making a frequentist approach to model-fitting straightforward. This closed form yields several advantages over existing methods, including easy inspection of model identifiability and straightforward adjustment for nonrandom ascertainment of subjects, such as that which occurs in family studies of disease aggregation. We use the proposed model to analyse two different binary datasets concerning disease outcome data from a familial aggregation study of breast and ovarian cancer in women and loss of heterozygosity outcomes from a brain tumour study.A concept-wide association study of clinical notes to discover new predictors of kidney failure
AbstractSingh, K., Betensky, R., Wright, A., Curhan, G. C., Bates, D. W., & Waikar, S. S. (n.d.).Publication year
2016Journal title
Clinical Journal of the American Society of NephrologyVolume
11Issue
12Page(s)
2150-2158AbstractBackground and objectives Identifying predictors of kidney disease progression is critical toward the development of strategies to prevent kidney failure. Clinical notes provide a unique opportunity for big data approaches to identify novel risk factors for disease. Design, setting, participants, &measurements Weusednatural language processing tools to extract concepts from the preceding year’s clinical notes among patients newly referred to a tertiary care center’s outpatient nephrology clinics and retrospectively evaluated these concepts as predictors for the subsequent development of ESRD using proportional subdistribution hazards (competing risk) regression. The primary outcome was time to ESRD, accounting for a competing risk of death. We identified predictors from univariate and multivariate (adjusting for Tangri linear predictor) models using a5%threshold for falsediscovery rate (q value, 0.05).Weincluded allpatients seen by an adult outpatient nephrologist between January 1, 2004 and June 18, 2014 and excluded patients seen only by transplant nephrology, with preexisting ESRD, with fewer than five clinical notes, with no follow-up, or with no baseline creatinine values. Results Among the 4013 patients selected in the final study cohort, we identified 960 concepts in the unadjusted analysis and 885 concepts in the adjusted analysis. Novel predictors identified included high-dose ascorbic acid (adjusted hazard ratio, 5.48; 95%confidence interval, 2.80 to 10.70; q, 0.001) and fast food (adjusted hazard ratio, 4.34; 95% confidence interval, 2.55 to 7.40; q, 0.001). Conclusions Novel predictors of human disease may be identified using an unbiased approach to analyze text from the electronic health record.A latent class model with hidden markov dependence for array CGH data
AbstractDesantis, S. M., Houseman, E. A., Coull, B. A., Louis, D. N., Mohapatra, G., & Betensky, R. (n.d.).Publication year
2009Journal title
BiometricsVolume
65Issue
4Page(s)
1296-1305AbstractArray CGH is a high-throughput technique designed to detect genomic alterations linked to the development and progression of cancer. The technique yields fluorescence ratios that characterize DNA copy number change in tumor versus healthy cells. Classification of tumors based on aCGH profiles is of scientific interest but the analysis of these data is complicated by the large number of highly correlated measures. In this article, we develop a supervised Bayesian latent class approach for classification that relies on a hidden Markov model to account for the dependence in the intensity ratios. Supervision means that classification is guided by a clinical endpoint. Posterior inferences are made about class-specific copy number gains and losses. We demonstrate our technique on a study of brain tumors, for which our approach is capable of identifying subsets of tumors with different genomic profiles, and differentiates classes by survival much better than unsupervised methods.A local likelihood proportional hazards model for interval censored data
AbstractBetensky, R., Lindsey, J. C., Ryan, L. M., & Wand, M. P. (n.d.).Publication year
2002Journal title
Statistics in MedicineVolume
21Issue
2Page(s)
263-275AbstractWe discuss the use of local likelihood methods to fit proportional hazards regression models to right and interval censored data. The assumed model allows for an arbitrary, smoothed baseline hazard on which a vector of covariates operates in a proportional manner, and thus produces an interpretable baseline hazard function along with estimates of global covariate effects. For estimation, we extend the modified EM algorithm suggested by Betensky, Lindsey, Ryan and Wand. We illustrate the method with data on times to deterioration of breast cosmeses and HIV-1 infection rates among haemophiliacs.A non-parametric maximum likelihood estimator for bivariate interval censored data
AbstractBetensky, R., & Finkelstein, D. M. (n.d.).Publication year
1999Journal title
Statistics in MedicineVolume
18Issue
22Page(s)
3089-3100AbstractWe derive a non-parametric maximum likelihood estimator for bivariate interval censored data using standard techniques for constrained convex optimization. Our approach extends those taken for univariate interval censored data. We illustrate the estimator with bivariate data from an AIDS study.A novel signal processing approach for the detection of copy number variations in the human genome
AbstractStamoulis, C., & Betensky, R. (n.d.).Publication year
2012Journal title
BioinformaticsVolume
28Issue
9Page(s)
1297Abstract~A novel signal processing approach for the detection of copy number variations in the human genome
AbstractStamoulis, C., & Betensky, R. (n.d.).Publication year
2011Journal title
BioinformaticsVolume
27Issue
17Page(s)
2338-2345AbstractMotivation: Human genomic variability occurs at different scales, from single nucleotide polymorphisms (SNPs) to large DNA segments. Copy number variations (CNVs) represent a signicant part of our genetic heterogeneity and have also been associated with many diseases and disorders. Short, localized CNVs, which may play an important role in human disease, may be undetectable in noisy genomic data. Therefore, robust methodologies are needed for their detection. Furthermore, for meaningful identication of pathological CNVs, estimation of normal allelic aberrations is necessary. Results: We developed a signal processing-based methodology for sequence denoising followed by pattern matching, to increase SNR in genomic data and improve CNV detection. We applied this signal-decomposition-matched ltering (SDMF) methodology to 429 normal genomic sequences, and compared detected CNVs to those in the Database of Genomic Variants. SDMF successfully detected a signicant number of previously identied CNVs with frequencies of occurrence ≥10%, as well as unreported short CNVs. Its performance was also compared to circular binary segmentation (CBS). through simulations. SDMF had a signicantly lower false detection rate and was signicantly faster than CBS, an important advantage for handling large datasets generated with high-resolution arrays. By focusing on improving SNR (instead of the robustness of the detection algorithm), SDMF is a very promising methodology for identifying CNVs at all genomic spatial scales.A Pairwise Naïve Bayes Approach to Bayesian Classification
AbstractAsafu-Adjei, J. K., & Betensky, R. (n.d.).Publication year
2015Journal title
International Journal of Pattern Recognition and Artificial IntelligenceVolume
29Issue
7AbstractDespite the relatively high accuracy of the naïve Bayes (NB) classifier, there may be several instances where it is not optimal, i.e. does not have the same classification performance as the Bayes classifier utilizing the joint distribution of the examined attributes. However, the Bayes classifier can be computationally intractable due to its required knowledge of the joint distribution. Therefore, we introduce a "pairwise naïve" Bayes (PNB) classifier that incorporates all pairwise relationships among the examined attributes, but does not require specification of the joint distribution. In this paper, we first describe the necessary and sufficient conditions under which the PNB classifier is optimal. We then discuss sufficient conditions for which the PNB classifier, and not NB, is optimal for normal attributes. Through simulation and actual studies, we evaluate the performance of our proposed classifier relative to the Bayes and NB classifiers, along with the HNB, AODE, LBR and TAN classifiers, using normal density and empirical estimation methods. Our applications show that the PNB classifier using normal density estimation yields the highest accuracy for data sets containing continuous attributes. We conclude that it offers a useful compromise between the Bayes and NB classifiers.A penalized latent class model for ordinal data
AbstractDesantis, S. M., Houseman, E. A., Coull, B. A., Stemmer-Rachamimov, A., & Betensky, R. (n.d.).Publication year
2008Journal title
BiostatisticsVolume
9Issue
2Page(s)
249-262AbstractLatent 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.A pilot protocol to assess the feasibility of a virtual multiple crossover, randomized controlled trial design using methylphenidate in mild cognitive impairment
AbstractDesRuisseaux, L. A., Williams, V. J., McManus, A. J., Gupta, A. S., Carlyle, B. C., Azami, H., Gerber, J. A., Bolling, A. M., Cook, C. L., Betensky, R., & Arnold, S. E. (n.d.).Publication year
2020Journal title
TrialsVolume
21Issue
1AbstractBackground: The conventional clinical trial design in Alzheimer’s disease (AD) and AD-related disorders (ADRDs) is the parallel-group randomized controlled trial. However, in heterogeneous disorders like AD/ADRDs, this design requires large sample sizes to detect meaningful effects in an “average” patient. They are very costly and, despite many attempts, have not yielded new treatments for many years. An alternative, the multi-crossover, randomized control trial (MCRCT) is a design in which each patient serves as their own control across successive, randomized blocks of active treatment and placebo. This design overcomes many limitations of parallel-group trials, yielding an unbiased assessment of treatment effect at the individual level (“N-of-1”) regardless of unique patient characteristics. The goal of the present study is to pilot a MCRCT of a potential symptomatic treatment, methylphenidate, for mild-stage AD/ADRDs, testing feasibility and compliance of participants in this design and efficacy of the drug using both standard and novel outcome measures suited for this design. Methods: Ten participants with mild cognitive impairment or mild-stage dementia due to AD/ADRDs will undergo a 4-week lead-in period followed by three, month-long treatment blocks (2 weeks of treatment with methylphenidate, 2 weeks placebo in random order). This trial will be conducted entirely virtually with an optional in-person screening visit. The primary outcome of interest is feasibility as measured by compliance and retention, with secondary and exploratory outcomes including cognition as measured by neuropsychological assessment at the end of each treatment period and daily brain games played throughout the study, actigraphy, and neuropsychiatric and functional assessments. Discussion: This pilot study will gauge the feasibility of conducting a virtual MCRCT for symptomatic treatment in early AD/ADRD. It will also compare home-based daily brain games with standard neuropsychological measures within a clinical trial for AD/ADRD. Particular attention will be paid to compliance, tolerability of drug and participation, learning effects, trends and stability of daily measures across blocks, medication carryover effects, and correlations between standard and brief daily assessments. These data will provide guidance for more efficient trial design and the use of potentially more robust, ecological outcome measures in AD/ADRD research. Trial registration: ClinicalTrials.gov, NCT03811847. Registered on 21 January 2019.A pseudolikelihood approach for simultaneous analysis of array comparative genomic hybridizations
AbstractEngler, D. A., Mohapatra, G., Louis, D. N., & Betensky, R. (n.d.).Publication year
2006Journal title
BiostatisticsVolume
7Issue
3Page(s)
399-421AbstractDNA sequence copy number has been shown to be associated with cancer development and progression. Array-based comparative genomic hybridization (aCGH) is a recent development that seeks to identify the copy number ratio at large numbers of markers across the genome. Due to experimental and biological variations across chromosomes and hybridizations, current methods are limited to analyses of single chromosomes. We propose a more powerful approach that borrows strength across chromosomes and hybridizations. We assume a Gaussian mixture model, with a hidden Markov dependence structure and with random effects to allow for intertumoral variation, as well as intratumoral clonal variation. For ease of computation, we base estimation on a pseudolikelihood function. The method produces quantitative assessments of the likelihood of genetic alterations at each clone, along with a graphical display for simple visual interpretation. We assess the characteristics of the method through simulation studies and analysis of a brain tumor aCGH data set. We show that the pseudolikelihood approach is superior to existing methods both in detecting small regions of copy number alteration and in accurately classifying regions of change when intratumoral clonal variation is present. Software for this approach is available at http://www.biostat.harvard.edu/∼betensky/papers.html.A transformation model for smoothed estimation from interval-censored data
AbstractBi, F., Zhang, R., & Betensky, R. (n.d.).Abstract~Accounting for incomplete testing in the estimation of epidemic parameters
AbstractBetensky, R., & Feng, Y. (n.d.).Publication year
2020Journal title
International Journal of EpidemiologyVolume
49Issue
5Page(s)
1419-1426Abstract~Accounting for incomplete testing in the estimation of epidemic parameters
AbstractBetensky, R., & Feng, Y. (n.d.).Publication year
2020Journal title
International Journal of EpidemiologyVolume
49Issue
5Page(s)
1419-1426Abstract~Accuracy of digital versus conventional implant impressions
AbstractLee, S. J., Betensky, R., Gianneschi, G. E., & Gallucci, G. O. (n.d.).Publication year
2015Journal title
Clinical Oral Implants ResearchVolume
26Issue
6Page(s)
715-719AbstractObjective: The accuracy of digital impressions greatly influences their clinical viability in implant restorations. The aim of this study was to compare the accuracy of gypsum models acquired from the conventional implant impression to digitally milled models created from direct digitalization by three-dimensional analysis. Materials and Methods: Thirty gypsum and 30 digitally milled models, impressed directly from a reference model, were prepared. The models and reference model were scanned by a laboratory scanner, and 30 surface tessellation language datasets from each group were imported to an inspection software program. The datasets were aligned to the reference dataset by a repeated best-fit algorithm, and 10 specified contact locations of interest were measured in mean volumetric deviations. The areas were pooled by cusps, fossae, interproximal contacts, horizontal and vertical axes of implant position and angulation. The pooled areas were statistically analysed by comparing each group to the reference model to investigate the mean volumetric deviations accounting for accuracy and standard deviations for precision. Results: Milled models from digital impressions had comparable accuracy to gypsum models from conventional impressions. However, differences in fossae and vertical displacement of the implant position from the gypsum and digitally milled models compared to the reference model exhibited statistical significance (P < 0.001, P = 0.020, respectively). Conclusion: Milled models from digital impression are comparable to gypsum models from conventional impression.Activation of STAT3, MAPK, and AKT in malignant astrocytic gliomas : Correlation with EGFR status, tumor grade, and survival
AbstractMizoguchi, M., Betensky, R., Batchelor, T. T., Bernay, D. C., Louis, D. N., & Nutt, C. L. (n.d.).Publication year
2006Journal title
Journal of Neuropathology and Experimental NeurologyVolume
65Issue
12Page(s)
1181-1188AbstractDiffuse astrocytic gliomas are the most common human glial tumors with glioblastoma being the most malignant form. Epidermal growth factor receptor (EGFR) gene amplification is one of the most common genetic changes in glioblastoma and can lead to the activation of various downstream signaling molecules, including STAT3, MAPK, and AKT. In this study, we investigated the activation status of these 3 signaling molecules as well as wild-type (EGFRwt) and mutant (EGFRvIII) EGFR in 82 malignant astrocytic gliomas (55 glioblastomas and 27 anaplastic astrocytomas) using immunohistochemistry. The presence of EGFRwt, but not EGFRvIII, immunopositivity correlated significantly with prevalent EGFR gene amplification in glioblastomas. STAT3 and AKT activation correlated significantly with EGFR status, although the correlation for p-STAT3 was attributed exclusively to EGFRvIII. The distribution of these 3 activated molecules varied significantly with tumor grade; although activation of STAT3 was essentially identical between anaplastic astrocytomas and glioblastomas, an increase in the activation of MAPK and AKT appeared to correlate with the progression of anaplastic astrocytoma to glioblastoma. Finally, activated STAT3 and AKT were marginally predictive of improved and worse prognosis, respectively. Taken together, these findings begin to elucidate the interrelationship between these signaling pathways in astrocytic gliomas in vivo.Actual versus ideal weight in the calculation of surface area : Effects on dose of 11 chemotherapy agents
AbstractBetensky, R., Gelman, 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.Age-Dependent Prognostic Effects of Genetic Alterations in Glioblastoma
AbstractBatchelor, T. T., Betensky, R., Esposito, J. M., Pham, L. D., Dorfman, M. V., Piscatelli, N., Jhung, S., Rhee, D., & Louis, D. N. (n.d.).Publication year
2004Journal title
Clinical Cancer ResearchVolume
10Issue
1 IPage(s)
228-233AbstractPurpose: Although the genetic alterations in glioblastoma have been well characterized, reports regarding their prognostic effects have been inconsistent. Experimental Design: In this series of 140 consecutive cases of glioblastoma treated at a single center, we analyzed the frequency, age dependency and prognostic effects of TP53 mutation, CDKN2A/p16 deletion, EGFR amplification, as well as loss of chromosome 1p, chromosome 10q, and chromosome 19q. The complete set of genetic alterations was available on 60 of 140 patients. Results: In this cohort of glioblastoma cases, TP53 mutation was significantly associated with patient age. The prognostic effects of TP53 mutation, EGFR amplification, CDKN2A/p16 alterations, and loss of chromosome 1p were dependent on the age of the patient. Conclusions: This is the first observation that the prognostic effects of TP53, 1p, and CDKN2A/p16 alterations are dependent on patient age. These observations concerning the interactions of age and genetic changes in glioblastoma suggest that tumorigenic pathways to glioblastoma vary with the age of the patient and that future molecular marker studies should carefully evaluate the potential age-dependent prognostic effects of these biological variables. The inconsistent or negative prognostic effects of molecular markers reported in prior studies of glioblastoma may be because different effects at different ages may have resulted in a cancellation of an overall effect in the entire cohort.