The Contrasting Strategies of Almroth Wright and Bradford Hill to Capture the Nomenclature of Controlled Trials
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School of Social Sciences Economics Division University of Southampton Southampton SO17 1BJ, UK Discussion Papers in Economics and Econometrics Mathematics in the Statistical Society 1883-1933 John Aldrich No. 0919 This paper is available on our website http://www.southampton.ac.uk/socsci/economics/research/papers ISSN 0966-4246 Mathematics in the Statistical Society 1883-1933* John Aldrich Economics Division School of Social Sciences University of Southampton Southampton SO17 1BJ UK e-mail: [email protected] Abstract This paper considers the place of mathematical methods based on probability in the work of the London (later Royal) Statistical Society in the half-century 1883-1933. The end-points are chosen because mathematical work started to appear regularly in 1883 and 1933 saw the formation of the Industrial and Agricultural Research Section– to promote these particular applications was to encourage mathematical methods. In the period three movements are distinguished, associated with major figures in the history of mathematical statistics–F. Y. Edgeworth, Karl Pearson and R. A. Fisher. The first two movements were based on the conviction that the use of mathematical methods could transform the way the Society did its traditional work in economic/social statistics while the third movement was associated with an enlargement in the scope of statistics. The study tries to synthesise research based on the Society’s archives with research on the wider history of statistics. Key names : Arthur Bowley, F. Y. Edgeworth, R. A. Fisher, Egon Pearson, Karl Pearson, Ernest Snow, John Wishart, G. Udny Yule. Keywords : History of Statistics, Royal Statistical Society, mathematical methods. -
Bayesian Inference for Heterogeneous Event Counts
Bayesian Inference for Heterogeneous Event Counts ANDREW D. MARTIN Washington University This article presents an integrated set of Bayesian tools one can use to model hetero- geneous event counts. While models for event count cross sections are now widely used, little has been written about how to model counts when contextual factors introduce heterogeneity. The author begins with a discussion of Bayesian cross-sectional count models and discusses an alternative model for counts with overdispersion. To illustrate the Bayesian framework, the author fits the model to the number of women’s rights cosponsorships for each member of the 83rd to 102nd House of Representatives. The model is generalized to allow for contextual heterogeneity. The hierarchical model allows one to explicitly model contextual factors and test alternative contextual explana- tions, even with a small number of contextual units. The author compares the estimates from this model with traditional approaches and discusses software one can use to easily implement these Bayesian models with little start-up cost. Keywords: event count; Markov chain Monte Carlo; hierarchical Bayes; multilevel models; BUGS INTRODUCTION Heterogeneity is what makes society and, for that matter, statistics interesting. However, until recently, little attention has been paidto modeling count data observed in heterogeneous clusters (a notable exception is Sampson, Raudenbush, and Earls 1997). Explanations AUTHOR’S NOTE: This research is supported by the National Science Foundation Law and Social Sciences and Methodology, Measurement, and Statistics Sections, Grant SES-0135855 to Washing- ton University. I would like to thank Dave Peterson, Kevin Quinn, and Kyle Saunders for their helpful comments. I am particularly indebted to Christina Wolbrecht for sharing her valuable data set with me. -
Statistics Making an Impact
John Pullinger J. R. Statist. Soc. A (2013) 176, Part 4, pp. 819–839 Statistics making an impact John Pullinger House of Commons Library, London, UK [The address of the President, delivered to The Royal Statistical Society on Wednesday, June 26th, 2013] Summary. Statistics provides a special kind of understanding that enables well-informed deci- sions. As citizens and consumers we are faced with an array of choices. Statistics can help us to choose well. Our statistical brains need to be nurtured: we can all learn and practise some simple rules of statistical thinking. To understand how statistics can play a bigger part in our lives today we can draw inspiration from the founders of the Royal Statistical Society. Although in today’s world the information landscape is confused, there is an opportunity for statistics that is there to be seized.This calls for us to celebrate the discipline of statistics, to show confidence in our profession, to use statistics in the public interest and to champion statistical education. The Royal Statistical Society has a vital role to play. Keywords: Chartered Statistician; Citizenship; Economic growth; Evidence; ‘getstats’; Justice; Open data; Public good; The state; Wise choices 1. Introduction Dictionaries trace the source of the word statistics from the Latin ‘status’, the state, to the Italian ‘statista’, one skilled in statecraft, and on to the German ‘Statistik’, the science dealing with data about the condition of a state or community. The Oxford English Dictionary brings ‘statistics’ into English in 1787. Florence Nightingale held that ‘the thoughts and purpose of the Deity are only to be discovered by the statistical study of natural phenomena:::the application of the results of such study [is] the religious duty of man’ (Pearson, 1924). -
School of Social Sciences Economics Division University of Southampton Southampton SO17 1BJ, UK
School of Social Sciences Economics Division University of Southampton Southampton SO17 1BJ, UK Discussion Papers in Economics and Econometrics Professor A L Bowley’s Theory of the Representative Method John Aldrich No. 0801 This paper is available on our website http://www.socsci.soton.ac.uk/economics/Research/Discussion_Papers ISSN 0966-4246 Key names: Arthur L. Bowley, F. Y. Edgeworth, , R. A. Fisher, Adolph Jensen, J. M. Keynes, Jerzy Neyman, Karl Pearson, G. U. Yule. Keywords: History of Statistics, Sampling theory, Bayesian inference. Professor A. L. Bowley’s Theory of the Representative Method * John Aldrich Economics Division School of Social Sciences University of Southampton Southampton SO17 1BJ UK e-mail: [email protected] Abstract Arthur. L. Bowley (1869-1957) first advocated the use of surveys–the “representative method”–in 1906 and started to conduct surveys of economic and social conditions in 1912. Bowley’s 1926 memorandum for the International Statistical Institute on the “Measurement of the precision attained in sampling” was the first large-scale theoretical treatment of sample surveys as he conducted them. This paper examines Bowley’s arguments in the context of the statistical inference theory of the time. The great influence on Bowley’s conception of statistical inference was F. Y. Edgeworth but by 1926 R. A. Fisher was on the scene and was attacking Bayesian methods and promoting a replacement of his own. Bowley defended his Bayesian method against Fisher and against Jerzy Neyman when the latter put forward his concept of a confidence interval and applied it to the representative method. * Based on a talk given at the Sample Surveys and Bayesian Statistics Conference, Southampton, August 2008. -
“It Took a Global Conflict”— the Second World War and Probability in British
Keynames: M. S. Bartlett, D.G. Kendall, stochastic processes, World War II Wordcount: 17,843 words “It took a global conflict”— the Second World War and Probability in British Mathematics John Aldrich Economics Department University of Southampton Southampton SO17 1BJ UK e-mail: [email protected] Abstract In the twentieth century probability became a “respectable” branch of mathematics. This paper describes how in Britain the transformation came after the Second World War and was due largely to David Kendall and Maurice Bartlett who met and worked together in the war and afterwards worked on stochastic processes. Their interests later diverged and, while Bartlett stayed in applied probability, Kendall took an increasingly pure line. March 2020 Probability played no part in a respectable mathematics course, and it took a global conflict to change both British mathematics and D. G. Kendall. Kingman “Obituary: David George Kendall” Introduction In the twentieth century probability is said to have become a “respectable” or “bona fide” branch of mathematics, the transformation occurring at different times in different countries.1 In Britain it came after the Second World War with research on stochastic processes by Maurice Stevenson Bartlett (1910-2002; FRS 1961) and David George Kendall (1918-2007; FRS 1964).2 They also contributed as teachers, especially Kendall who was the “effective beginning of the probability tradition in this country”—his pupils and his pupils’ pupils are “everywhere” reported Bingham (1996: 185). Bartlett and Kendall had full careers—extending beyond retirement in 1975 and ‘85— but I concentrate on the years of setting-up, 1940-55. -
Major Greenwood and Clinical Trials
From the James Lind Library Journal of the Royal Society of Medicine; 2017, Vol. 110(11) 452–457 DOI: 10.1177/0141076817736028 Major Greenwood and clinical trials Vern Farewell1 and Tony Johnson2 1MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK 2MRC Clinical Trials Unit, Aviation House, London WC2B 6NH, UK Corresponding author: Vern Farewell. Email: [email protected] Introduction References to Greenwood’s work in clinical trials Major Greenwood (1880–1949) was the foremost medical statistician in the UK during the first Greenwood was a renowned epidemiologist who is not half of the 20th century.1 The son of a general usually associated with randomised clinical trials. At medical practitioner, he obtained a medical degree the time of his retirement, randomised clinical trials from the London Hospital in 1904 and then stu- were still under development and Peter Armitage has died statistics under Karl Pearson at University no recollection of Greenwood commenting on trials College London. Instead of general practice, he when Greenwood visited the London School of opted to pursue a career in medical research, first Hygiene and Tropical Medicine during his retirement under the physiologist Leonard Hill at the London (personal communication). Indeed, randomised clin- Hospital, where, in 1908, he set up the first depart- ical trials were a major component of the work of ment of medical statistics and gave the first lecture Austin Bradford Hill,5 who began his employment in course in the subject in 1909. He established the Greenwood’s department in 1923 and succeeded him second department of medical statistics in 1910 at as professor at the London School of Hygiene and the Lister Institute and established the second Tropical Medicine and director of the Medical course of lectures there. -
“I Didn't Want to Be a Statistician”
“I didn’t want to be a statistician” Making mathematical statisticians in the Second World War John Aldrich University of Southampton Seminar Durham January 2018 1 The individual before the event “I was interested in mathematics. I wanted to be either an analyst or possibly a mathematical physicist—I didn't want to be a statistician.” David Cox Interview 1994 A generation after the event “There was a large increase in the number of people who knew that statistics was an interesting subject. They had been given an excellent training free of charge.” George Barnard & Robin Plackett (1985) Statistics in the United Kingdom,1939-45 Cox, Barnard and Plackett were among the people who became mathematical statisticians 2 The people, born around 1920 and with a ‘name’ by the 60s : the 20/60s Robin Plackett was typical Born in 1920 Cambridge mathematics undergraduate 1940 Off the conveyor belt from Cambridge mathematics to statistics war-work at SR17 1942 Lecturer in Statistics at Liverpool in 1946 Professor of Statistics King’s College, Durham 1962 3 Some 20/60s (in 1968) 4 “It is interesting to note that a number of these men now hold statistical chairs in this country”* Egon Pearson on SR17 in 1973 In 1939 he was the UK’s only professor of statistics * Including Dennis Lindley Aberystwyth 1960 Peter Armitage School of Hygiene 1961 Robin Plackett Durham/Newcastle 1962 H. J. Godwin Royal Holloway 1968 Maurice Walker Sheffield 1972 5 SR 17 women in statistical chairs? None Few women in SR17: small skills pool—in 30s Cambridge graduated 5 times more men than women Post-war careers—not in statistics or universities Christine Stockman (1923-2015) Maths at Cambridge. -
Alan Agresti
Historical Highlights in the Development of Categorical Data Analysis Alan Agresti Department of Statistics, University of Florida UF Winter Workshop 2010 CDA History – p. 1/39 Karl Pearson (1857-1936) CDA History – p. 2/39 Karl Pearson (1900) Philos. Mag. Introduces chi-squared statistic (observed − expected)2 X2 = X expected df = no. categories − 1 • testing values for multinomial probabilities (Monte Carlo roulette runs) • testing fit of Pearson curves • testing statistical independence in r × c contingency table (df = rc − 1) CDA History – p. 3/39 Karl Pearson (1904) Advocates measuring association in contingency tables by approximating the correlation for an assumed underlying continuous distribution • tetrachoric correlation (2 × 2, assuming bivariate normality) X2 2 • contingency coefficient 2 based on for testing q X +n X independence in r × c contingency table • introduces term “contingency” as a “measure of the total deviation of the classification from independent probability.” CDA History – p. 4/39 George Udny Yule (1871-1951) (1900) Philos. Trans. Royal Soc. London (1912) JRSS Advocates measuring association using odds ratio n11 n12 n21 n22 n n n n − n n θˆ = 11 22 Q = 11 22 12 21 = (θˆ − 1)/(θˆ + 1) n12n21 n11n22 + n12n21 “At best the normal coefficient can only be said to give us. a hypothetical correlation between supposititious variables. The introduction of needless and unverifiable hypotheses does not appear to me a desirable proceeding in scientific work.” (1911) An Introduction to the Theory of Statistics (14 editions) CDA History – p. 5/39 K. Pearson, with D. Heron (1913) Biometrika “Unthinking praise has been bestowed on a textbook which can only lead statistical students hopelessly astray.” . -
The Development of the MRC Statistical Unit, 1911-1948
Medical History, 2000, 44: 323-340 Medical Statistics, Patronage and the State: The Development of the MRC Statistical Unit, 1911-1948 EDWARD HIGGS* The development of medical statistics based on the concepts of probability and error-theory developed by Francis Galton, Karl Pearson, George Udny Yule, R A Fisher, and their colleagues, has had a profound impact on medical science in the present century. This has been associated especially with the activities of the Medical Research Council (MRC) Statistical Unit at the London School of Hygiene and Tropical Medicine (LSHTM). It was here in 1946 that the world's first statistically rigorous clinical trial was undertaken, and where in the 1950s Austin Bradford Hill and Richard Doll revealed a statistical relationship between smoking and lung- cancer. But how and why was this key institution founded, and why were its methods probabilistic?' One might assume that the sheer brilliance of the work produced by Pearson and his statistical followers was sufficient to win over the medical profession, and to attract funding for the MRC Unit. However, one might question the extent to which an association with Pearson, and with the mathematical technicalities of his statistics, immediately endeared the fledgling discipline of biostatistics to the medical com- munity. As J Rosser Matthews has shown recently, Pearsonian statistics were only haltingly accepted by medical scientists in Britain in the 1920s and 1930s, and were little understood by medical practitioners.2 How then was such a university-based infrastructure for medical statistics established? Ultimately, of course, the Statistical * Edward Higgs, DPhil, Department of History, the statistical work on smoking, see V Berridge, University of Essex, Wivenhoe Park, Colchester, 'Science and policy: the case of postwar British C04 3SQ. -
Spatial Analysis, GIS, and Remote Sensing Applications in the Health Sciences
Spatial Analysis, GIS, and Remote Sensing Applications in the Health Sciences Editors Donald P.Albert Wilbert M.Gesler Barbara Levergood Ann Arbor Press Chelsea, Michigan This edition published in the Taylor & Francis e-Library, 2005. “To purchase your own copy of this or any of Taylor & Francis or Routledge’s collection of thousands of eBooks please go to www.eBookstore.tandf.co.uk.” Library of Congress Cataloging-in-Publication Data Spatial analysis, GIS and remote sensing: applications in the health sciences/ edited by Donald P.Albert, Wilbert M.Gesler, Barbara Levergood. p. cm. Includes bibliographical references and index. ISBN 1-57504-101-4 (Print Edition) 1. Medical geography. 2. Medical geography–Research–Methodology. I. Albert, Donald Patrick. II. Gesler, Wilbert M., 1941— . III. Levergood, Barbara. RA792 .S677 2000 614.4′2—dc21 99—089917 ISBN 0-203-30524-8 Master e-book ISBN ISBN 0-203-34374-3 (Adobe eReader Format) ISBN 1-57504-101-4 (Print Edition) © 2000 by Sleeping Bear Press Ann Arbor Press is an imprint of Sleeping Bear Press This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission, and sources are indicated. A wide vari ety of references are listed. Reasonable efforts have been made to publish reliable data and information, but the author and the publisher cannot assume responsibility for the validity of all materials or for the consequences of their use. Neither this book nor any part may be reproduced or transmitted in any form by any means, electronic or mechanical, including photocopying, microfilming, and record ing, or by any information storage or retrieval system, without prior permission in writing from the publisher. -
NATURE September 29, 1951 Vol. 16E
542 NATURE September 29, 1951 voL. 16e actually completed this year four months ahead of Fawley now assumes priority as the largest oil schedule. The scheme involved clearing and levelling refinery in Europe. It cost £37,500,000 to build. 450 acres of wooded gravel-land; construction of When in full operation it will produce 6,500,000 tons roads ; laying down a railway system to connect up of petroleum products a year ; more than a million with permanent way to Southampton ; erecting a gallons of motor spirit a day ; and nearly 30 per huge building, 800 ft. long, 180 ft. wide and occupying cent of present total demand for petroleum and more than 3 acres, first to house constructional products in the British Isles. Not least in these materials, ultimately to serve as maintenance and financially stringent times, this brilliantly planned machine shop; lay-out of a construction camp on and executed work will. effect a saving in foreign site for 750 workmen; erection of a huge concrete exchange of more than 2 million dollars a week. plant with output of 140 cubic yards per hour, one Someone concerned with the lavish series · of of the largest in the world ; installation of the main publications which a great occasion like this official .refinery units, in itself a colossal task of erecting and opening obviously presented had the foresight and linking-up the bewildering maze of pipes, tanks, sensitive imagination to commission H. E. Bates to stills, furnaces and huge towers that go to make up write a commemoration "Fawley Achievement", "as a modern oil refinery. -
The Economist
Statisticians in World War II: They also served | The Economist http://www.economist.com/news/christmas-specials/21636589-how-statis... More from The Economist My Subscription Subscribe Log in or register World politics Business & finance Economics Science & technology Culture Blogs Debate Multimedia Print edition Statisticians in World War II Comment (3) Timekeeper reading list They also served E-mail Reprints & permissions Print How statisticians changed the war, and the war changed statistics Dec 20th 2014 | From the print edition Like 3.6k Tweet 337 Advertisement “I BECAME a statistician because I was put in prison,” says Claus Moser. Aged 92, he can look back on a distinguished career in academia and civil service: he was head of the British government statistical service in 1967-78 and made a life peer in 2001. But Follow The Economist statistics had never been his plan. He had dreamed of being a pianist and when he realised that was unrealistic, accepted his father’s advice that he should study commerce and manage hotels. (“He thought I would like the music in the lobby.”) War threw these plans into disarray. In 1936, aged 13, he fled Germany with his family; four years later the prime minister, Winston Churchill, decided that because some of the refugees might be spies, he would “collar the lot”. Lord Moser was interned in Huyton, Latest updates » near Liverpool (pictured above). “If you lock up 5,000 Jews we will find something to do,” The Economist explains : Where Islamic he says now. Someone set up a café; there were lectures and concerts—and a statistical State gets its money office run by a mathematician named Landau.