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BIOGRAPHICAL SKETCH Garrett M. Fitzmaurice Associate Professor Of
Principal Investigator/Program Director (Last, First, Middle): BIOGRAPHICAL SKETCH Provide the following information for the key personnel and other significant contributors in the order listed on Form Page 2. Follow this format for each person. DO NOT EXCEED FOUR PAGES. NAME POSITION TITLE Garrett M. Fitzmaurice Associate Professor of Biostatistics eRA COMMONS USER NAME EDUCATION/TRAINING (Begin with baccalaureate or other initial professional education, such as nursing, and include postdoctoral training.) DEGREE INSTITUTION AND LOCATION YEAR(s) FIELD OF STUDY (if applicable) National University of Ireland BA, MA 1983, 1987 Psychology University of London MSc 1986 Quantitative Methods Harvard University ScD 1993 Biostatistics Professional Experience: 1986-1989 Statistician, Department of Psychology, New York University 1989-1990 Teaching Assistant, Department of Biostatistics, Harvard School of Public Health 1990-1993 Teaching Fellow in Biostatistics, Department of Biostatistics, Harvard SPH 1993-1994 Post-doctoral Research Fellow, Department of Biostatistics, Harvard SPH 1994-1997 Research Fellow, Nuffield College, Oxford University, United Kingdom 1997-1999 Assistant Professor of Biostatistics, Harvard School of Public Health 1999-present Associate Professor of Biostatistics, Harvard School of Public Health 2004-present Associate Professor of Medicine (Biostatistics), Harvard Medical School 2004-present Biostatistician, Division of General Medicine, Brigham and Women’s Hospital, Boston 2006-present Foreign Adjunct Professor of Biostatistics, -
TUTORIAL in BIOSTATISTICS: the Self-Controlled Case Series Method
STATISTICS IN MEDICINE Statist. Med. 2005; 0:1–31 Prepared using simauth.cls [Version: 2002/09/18 v1.11] TUTORIAL IN BIOSTATISTICS: The self-controlled case series method Heather J. Whitaker1, C. Paddy Farrington1, Bart Spiessens2 and Patrick Musonda1 1 Department of Statistics, The Open University, Milton Keynes, MK7 6AA, UK. 2 GlaxoSmithKline Biologicals, Rue de l’Institut 89, B-1330 Rixensart, Belgium. SUMMARY The self-controlled case series method was developed to investigate associations between acute outcomes and transient exposures, using only data on cases, that is, on individuals who have experienced the outcome of interest. Inference is within individuals, and hence fixed covariates effects are implicitly controlled for within a proportional incidence framework. We describe the origins, assumptions, limitations, and uses of the method. The rationale for the model and the derivation of the likelihood are explained in detail using a worked example on vaccine safety. Code for fitting the model in the statistical package STATA is described. Two further vaccine safety data sets are used to illustrate a range of modelling issues and extensions of the basic model. Some brief pointers on the design of case series studies are provided. The data sets, STATA code, and further implementation details in SAS, GENSTAT and GLIM are available from an associated website. key words: case series; conditional likelihood; control; epidemiology; modelling; proportional incidence Copyright c 2005 John Wiley & Sons, Ltd. 1. Introduction The self-controlled case series method, or case series method for short, provides an alternative to more established cohort or case-control methods for investigating the association between a time-varying exposure and an outcome event. -
The Meaning of Probability
CHAPTER 2 THE MEANING OF PROBABILITY INTRODUCTION by Glenn Shafer The meaning of probability has been debated since the mathematical theory of probability was formulated in the late 1600s. The five articles in this section have been selected to provide perspective on the history and present state of this debate. Mathematical statistics provided the main arena for debating the meaning of probability during the nineteenth and early twentieth centuries. The debate was conducted mainly between two camps, the subjectivists and the frequentists. The subjectivists contended that the probability of an event is the degree to which someone believes it, as indicated by their willingness to bet or take other actions. The frequentists contended that probability of an event is the frequency with which it occurs. Leonard J. Savage (1917-1971), the author of our first article, was an influential subjectivist. Bradley Efron, the author of our second article, is a leading contemporary frequentist. A newer debate, dating only from the 1950s and conducted more by psychologists and economists than by statisticians, has been concerned with whether the rules of probability are descriptive of human behavior or instead normative for human and machine reasoning. This debate has inspired empirical studies of the ways people violate the rules. In our third article, Amos Tversky and Daniel Kahneman report on some of the results of these studies. In our fourth article, Amos Tversky and I propose that we resolve both debates by formalizing a constructive interpretation of probability. According to this interpretation, probabilities are degrees of belief deliberately constructed and adopted on the basis of evidence, and frequencies are only one among many types of evidence. -
Teen Stabbing Questions Still Unanswered What Motivated 14-Year-Old Boy to Attack Family?
Save $86.25 with coupons in today’s paper Penn State holds The Kirby at 30 off late Honoring the Center’s charge rich history and its to beat Temple impact on the region SPORTS • 1C SPECIAL SECTION Sunday, September 18, 2016 BREAKING NEWS AT TIMESLEADER.COM '365/=[+<</M /88=C6@+83+sǍL Teen stabbing questions still unanswered What motivated 14-year-old boy to attack family? By Bill O’Boyle Sinoracki in the chest, causing Sinoracki’s wife, Bobbi Jo, 36, ,9,9C6/Ľ>37/=6/+./<L-97 his death. and the couple’s 17-year-old Investigators say Hocken- daughter. KINGSTON TWP. — Specu- berry, 14, of 145 S. Lehigh A preliminary hearing lation has been rampant since St. — located adjacent to the for Hockenberry, originally last Sunday when a 14-year-old Sinoracki home — entered 7 scheduled for Sept. 22, has boy entered his neighbors’ Orchard St. and stabbed three been continued at the request house in the middle of the day members of the Sinoracki fam- of his attorney, Frank Nocito. and stabbed three people, kill- According to the office of ing one. ily. Hockenberry is charged Magisterial District Justice Everyone connected to the James Tupper and Kingston case and the general public with homicide, aggravated assault, simple assault, reck- Township Police Chief Michael have been wondering what Moravec, the hearing will be lessly endangering another Photo courtesy of GoFundMe could have motivated the held at 9:30 a.m. Nov. 7 at person and burglary in connec- In this photo taken from the GoFundMe account page set up for the Sinoracki accused, Zachary Hocken- Tupper’s office, 11 Carverton family, David Sinoracki is shown with his wife, Bobbi Jo, and their three children, berry, to walk into a home on tion with the death of David Megan 17; Madison, 14; and David Jr., 11. -
Biometrics & Biostatistics
Hanley and Moodie, J Biomet Biostat 2011, 2:5 Biometrics & Biostatistics http://dx.doi.org/10.4172/2155-6180.1000124 Research Article Article OpenOpen Access Access Sample Size, Precision and Power Calculations: A Unified Approach James A Hanley* and Erica EM Moodie Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Canada Abstract The sample size formulae given in elementary biostatistics textbooks deal only with simple situations: estimation of one, or a comparison of at most two, mean(s) or proportion(s). While many specialized textbooks give sample formulae/tables for analyses involving odds and rate ratios, few deal explicitly with statistical considera tions for slopes (regression coefficients), for analyses involving confounding variables or with the fact that most analyses rely on some type of generalized linear model. Thus, the investigator is typically forced to use “black-box” computer programs or tables, or to borrow from tables in the social sciences, where the emphasis is on cor- relation coefficients. The concern in the – usually very separate – modules or stand alone software programs is more with user friendly input and output. The emphasis on numerical exactness is particularly unfortunate, given the rough, prospective, and thus uncertain, nature of the exercise, and that different textbooks and software may give different sample sizes for the same design. In addition, some programs focus on required numbers per group, others on an overall number. We present users with a single universal (though sometimes approximate) formula that explicitly isolates the impacts of the various factors one from another, and gives some insight into the determinants for each factor. -
The American Statistician
This article was downloaded by: [T&F Internal Users], [Rob Calver] On: 01 September 2015, At: 02:24 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: 5 Howick Place, London, SW1P 1WG The American Statistician Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/utas20 Reviews of Books and Teaching Materials Published online: 27 Aug 2015. Click for updates To cite this article: (2015) Reviews of Books and Teaching Materials, The American Statistician, 69:3, 244-252, DOI: 10.1080/00031305.2015.1068616 To link to this article: http://dx.doi.org/10.1080/00031305.2015.1068616 PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. -
Strength in Numbers: the Rising of Academic Statistics Departments In
Agresti · Meng Agresti Eds. Alan Agresti · Xiao-Li Meng Editors Strength in Numbers: The Rising of Academic Statistics DepartmentsStatistics in the U.S. Rising of Academic The in Numbers: Strength Statistics Departments in the U.S. Strength in Numbers: The Rising of Academic Statistics Departments in the U.S. Alan Agresti • Xiao-Li Meng Editors Strength in Numbers: The Rising of Academic Statistics Departments in the U.S. 123 Editors Alan Agresti Xiao-Li Meng Department of Statistics Department of Statistics University of Florida Harvard University Gainesville, FL Cambridge, MA USA USA ISBN 978-1-4614-3648-5 ISBN 978-1-4614-3649-2 (eBook) DOI 10.1007/978-1-4614-3649-2 Springer New York Heidelberg Dordrecht London Library of Congress Control Number: 2012942702 Ó Springer Science+Business Media New York 2013 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer. -
Cramer, and Rao
2000 PARZEN PRIZE FOR STATISTICAL INNOVATION awarded by TEXAS A&M UNIVERSITY DEPARTMENT OF STATISTICS to C. R. RAO April 24, 2000 292 Breezeway MSC 4 pm Pictures from Parzen Prize Presentation and Reception, 4/24/2000 The 2000 EMANUEL AND CAROL PARZEN PRIZE FOR STATISTICAL INNOVATION is awarded to C. Radhakrishna Rao (Eberly Professor of Statistics, and Director of the Center for Multivariate Analysis, at the Pennsylvania State University, University Park, PA 16802) for outstanding distinction and eminence in research on the theory of statistics, in applications of statistical methods in diverse fields, in providing international leadership for 55 years in directing statistical research centers, in continuing impact through his vision and effectiveness as a scholar and teacher, and in extensive service to American and international society. The year 2000 motivates us to plan for the future of the discipline of statistics which emerged in the 20th century as a firmly grounded mathematical science due to the pioneering research of Pearson, Gossett, Fisher, Neyman, Hotelling, Wald, Cramer, and Rao. Numerous honors and awards to Rao demonstrate the esteem in which he is held throughout the world for his unusually distinguished and productive life. C. R. Rao was born September 10, 1920, received his Ph.D. from Cambridge University (England) in 1948, and has been awarded 22 honorary doctorates. He has worked at the Indian Statistical Institute (1944-1992), University of Pittsburgh (1979- 1988), and Pennsylvania State University (1988- ). Professor Rao is the author (or co-author) of 14 books and over 300 publications. His pioneering contributions (in linear models, multivariate analysis, design of experiments and combinatorics, probability distribution characterizations, generalized inverses, and robust inference) are taught in statistics textbooks. -
An Argument Against Athletes As Political Role Models
Fair Play REVISTA DE FILOSOFÍA, ÉTICA Y DERECHO DEL DEPORTE www.upf.edu/revistafairplay An Argument against Athletes as Political Role Models Shawn E. Klein Arizona State University (USA) Citar este artículo como: Shawn E. Klein (2017): An Argument against Athletes as Political Role Models, Fair Play. Revista de Filosofía, Ética y Derecho del Deporte, vol. 10. FECHA DE RECEPCIÓN: 5 de Abril de 2017 FECHA DE ACEPTAPCIÓN: 13 de Mayo de 2017 "25 An Argument against Athletes as Political Role Models1 Shawn E. Klein Arizona State University (USA) Abstract A common refrain in and outside academia is that prominent sports figures ought to engage more in the public discourse about political issues. This idea parallels the idea that athletes ought to be role models in general. This paper first examines and critiques the “athlete as role model” argument and then applies this critique to the “athlete as political activist” argument. Appealing to the empirical political psychological literature, the paper sketches an argument that athlete activism might actually do more harm than good. Keywords: role model, athlete, activism, obligation, political psychology 1. Introduction Colin Kapernick is both widely criticized and widely praised for his controversial protest during the 2016 NFL season.2 Part of the praise comes from the idea that it is good for people to speak out on political issues.3 The hope is that such activism leads to ‘national conversations’ about hard and divisive topics.4 Maybe if more athletes spoke out politically, like Kaepernick, people in the US would not be as divided and partisan and could work together to solve real problems. -
IISA Newsletter Autumn 2017
http://intindstat.org IISA Newsletter Autumn 2017 Hyderabad (Courtesy: Can Stock Photo Inc./snehitdesign) Contents President’s Message 2 By Sujit K. Ghosh IISA is 25! We are excited to celebrate 25 IISA Election Results 4 years. Look through the following pages to see the photos from the IISA 2017 Conference in Hyderabad 5 past 25 years of our members. IISA 2017 Awards 6 Does Machine Learning Reduce Racial Disparities in Policing? 10 By Jyotishka Datta and Grant Drawve, University of Arkansas, Fayetteville Donate to the Bahadur Lecture Series 13 IISA 2018 Conference in Florida 14 Update Your By Somnath Datta, University of Florida, Gainesville Contact Information An Outlook into the Principle of Please visit www.intindstat.org/ Restricted Choice 15 update-contact to update your By Rahul Zalkikar, University of Virginia information. Member News 18 Obituary for Jayanta Kumar Ghosh (1937-2017) 19 By R. V. Ramamoorthi, Michigan State University Upcoming Conferences and Workshops 20 Memories from JSM 2017 22 Contribute to the News! Advertisements: Please submit items of interest Fordham University 23 to [email protected] Cytel 24 Department of Health and Human Services 25 1 Message from Sujit K. Ghosh, President of IISA Dear Members of IISA (Bandhugan!): Board of Trustees This is my last article as IISA president and I’d like to N. Balakrishnan, McMaster University express my sincere thanks to each one of you for Dipak K. Dey, University of Connecticut giving me an opportunity serve in my role as the Nandini Kannan, National Science Foundation president for 2017 and as president-elect last year. -
2008-2009Program Highlights
AIDS FOUNDATION OF CHICAGO 2008-2009 PROGRAM HIGHLIGHTS The AIDS FOUNDATION OF CHICAGO is a force for change. We pursue a broad range of activities to end new infections, improve the lives of people with HIV, and strengthen government and community responses to the epidemic. Founded in 1985 by community activists and physicians, we fortify local, national, and international HIV-related activities. We specialize in efforts to: • Advocate with and for people affected by HIV/AIDS. • Fund and coordinate prevention, care, and advocacy projects. • Cultivate the capacity and expertise of individuals and organizations dedicated to ending HIV/AIDS. Together with our individual, government, and community partners, we reach tens of thousands of people in need each year and make our collective efforts better and stronger. We are a source of hope—a force for change. The AIDS Foundation of Chicago dedicates this report to the tens of thousands of Illinoisans affected by HIV/AIDS and the many dedicated volunteers and professionals who, through their daily activities, champion compassion and action in the wake of the HIV/AIDS crisis. A Force for Change Table of Contents: A message from the President/CEO, Board Chair and SPC Chair 7 Leadership and advocacy 8 Prevention and care 10 Preparing for the future 12 AFC 2.0: Our online presence 14 Special events 16 Financial summary 18 Corporate and Foundation Supporters 19 The Year Ahead 20 2009 Calendar of Events 21 Individual Donors 22 Board of Directors and Staff 27 Service Providers Council Executive Committee & Junior Board 28 AFC’s Service Providers Council 29 Thank You 30 A Look Back 31 Message from President/CEO, Board Chair, and SPC Chair Dear Friends: For nearly 25 years, the AIDS Foundation of Chicago (AFC) has been a source of hope and a force for change, working with key partners to reduce HIV transmission and improve the lives of people with and at risk for HIV through advocacy, funding, and collaboration. -
Statistical Inference: Paradigms and Controversies in Historic Perspective
Jostein Lillestøl, NHH 2014 Statistical inference: Paradigms and controversies in historic perspective 1. Five paradigms We will cover the following five lines of thought: 1. Early Bayesian inference and its revival Inverse probability – Non-informative priors – “Objective” Bayes (1763), Laplace (1774), Jeffreys (1931), Bernardo (1975) 2. Fisherian inference Evidence oriented – Likelihood – Fisher information - Necessity Fisher (1921 and later) 3. Neyman- Pearson inference Action oriented – Frequentist/Sample space – Objective Neyman (1933, 1937), Pearson (1933), Wald (1939), Lehmann (1950 and later) 4. Neo - Bayesian inference Coherent decisions - Subjective/personal De Finetti (1937), Savage (1951), Lindley (1953) 5. Likelihood inference Evidence based – likelihood profiles – likelihood ratios Barnard (1949), Birnbaum (1962), Edwards (1972) Classical inference as it has been practiced since the 1950’s is really none of these in its pure form. It is more like a pragmatic mix of 2 and 3, in particular with respect to testing of significance, pretending to be both action and evidence oriented, which is hard to fulfill in a consistent manner. To keep our minds on track we do not single out this as a separate paradigm, but will discuss this at the end. A main concern through the history of statistical inference has been to establish a sound scientific framework for the analysis of sampled data. Concepts were initially often vague and disputed, but even after their clarification, various schools of thought have at times been in strong opposition to each other. When we try to describe the approaches here, we will use the notions of today. All five paradigms of statistical inference are based on modeling the observed data x given some parameter or “state of the world” , which essentially corresponds to stating the conditional distribution f(x|(or making some assumptions about it).