A. James O'Malley
Bio: A. James O'Malley is an academic researcher from The Dartmouth Institute for Health Policy and Clinical Practice. The author has contributed to research in topics: Health care & Population. The author has an hindex of 50, co-authored 218 publications receiving 11741 citations. Previous affiliations of A. James O'Malley include Dartmouth College & Harvard University.
Papers published on a yearly basis
TL;DR: GnRH agonist treatment for men with locoregional prostate cancer may be associated with an increased risk of incident diabetes and cardiovascular disease and the benefits of GnRH agonists should be weighed against these potential risks.
Abstract: Purpose Androgen deprivation therapy with a gonadotropin-releasing hormone (GnRH) agonist is associated with increased fat mass and insulin resistance in men with prostate cancer, but the risk of obesity-related disease during treatment has not been well studied. We assessed whether androgen deprivation therapy is associated with an increased incidence of diabetes and cardiovascular disease. Patients and Methods Observational study of a population-based cohort of 73,196 fee-for-service Medicare enrollees age 66 years or older who were diagnosed with locoregional prostate cancer during 1992 to 1999 and observed through 2001. We used Cox proportional hazards models to assess whether treatment with GnRH agonists or orchiectomy was associated with diabetes, coronary heart disease, myocardial infarction, and sudden cardiac death. Results More than one third of men received a GnRH agonist during follow-up. GnRH agonist use was associated with increased risk of incident diabetes (adjusted hazard ratio [HR], 1.44...
TL;DR: The measures of accuracy—sensitivity, specificity, and area under the curve (AUC)—that use the ROC curve are reviewed, and how these measures can be applied using the evaluation of a hypothetical new diagnostic test as an example are illustrated.
Abstract: Receiver-operating characteristic (ROC) analysis was originally developed during World War II to analyze classification accuracy in differentiating signal from noise in radar detection.1 Recently, the methodology has been adapted to several clinical areas heavily dependent on screening and diagnostic tests,2–4 in particular, laboratory testing,5 epidemiology,6 radiology,7–9 and bioinformatics.10 ROC analysis is a useful tool for evaluating the performance of diagnostic tests and more generally for evaluating the accuracy of a statistical model (eg, logistic regression, linear discriminant analysis) that classifies subjects into 1 of 2 categories, diseased or nondiseased. Its function as a simple graphical tool for displaying the accuracy of a medical diagnostic test is one of the most well-known applications of ROC curve analysis. In Circulation from January 1, 1995, through December 5, 2005, 309 articles were published with the key phrase “receiver operating characteristic.” In cardiology, diagnostic testing plays a fundamental role in clinical practice (eg, serum markers of myocardial necrosis, cardiac imaging tests). Predictive modeling to estimate expected outcomes such as mortality or adverse cardiac events based on patient risk characteristics also is common in cardiovascular research. ROC analysis is a useful tool in both of these situations. In this article, we begin by reviewing the measures of accuracy—sensitivity, specificity, and area under the curve (AUC)—that use the ROC curve. We also illustrate how these measures can be applied using the evaluation of a hypothetical new diagnostic test as an example. A diagnostic classification test typically yields binary, ordinal, or continuous outcomes. The simplest type, binary outcomes, arises from a screening test indicating whether the patient is nondiseased (Dx=0) or diseased (Dx=1). The screening test indicates whether the patient is likely to be diseased or not. When >2 categories are used, the test data can be on an ordinal rating …
TL;DR: Perioperative mortality was lower after endovascular repair than after open repair (1.2% vs. 4.8%, P<0.001), and the reduction in mortality in long-term survival was similar for the two procedures.
Abstract: Background Randomized trials have shown reductions in perioperative mortality and morbidity with endovascular repair of abdominal aortic aneurysm, as compared with open surgical repair Longer-term survival rates, however, were similar for the two procedures There are currently no long-term, population-based data from the comparison of these strategies Methods We studied perioperative rates of death and complications, long-term survival, rupture, and reinterventions after open as compared with endovascular repair of abdominal aortic aneurysm in propensity-score-matched cohorts of Medicare beneficiaries undergoing repair during the 2001-2004 period, with follow-up until 2005 Results There were 22,830 matched patients undergoing open repair of abdominal aortic aneurysm in each cohort The average age of the patients was 76 years, and approximately 20% were women Perioperative mortality was lower after endovascular repair than after open repair (12% vs 48%, P Conclusions As compared with open repair, endovascular repair of abdominal aortic aneurysm is associated with lower short-term rates of death and complications The survival advantage is more durable among older patients Late reinterventions related to abdominal aortic aneurysm are more common after endovascular repair but are balanced by an increase in laparotomy-related reinterventions and hospitalizations after open surgery
TL;DR: Androgen deprivation therapy with GnRH agonists was associated with an increased risk of diabetes and cardiovascular disease and oral antiandrogen monotherapy was not associated with any outcome studied.
Abstract: Background Previous studies indicate that androgen deprivation therapy for prostate cancer is associated with diabetes and cardiovascular disease among older men. We evaluated the relationship between androgen deprivation therapy and incident diabetes and cardiovascular disease in men of all ages with prostate cancer.
TL;DR: Although the rate of detection of large tumors fell after the introduction of screening mammography, the more favorable size distribution was primarily the result of the additional detection of small tumors.
Abstract: BackgroundThe goal of screening mammography is to detect small malignant tumors before they grow large enough to cause symptoms. Effective screening should therefore lead to the detection of a greater number of small tumors, followed by fewer large tumors over time. MethodsWe used data from the Surveillance, Epidemiology, and End Results (SEER) program, 1975 through 2012, to calculate the tumor-size distribution and size-specific incidence of breast cancer among women 40 years of age or older. We then calculated the size-specific cancer case fatality rate for two time periods: a baseline period before the implementation of widespread screening mammography (1975 through 1979) and a period encompassing the most recent years for which 10 years of follow-up data were available (2000 through 2002). ResultsAfter the advent of screening mammography, the proportion of detected breast tumors that were small (invasive tumors measuring <2 cm or in situ carcinomas) increased from 36% to 68%; the proportion of detecte...
01 Jan 2009
TL;DR: WRITING GROUP MEMBERS Emelia J. Benjamin, MD, SCM, FAHA Michael J. Reeves, PhD Matthew Ritchey, PT, DPT, OCS, MPH Carlos J. Jiménez, ScD, SM Lori Chaffin Jordan,MD, PhD Suzanne E. Judd, PhD
Abstract: WRITING GROUP MEMBERS Emelia J. Benjamin, MD, SCM, FAHA Michael J. Blaha, MD, MPH Stephanie E. Chiuve, ScD Mary Cushman, MD, MSc, FAHA Sandeep R. Das, MD, MPH, FAHA Rajat Deo, MD, MTR Sarah D. de Ferranti, MD, MPH James Floyd, MD, MS Myriam Fornage, PhD, FAHA Cathleen Gillespie, MS Carmen R. Isasi, MD, PhD, FAHA Monik C. Jiménez, ScD, SM Lori Chaffin Jordan, MD, PhD Suzanne E. Judd, PhD Daniel Lackland, DrPH, FAHA Judith H. Lichtman, PhD, MPH, FAHA Lynda Lisabeth, PhD, MPH, FAHA Simin Liu, MD, ScD, FAHA Chris T. Longenecker, MD Rachel H. Mackey, PhD, MPH, FAHA Kunihiro Matsushita, MD, PhD, FAHA Dariush Mozaffarian, MD, DrPH, FAHA Michael E. Mussolino, PhD, FAHA Khurram Nasir, MD, MPH, FAHA Robert W. Neumar, MD, PhD, FAHA Latha Palaniappan, MD, MS, FAHA Dilip K. Pandey, MBBS, MS, PhD, FAHA Ravi R. Thiagarajan, MD, MPH Mathew J. Reeves, PhD Matthew Ritchey, PT, DPT, OCS, MPH Carlos J. Rodriguez, MD, MPH, FAHA Gregory A. Roth, MD, MPH Wayne D. Rosamond, PhD, FAHA Comilla Sasson, MD, PhD, FAHA Amytis Towfighi, MD Connie W. Tsao, MD, MPH Melanie B. Turner, MPH Salim S. Virani, MD, PhD, FAHA Jenifer H. Voeks, PhD Joshua Z. Willey, MD, MS John T. Wilkins, MD Jason HY. Wu, MSc, PhD, FAHA Heather M. Alger, PhD Sally S. Wong, PhD, RD, CDN, FAHA Paul Muntner, PhD, MHSc On behalf of the American Heart Association Statistics Committee and Stroke Statistics Subcommittee Heart Disease and Stroke Statistics—2017 Update
22 Jan 2006
TL;DR: Some of the major results in random graphs and some of the more challenging open problems are reviewed, including those related to the WWW.
Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.
TL;DR: March 5, 2019 e1 WRITING GROUP MEMBERS Emelia J. Virani, MD, PhD, FAHA, Chair Elect On behalf of the American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee.
Abstract: March 5, 2019 e1 WRITING GROUP MEMBERS Emelia J. Benjamin, MD, ScM, FAHA, Chair Paul Muntner, PhD, MHS, FAHA, Vice Chair Alvaro Alonso, MD, PhD, FAHA Marcio S. Bittencourt, MD, PhD, MPH Clifton W. Callaway, MD, FAHA April P. Carson, PhD, MSPH, FAHA Alanna M. Chamberlain, PhD Alexander R. Chang, MD, MS Susan Cheng, MD, MMSc, MPH, FAHA Sandeep R. Das, MD, MPH, MBA, FAHA Francesca N. Delling, MD, MPH Luc Djousse, MD, ScD, MPH Mitchell S.V. Elkind, MD, MS, FAHA Jane F. Ferguson, PhD, FAHA Myriam Fornage, PhD, FAHA Lori Chaffin Jordan, MD, PhD, FAHA Sadiya S. Khan, MD, MSc Brett M. Kissela, MD, MS Kristen L. Knutson, PhD Tak W. Kwan, MD, FAHA Daniel T. Lackland, DrPH, FAHA Tené T. Lewis, PhD Judith H. Lichtman, PhD, MPH, FAHA Chris T. Longenecker, MD Matthew Shane Loop, PhD Pamela L. Lutsey, PhD, MPH, FAHA Seth S. Martin, MD, MHS, FAHA Kunihiro Matsushita, MD, PhD, FAHA Andrew E. Moran, MD, MPH, FAHA Michael E. Mussolino, PhD, FAHA Martin O’Flaherty, MD, MSc, PhD Ambarish Pandey, MD, MSCS Amanda M. Perak, MD, MS Wayne D. Rosamond, PhD, MS, FAHA Gregory A. Roth, MD, MPH, FAHA Uchechukwu K.A. Sampson, MD, MBA, MPH, FAHA Gary M. Satou, MD, FAHA Emily B. Schroeder, MD, PhD, FAHA Svati H. Shah, MD, MHS, FAHA Nicole L. Spartano, PhD Andrew Stokes, PhD David L. Tirschwell, MD, MS, MSc, FAHA Connie W. Tsao, MD, MPH, Vice Chair Elect Mintu P. Turakhia, MD, MAS, FAHA Lisa B. VanWagner, MD, MSc, FAST John T. Wilkins, MD, MS, FAHA Sally S. Wong, PhD, RD, CDN, FAHA Salim S. Virani, MD, PhD, FAHA, Chair Elect On behalf of the American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee
TL;DR: The number of cancer survivors continues to increase because of both advances in early detection and treatment and the aging and growth of the population and for the public health community to better serve these survivors, the American Cancer Society and the National Cancer Institute collaborate to estimate the number of current and future cancer survivors.
Abstract: The number of cancer survivors continues to increase because of both advances in early detection and treatment and the aging and growth of the population. For the public health community to better serve these survivors, the American Cancer Society and the National Cancer Institute collaborate to estimate the number of current and future cancer survivors using data from the Surveillance, Epidemiology, and End Results cancer registries. In addition, current treatment patterns for the most prevalent cancer types are presented based on information in the National Cancer Data Base and treatment-related side effects are briefly described. More than 15.5 million Americans with a history of cancer were alive on January 1, 2016, and this number is projected to reach more than 20 million by January 1, 2026. The 3 most prevalent cancers are prostate (3,306,760), colon and rectum (724,690), and melanoma (614,460) among males and breast (3,560,570), uterine corpus (757,190), and colon and rectum (727,350) among females. More than one-half (56%) of survivors were diagnosed within the past 10 years, and almost one-half (47%) are aged 70 years or older. People with a history of cancer have unique medical and psychosocial needs that require proactive assessment and management by primary care providers. Although there are a growing number of tools that can assist patients, caregivers, and clinicians in navigating the various phases of cancer survivorship, further evidence-based resources are needed to optimize care. CA Cancer J Clin 2016;66:271-289. © 2016 American Cancer Society.