Encyclopaedic Companion to Medical Statistics E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 1834

Veröffentlichungsjahr: 2011

Beschreibung

Statistical methodology is of great importance to medical research and clinical practice. The Encyclopaedic Companion to Medical Statistics contains readable accounts of the key topics central to current research and practice. Each entry has been written by an individual chosen for both their expertise in the field and their ability to communicate statistical concepts successfully to medical researchers. Real examples from the biomedical literature and relevant illustrations feature in many entries and extensive cross-referencing signposts the reader to related entries. Key Features: * Contains accounts of over 400 statistical topics central to current medical research. * 80% of first edition entries updated and revised. * Presents the latest techniques used at the cutting edge of medical research. * Covers common errors in statistical analyses in medicine. * Real examples from the biomedical literature and relevant illustrations feature throughout. * Contains contributions from over 70 experts in the field. Medical researchers, researchers and practitioners in medical research and statistics will benefit greatly from this book.

Leseprobe

Contents

Foreword

Preface to the Second Edition

Preface

Biographical Information on the Editors

List of Contributors

Abbreviations and Acronyms

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This edition first published 2011© 2011 John Wiley & Sons, Ltd

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Library of Congress Cataloging-in-Publication Data

The encyclopaedic companion to medical statistics/edited by Brian S. Everitt and Christopher R. Palmer; with a foreword by Richard Horton. – 2nd ed.p.; cm.Includes bibliographical references.Summary: “The Encyclopaedic Companion to Medical Statistics, contains readable accounts of almost 400 statistical topics central to current medical research. Each entry has been written by an individual chosen for both their expertise in the field and their ability to communicate statistical concepts successfully to medical researchers. Real examples from the biomedical literature and relevant illustrations feature in many entries, and extensive cross–referencing signposts the reader to related entries”–Provided by publisher.ISBN 978-0-470-68419-1 (h/b)

1. Medical statistics–Encyclopedias. I. Everitt, Brian. II. Palmer,Christopher Ralph.[DNLM: 1. Statistics as Topic–methods–Encyclopedias–English. 2. Models,Theoretical–Encyclopedias–English. WA 13 E562 2010]RA409.E527 2010610.7203–dc222010018741

A catalogue record for this book is available from the British Library.Print ISBN: 978-0-470-68419-1ePDF ISBN: 978-0-470-66974-7

To Mary-ElizabethBrian S. Everitt

To Cathy-Joan, Laura, Carolyn and DavidChristopher R. Palmer

Foreword

This encyclopaedia contains no entry for ‘Peer review’. In my small corner of the medical statistical universe, this seems like a gross sin of omission. Instead, the process of evaluating research papers is discussed under ‘Critical appraisal’. Are these two procedures synonymous? And, irrespective of whether they are or are not, should anybody care?

I believe that peer review and critical appraisal do differ, that these differences matter a great deal when considering the ways in which readers should interpret the medical literature, and that an understanding of these differences helps to place medical statistics in its proper context when surveying the wide horizon of clinical and public health research.

The editors of this quite wonderfully rewarding treatise on statistical terms have defined critical appraisal as ‘the process of evaluating research reports and assessing their contribution to scientific knowledge’. This statement follows naturally from the meaning of the words ‘criticism’ (the art of judging) and ‘appraisal’ (the estimation of quality). That is to say, critical appraisal is an estimation of worth followed by some kind of judgment – a judgment that leans more towards an art than a science. As a non-statistician, I rather warm to the precise imprecision of this definition.

Now consider the more commonly embedded term ‘peer review’ and look how inferior it is! Who is this anonymous idealised peer? Generally, one would consider a peer to be an equal, somebody who comes from a group comparable to that from which the person under scrutiny has emerged. This intellectual egalitarian is subsequently set the task of viewing again (to take ‘review’ at its most literal meaning) the work under consideration. But to view with what purpose? None is specified.

Despite these practical shortcomings, editors of biomedical journals remain wedded to ‘peer review’. We feel uncomfortable with the notion of critical appraisal. The embodiment of peer review as a distinct scientific discipline is the series of international congresses devoted to peer review in biomedical publication, organised jointly by JAMA and the BMJ. These congresses have spawned hundreds of abstracts, dozens of research papers, and four theme issues of JAMA. They are entirely commendable in every way. For the editors of JAMA and the BMJ, peer review encompasses a broad range of activities: mechanisms of editorial decision making, together with their quality, validity, and practicality, online peer review and publication, pre-publication posting of information, quality assurance of reviewers and editors, authorship and contributorship, conflicts of interest, scientific misconduct, peer review of grant proposals, economic aspects of peer review, and the future of scientific publication.

In other words, peer review is a tremendously elastic concept, allowing editors to stretch it to mean whatever interests them at a given (whimsical) moment in time and place. Indeed, its elasticity is seen by many of us as its great strength. The concept grows in richness and understanding as our own appreciation of its complexity and nuance soars. The impenetrable nature of peer review, and the obscure and hard to-learn expertise it demands, feeds our brittle egos. The notion of critical appraisal, by contrast, is far thinner in meaning, with much less room for editorial manipulation and aggrandisement.

Even if peer review and critical appraisal do differ, should anyone actually care? Yes, they should, and for a very simple reason: the idea of peer review is now bankrupt. Its retention as an operation within the biomedical sciences reflects the interests of those who wish to preserve their own power and position. Peer review is fundamentally anti-democratic. It elevates the mediocre. It asphyxiates originality and it kills careers. How so?

Peer review is not about intelligent engagement with a piece of research. It is about defining the margins of what is acceptable and unacceptable to the reviewer. The mythical ‘peer’ is being asked to view again, after the editor, the work in question and to offer a comment about the geographical location of that work on the map of existing knowledge. If there is space on this map, and provided the work does not disrupt (too much) the terrain established by others, its location can be secured and marked by sanctioning publication. If the disruption is too great, the work’s wish to seek a place of rest must be vetoed. Peer reviewis about the agency of power to preserve established orthodoxy. It has nothing to do with science. It has everything to do with ideology – and the maintenance of a quiet life of privilege and mystique.

Instead, critical appraisal is about incrementally working one’s way towards truth1. It can never be about truth itself. The essence of biomedical research is estimation. Our world resists certainty. Critical appraisal is about transparent, measurable analysis that cuts a path towards greater precision.

Critical appraisal refuses to veil itself in the gaudy adornments that editors pin to peer review in order to embellish their own importance in the cartography of scientific inquiry. A far more robust instrument critical appraisal is for that refusal.

What do these differences tell us about the proper place of medical statistics in biomedicine today? In my view, as a lapsed doctor and a now wrinkled editor, medical statistics is the most important aspect of our critical appraisal of any piece of new research. The evaluations by so-called peers in the clinical specialties that concern a particular research paper provide valuable insight in to how that work will be received by a community of practitionersor scholars. However, as an editor I am less interested in reception than I am in meaning2.

I want a tough interrogation of new work before its publication, according to commonly agreed standards of questioning – standards that I can see and evaluate for myself. To return to my personal definition of critical appraisal, I want an estimation of quality combined with a judgment. I do not want a view from the club culture of one particular academic discipline. The rejection of peer review by the editors of this encyclopaedia is therefore a triumph of liberty against the forces of conformity.

Yet still today, too much of medicine takes medical statistics for granted. Time and again, we see research that has clearly not been within a hundred miles of a statistical brain. Physicians usually make poor scientists, and physicians and scientists together too often play the part of amateur statistician – with appalling consequences. The future of a successful biomedical research enterprise depends on the flourishing of the discipline we call medical statistics. It is not at all clear to me that those who so depend on medical statistics appreciate either that dependence or the fragility of its foundation.

If this magnificent encyclopaedia can be deployed in the ongoing argument about the future of twenty-first century academic medicine, then not only the research enterprise but also the public’s health and well-being will be far stronger tomorrow than it is today.

Richard HortonEditor, Lancet

1. Horton, R. 2002: Postpublication criticism and the shaping of clinical knowledge. JAMA 287, 2843–7.

2. Horton, R. 2000: Common sense and figures: the rhetoric of validity in medicine. Satist Med 19, 3149–64.

Preface to the Second Edition

In this second edition of the Encyclopaedic Companion to Medical Statistics there are over30 completely new entries and the majority of entries from the first edition from 2005 have been revised and updated. The aim of this new edition remains the same as before and that is, quintessentially, to aid communication between medical researchers and statisticians.

We hope this aim has been met by providing fully cross-referenced articles encompassing a wide range of statistical topics likely to be encountered in today’s medical literature, of suitable breadth and depth to give sufficient detail, but not to overwhelm with too much technical background and to provide helpful further references where needed for those wishing to explore topics more deeply. We believe a key strength of this single-volume reference work also remains from the first edition, namely the accessibility of the articles. Their readability is enhanced because contributors are not only experts in their respective fields but also adept at communicating statistical concepts to non-statisticians, including those who may admit to having a certain fear of handling data and not knowing how to deal with all the numbers arising from their medical research!

Another aspect of the aim to enhance communication between medical and statistical disciplines concerns encouraging timeliness of seeking statistical advice. It is our hope too that this Encyclopaedic Companion will serve to encourage medical researchers to consult with statisticians at the earliest opportunity within the life cycle of a research project. Relevant entries herein might be read before, and reviewed after, such consultations to enable clearer understanding and, ultimately, help facilitate better quality medical research.

Once again our thanks are due to a large number of people: the contributors for their sterling efforts in producing such excellent entries, the team at Wiley, in particular Richard Davies and Heather Kay and, of course, to our families.

Brian S. Everitt, Dulwich, LondonChristopher R. Palmer, Cambridge, UK

Preface

Statistical science plays an important role in medical research. Indeed a major part of the key to the progress in medicine from the 17th century to the present day has been the collection and valid interpretation of evidence, particularly quantitative evidence, provided by the application of statistical methods to medical investigations. Current medical journals are full of statistical material, both relatively simple (for example, t-tests, p-values, linear regression) and, increasingly, more complex (for example, generalised estimating equations, cluster analysis, Bayesian methods). The latter material reflects the vibrant state of statistical research with many new methods having practical implications for medicine being developed in the last two decades or so. But why is statistics important in medicine? Some possible answers are:

(1) Medical practice and medical research generate large amounts of data. Such data are generally full of uncertainty and variation, and extracting the ‘signal’ from the ‘noise’ is usually not trivial.

(2) Medicine involves asking questions that have strong statistical overtones. How common is the disease? Who is especially likely to contract a particular condition? What are the chances that a patient diagnosed with breast cancer will survive more than five years?

(3) The evaluation of competing treatments or preventative measures relies heavily on statistical concepts in both the design and analysis phase.

Recognition of the importance of statistics in medicine has increased considerably in recent years. The last decade, in particular, has seen the emergence of evidence-based medicine, and with it the need for clinicians to keep one step ahead of their patients, many of whom nowadays have access to virtually unlimited information (much of it being virtual, yet some of it being limited in its reliability). Compared with previous generations of medical students, today’s pre-clinical undergraduates are being taught more about statistical principles than their predecessors. Furthermore, today’s clinical researchers are faced (happily, in our view) with growing numbers of biomedical journals utilising statistical referees as part of their peer review processes (see CRITICAL APPRAISAL and STATISTICAL REFEREEING). This enhances the quality of the papers journal editors select, although from the clinical researcher’s perspective it has made publication in leading journals more challenging than ever before.

So statistics is (and are) prevalent in the medical world now and is set to remain so for the future. Clearly, clinicians and medical researchers need to know something about the subject, even if only to make their discussion with a friendly statistician more fruitful. The article on consulting a statistician quotes one of the forefathers of modern statistics, R.A. Fisher who, back in 1938, observed wryly: ‘To consult the statistician after an experiment is finished is often merely to ask him to conduct a post-mortem examination. He can perhaps say what the experiment died of.’ Thus, one of our hopes for the usefulness and helpfulness of the Encyclopaedic Companion to Medical Statistics is that it may serve to encourage both productive and timely interactions between medical researchers and statisticians. Another sincere hope is that it fills a gap between, on the one hand, textbooks that delve into possibly too much theory and, on the other hand, shorter dictionaries that may not necessarily focus on the needs of medical researchers, or else have entries that are tantalisingly succinct. To meet these ends, the present reference work contains concise, informative, relatively nontechnical, and hence, we trust, readable accounts of over 350 topics central to modern medical statistics.

Topics are covered either briefly or more extensively, in general, in accordance with the subject matter’s perceived importance, although we acknowledge there will be disagreement, inevitably, about our choice of article lengths. Many entries benefit from containing real-life, clinical examples. Each has been written by an individual chosen not only for subject-matter expertise in the field but, just as importantly, also by ability to communicate statistical concepts to others.

The extensive cross-referencing supplied using SMALL CAPITALS to indicate terms that appear as separate entries should help the reader to find his or her way around and also serves to point out associated topics that might be of interest elsewhere within the Encyclopaedic Companion. All but the shortest entries contain references to further resources where the interested reader can learn in greater depth about the particular topic.

Thus, while hoping this work is found to be mostly comprehensible we do not claim it to be fully comprehensive. As co-editors we take joint responsibility for any errors (‘sins of commission’) and would positively welcome suggestions for possible new topics to consider for future inclusion to rectify perceived missing entries (‘sins of omission’).

Our thanks are due to numerous people – first, to all of the many contributors for providing such excellent material, mostly on time (mostly!) with particular gratitude extended to those who contributed multiple articles or who handled requests for additional articles so gracefully. Next, we appreciated the tremendous and indispensable efforts of staff at Arnold, especially Liz Gooster and Liz Wilson, and not least for their remaining calm during an editor’s moments of anxiety and neurosis about the entire project. In addition we would like to thank Harriet Meteyard for her constant support and encouragement throughout the preparation of this book. Finally, our family members deserve especial thanks for having been extra tolerant of our time spent on developing and executing this extensive project from beginning to end. It is our hope that the Encyclopaedic Companion proves all these efforts and sacrifices to be well worthwhile, becoming a useful, regularly-thumbed reference added to the bookshelf of many of those involved in contemplating, conducting or contributing to medical research.

Brian S. Everitt and Christopher R. PalmerJanuary 2005

Biographical Information on the Editors

Brian S. Everitt – Professor Emeritus, King’s College London. After 35 years at the Institute of Psychiatry, University of London, Brian Everitt retired in May 2004. Author of approximately 100 journal articles and over 50 books on statistics, and also co-editor of Statistical Methods in Medical Research. Writing continues apace in retirement but now punctuated by tennis, walks in the country, guitar playing and visits to the gym, rather than by committees, committees and more committees.

Christopher R. Palmer, founding Director of Cambridge University’s Centre for Applied Medical Statistics, regularly teaches and collaborates with current and future doctors. His first degree was from Oxford, while graduate and postdoctoral studies were in the USA (at UNC-Chapel Hill and Harvard). He has shifted from mathematical towards applied statistics, with particular interest in the ethics of clinical trials and the use of flexible designs whenever appropriate. Fundamentally, he likes to promote sound statistical thinking in all areas of medical research and hopes this volume might help towards that end. Chris served as Deputy or Acting Editor for Statistics in Medicine, 1996–2000, and is a long-standing statistical reviewer for The Lancet. He and his wife have three children they consider to be more than statistically significant.

List of Contributors

K. R. Abrams (KRA), Centre for Biostatistics and Genetic, Epidemiology, Department of Health Sciences, University of Leicester, Leicester, LE1 7RH, UK

Colin Baigent (CB), Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Richard Doll Building, Old Road Campus, Roosevelt Drive, Oxford OX3 7LF, UK

Alun Bedding (AB), Quantitative Sciences, GlaxoSmith-Kline, Medicines Research Centre, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, UK

Tijl De Bie (TDB), ISIS Research Group, Building 1, University of Southampton, Southampton SO17 1BJ, UK

Kathe Bjork (KB), Primetrics, Inc. Arvada, Colorado, USA ([email protected])

J. Martin Bland (JMB), Professor of Health Statistics, Department of Health Sciences, University of York, Heslington, York YO10 5DD, UK

Matteo Bottai (MtB), Division of Biostatics, Arnold School of Public Health, University of South Carolina, 800 Sumter Street, Columbia, SC 29208, USA, and also Unit of Biostatistics, Institute of Environmental Medicine, Karolinska Institutet Nobels v€ag 13, Stockholm, Sweden

Michelle Bradley (MMB), Health Information and Quality Authority, George’s Court, George’s lane, Dublin 7, Ireland

Sara Brookes (SB), Department of Social Medicine, University of Bristol, Canynge Hall, Whiteladies Road, Clifton, Bristol BS8 2PR, UK

Marc Buyse (MB), International Drug Development Institute (IDDI), 30 avenue provinciale, 1340 Louvain-la-Neuve, Belgium

M.J. Campbell (MJC), School of Health and Related Research, University of Sheffield, Regent Court, 30 Regent Street, Sheffield S1 4DA, UK

James R. Carpenter (JRC), Medical Statistics Unit, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK

Lucy M. Carpenter (LMC), Department of Public Health, University of Oxford, and Nuffield College, Oxford OX1 1NF, UK

Susan Chinn (SC), Respiratory Epidemiology and Public Health, Imperial College, Emmanuel Kaye Building, Manresa Road, London SW3 6LR, UK

Tim Cole (TJC), MRC Centre of Epidemiology for Child Health, UCL Institute of Child Health, 30 Guilford Street, London WC1N 1EH, UK

Chris Corcoran (CCo), Department of Mathematics and Statistics, Utah State University, Logan, UT 84322-3900, USA

Nello Cristianini (NC), UC Davis Department of Statistics, 360Kerr Hall, One ShieldsAvenue, Davis, CA 95616, USA

Sarah Crozier (SRC), MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton General Hospital, Southampton SO16 6YD, UK

Carole Cummins (CLC), The University of Birmingham, Department of Public Health, Epidemiology and Biotatistics, 90 Vincent Drive, Edgbaston, Birmingham, B15 2TH

George Davey-Smith (GDS), School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK

Simon Day (SD), Roche Products Limited,Welwyn Garden City, Hertfordshire. AL7 1TW, UK.

Daniela De Angelis (DDA), Statistics, Modelling and Economics Department, Health Protection Agency, Centre for Infections, London and MRC Biostatistics Unit, Institute of Public Health, University Forvie Site, Robinson Way, Cambridge CB2 0SR, UK

Jonathan Deeks (JD), Public Health, Epidemiology and Biostatistics, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK

Graham Dunn (GD), Health Sciences Research Group, School of Community Based Medicine, University of Manchester, Jean McFarlane Building, Oxford Road, Manchester M13 9PL, UK

Doug Easton (DE), Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Worts Causeway, Cambridge CB1 8RN, UK

Jonathan Emberson (JE), Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Richard Doll Building, Old Road Campus, Roosevelt Drive, OxfordOX3 7LF, UK

Richard Emsley (RE), Health Sciences Research Group, School of Community Based Medicine, University of Manchester, Jean McFarlane Building, Oxford Road, Manchester M13 9PL, UK

Brian S. Everitt (BSE), Biostatistics Department, Institute of Psychiatry, Denmark Hill, London SE5 8AF, UK

David Faraggi (DF), Department of Statistics, University of Haifa, Haifa 31905, Israel

W. Harper Gilmour (WHG), Section for Public Health and Health Policy, Division of Community Based Sciences, University of Glasgow, Glasgow G12 8RZ, UK

Els Goetghebeur (EG), Department of Applied Mathematics and Statistics, Ghent University, Krijgslaan 281-S9, 9000 Ghent, Belgium

Andrew Grieve (AG), Division of Health & Social Care Research, Department of Primary Care and Public Health Sciences, School of Medicine, King’s College, Floor 7, Capital House, 42 Weston St, London SE1 3QD, UK

Julian P. T. Higgins (JPTH), MRC Biostatistics Unit, Institute of Public Health, University Forvie Site, Robinson Way, Cambridge CB2 0SR, UK

Theodore R. Holford (TRH), Division of Biostatistics, Yale School of Public Health, Yale, New Haven, CT 06520, USA

Sally Hollis (SH), AstraZeneca, Parklands, Alderley Park, Macclesfield, Cheshire SK10 4TF, UK

Torsten Hothorn (TH), Institut für Statistik, Ludwig Maximilians - Universität München, Ludwigstrasse 33, DE-80539 München, Germany

Hazel Inskip (HI), MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton General Hospital, Southampton SO16 6YD, UK

Tony Johnson (TJ), MRC Biostatistics Unit, Institute of Public Health, University Forvie Site, Robinson Way, Cambridge CB2 0SR, UK& MRC Clinical Trials Unit, 222 Euston Road, London, NW1 2DA

Karen Kafadar (KKa), Department of Mathematics, University of Colorado at Denver, PO Box 173364, Campus Box 170, Denver, CO 80217-3364, USA

Kyungmann Kim (KK), Department of Biostatistics and Medical Informatics, University of Wisconsin Medical School, 600 Highland Ave., Madison, WI 53792-4675, USA

Ruth King (RK), School of Mathematics and Statistics, Mathematical Institute, University of St Andrews, Fife KY16 9SS, UK

Wojtek Krzanowski (WK), School of Engineering, Mathematics and Physical Science, University of Exeter, Harrison Building, North Park Road, Exeter EX4 4QF, UK

Ranjit Lall (RL), Warwick Emergency Care and Rehabilitation, Division of Health in the Community, Warwick Medical School, University of Warwick, The Farmhouse, Gibbet Hill Campus, Coventry CV4 7AL, UK

Sabine Landau (SL), Biostatistics Department, Institute of Psychiatry, King’s College, Denmark Hill, London SE5 8AF, UK

Andrew B. Lawson (AL), Division of Biostatistics and Epidemiology, College of Medicine, Medical University of South Carolina, Charleston, SC 29425, USA

Morven Leese (ML), Health Service and Population Research Department, Institute of Psychiatry, King’s College, Denmark Hill, London SE5 8AF, UK

Andy Lynch (AGL), Department of Oncology, University of Cambridge, LiKa Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK

Cyrus Mehta (CM), President, Cytel Software Corporation, 675 Massachusetts Avenue, Cambridge, MA 02139, USA

Richard Morris (RM), Department of Primary Care and Population Health, UCL Medical School, Royal Free Campus, London NW3 2PF, UK

Paul Murrell (PM), Department of Statistics, The University of Auckland, Private Bag 92019, Auckland, New Zealand

Christopher R. Palmer (CRP), Department of Public Health and Primary Care, Institute of Public Health, University Forvie Site, RobinsonWay, Cambridge CB2 0SR, UK

Max Parmar (MP), MRC Clinical Trials Unit, 222 Euston Road, London NW1 2DA, UK

Nitin Patel (NP), Cytel Software Corporation, 675 Massachusetts Avenue, Cambridge, MA 02139-3309, USA

John Powles (JP), Department of Public Health and Primary Care, Institute of Public Health, University Forvie Site, Robinson Way, Cambridge CB2 0SR, UK

P. Prescott (PP), Faculty of Mathematical Studies, University of Southampton, Southampton SO17 1BJ, UK

Sophia Rabe-Hesketh (SRH), Graduate School of Education and Graduate Group in Biostatistics, University of California, Berkeley, 3659 Tolman Hall, California 94720, USA and Institute of Education, University of London

Ben Reiser (BR), Department of Statistics, University of Haifa, Haifa 31905, Israel

Shaun Seaman (SRS), MRC Biostatistics Unit, Institute of Public Health, University Forvie Site, Robinson Way, Cambridge CB2 0SR, UK

Mark Segal (MRS), Division of Biostatistics, University of California, 185 Berry Street, Suite 5700, San Francisco, CA 94107, USA

Pralay Senchaudhuri (PSe), Cytel Software Corporation, 675 Massachusetts Avenue, Cambridge, MA 02139-3309, USA

Stephen Senn (SS), Department of Statistics, The University of Glasgow, Glasgow G12 8QQ, UK

Pak Sham (PS), Department of Psychiatry, The University of Hong Kong, Queen Mary Hospital, 102 Pokfulam Rd, Hong Kong

Charlie Sharp (CS), Computing Department, Institute of Psychiatry, Denmark Hill, London SE5 8AF, UK

Arvid Sjolander (AJS), Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels Väg 12A, 171 77 Stockholm, Sweden

Anders Skrondal (AS), Division of Epidemiology, Norwegian Institute of Public Health, PO Box 4404 Nydalen, N-0403, Oslo, Norway

Nigel Smeeton (NCS), King’s College London, Department of Primary Care and Public Health Sciences, Division of Health and Social Care Research, 7th Floor Capital House, 42 Weston Street, London SE1 3QD, UK

Nigel Stallard (NS), Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK

Jonathan Sterne (JS), School of Social and Community Medicine, University of Bristol, Canynge Hall, 39 Whatley Road, Bristol BS8 2PS, UK

Elisabeth Svensson (ES), Swedish Business School/ Statistics, Örebro University, Örebro, Sweden

Matthew Sydes (MS), MRC Clinical Trials Unit, 222 Euston Road, London NW1 2DA, UK

Jeremy Taylor (JMGT), Department of Biostatistics, University of Michigan, 1420 Washington Heights, Ann Arbor, MI 48109-2029, USA

Kate Tilling (KT), School of Social and Community Medicine, University of Bristol, Canynge Hall, 39 Whatley Road, Bristol BS8 2PS, UK

Brian Tom (BT), MRC Biostatistics Unit, Institute of Public Health, University Forvie Site, Robinson Way, Cambridge CB2 0SR, UK

Rebecca Turner (RT), MRC Biostatistics Unit, Institute of Public Health, University Forvie Site, Robinson Way, Cambridge CB2 0SR, UK

Andy Vail (AV), Biostatistics Group, University of Manchester, Oxford Road, Manchester M13 9PL, UK

Stijn Vansteelandt (SV), Ghent University, Dept. of Applied Mathematics and Computer Science, Krijgslaan 281, S9, B-9000 Ghent, Belgium

Sarah L. Vowler (SLV), Bioinformatics Core, Cancer Research UK, Cambridge Research Institute, RobinsonWay, Cambridge CB2 0RE, UK

Stephen J. Walters (SJW), Medical Statistics Group, School of Health and Related Research, University of Sheffield, Regent Court, 30 Regent Street, Sheffield S1 4DA, UK

J.G. Wheeler (JGW), Quanticate Ltd, Bevan House, 9-11 Bancroft Court, Hitchin, Herts, SG5 1LH, UK

Brandon Whitcher (BW), Clinical Imaging Centre, GlaxoSmithKline, Hammersmith Hospital, Du Cane Road, London W12 0HS, UK

Ian White (IW), MRC Biostatistics Unit, Institute of Public Health, University Forvie Site, Robinson Way, Cambridge CB2 0SR, UK

Janet Wittes (JW), 1710 Rhode IslandAve. NW, Suite 200, Washington DC 20036, USA

Mark Woodward (MW), The George Institute for International Health, PO Box M201, Missenden Road, Sydney NSW 2050, Australia

Ru-Fang Yeh (RFY), University of California San Francisco, Campus Box Number 0560, 500 Parnassus, 420 MU-W, San Francisco, CA 94143-0560, USA

Abbreviations and Acronyms

ACESActive control equivalence studyACETActive control equivalence testADAdaptive designAIArtificial intelligenceAICAkaike's information criterionANCOVAAnalysis of covarianceANOVAAnalysis of varianceARAutoregressiveARMAAutoregressive moving averageAUCArea under curveBICBayesian information criterionBUGSBayesian inference Using Gibbs Sampling (software)CACEComplier average causal effectCARTClassification and regression treeCATComputer-adaptive testingCBACost-benefit analysisCEACost-effectiveness analysisCIConfidence intervalCONSORTConsolidation of standards of reporting trialsCORECCentral Office for Research Ethics CommitteesCPMPCommittee for Proprietary Medicinal ProductsCPOConditional predictive ordinateCrICredible intervalCRMContinual reassessment methodCSMCommittee on Safety of MedicinesCUECost-utility analysisCVCoefficient of variationCWTContinuous wavelet transformDAGDirected acyclic graphDALYDisability adjusted life-yearDARDropout at randomDCARDropout completely at randomDDDData-dependent designDEDesign effectDoFDegrees of freedomDICDeviance information criterionDMData miningDMCData monitoring committeeDSMCData and safety monitoring committeeDWTDiscrete wavelet transformDZDizygoticEBMEvidence-based medicineEDAExploratory data analysisEMExpectation-maximisationEMEAEuropean Medicines Evaluation AgencyFDAFood and Drug AdministrationGAMGeneralised additive modelGEEGeneralised estimating equationsGFRGeneral fertility rateGISGeographical information systemGLIMGeneralised linear interactive modeling (software)GLIMMGeneralised linear mixed modelGLMGeneralised linear modelGLMMGeneralised linear mixed modelGRRGross reproduction rateGWASGenome-wide association studiesHALEHealth-adjusted life expectancyHMMHidden Markov modelHPDIHighest posterior density intervalHRECHuman research ethics committeeHRQoLHealth-related quality of lifeIBDIdentity-by-descentICCIntraclass (or intracluster) correlation coefficientICERIncremental cost-effectiveness ratioICHInternational Conference on HarmonizationIRBInstitutional review boardITTIntention-to-treatIVInstrumental variableKDDKnowledge discovery in databasesKMKaplan-MeierkNNk-nearest neighbourLDFLinear discriminant functionLRLikelihood ratioLRECLocal research ethics committeeLSLeast squaresLSTLarge simple trialMAMoving averageMANOVAMultivariate analysis of varianceMARMissing at randomMCAMedicines Control AgencyMCARMissing completely at randomMCMCMarkov chain Monte CarloMHRAMedicines and Healthcare Products Regulatory AgencyMLEMaximum likelihood estimate (or estimation)MRECMulticentre research ethics committeeMSEMean square errorMTDMaximum tolerated doseMZMonozygoticNINonignorable (or noninformative)NMBNet monetary benefitNNHNumber needed to harmNNTNumber needed to treatNPVNegative predictive valueNRESNational Research Ethics ServiceNRRNet reproduction rateOLSOrdinary least squaresOROdds ratioPCAPrincipal component analysisPDFProbability density functionPESTPlanning and Evaluation of Sequential Trials (software)PGMPatient generated measurePHProportional hazardsPK/PDPharmacokinetic s/pharmacody namic sPOPPersuade-the-optimist probabilityPPPer protocolP-PPercentile-percentilePPPPersuade-the-pessimist probabilityPPVPositive predictive valueQALYQuality adjusted life-yearQoLQuality of lifeQ-QQuantile-quantileQTLQuantitative trait lociRCTRandomised controlled trialREBResearch ethics boardRECResearch ethics committeeREMLRestricted maximum likelihoodROCReceiver operating characteristicROIRegion of interestRPWRandomised play-the-winnerRRRelative riskSDStandard deviationSEStandard errorSEMStandard error of the mean; structural equation modelSMRStandardised mortality ratioSPMStatistical parametric mapSPRTSequential probability ratio testSSSum of squaresSSESum of squares due to errorSVMSupport vector machineTDTTransmission distortion testTFRTotal fertility rateTSMTree-structured methodTTTriangular testVASVisual analogue scaleWLSEWeighted least squares estimate (or estimation)

A

accelerated factor See SURVIVAL ANALYSIS

accelerated failure time models See SURVIVAL ANALYSIS, TRANSFORMATION

active control equivalence studies The classic randomised CLINICAL TRIAL seeks to prove superiority of a new treatment to an existing one and a successful conclusion is one in which such proof is demonstrated. The famous MRC trial of streptomycin is a case in point (Medical Research Council Streptomycin in Tuberculosis Trials Committee, 1948). The trial concluded with a significant difference in out come in favour of the group given streptomycin compared to the group that was not. In recent years, however, there has been an increasing interest in trials whose objective is to show that some new therapy is no worse as regards some outcome than an existing treatment. Such trials have particular features and difficulties that were described in an important paper by Makuch and Johnson (1989) in which they used the term ‘active control equivalence studies’ (ACES).

Actually, the term is not ideally chosen since, unlike bioequivalence studies, where the object is to show that the bioavailability of a new formulation is not only at least 20 % less than that of an existing formulation, but also at most 25 % more, and hence where equivalence to some degree is genuinely the aim, in ACES it is almost always the case that only non inferiority is the goal. It may be questioned as to why the rather modest goal of non inferiority should be of any interest in drug regulation. There are several reasons. The first is that the new drug may have advantages in terms of tolerability. Second, the new drug, while showing no net advantage to the existing one, may increase patient choice and this can be useful. For example, many people have an aspirin allergy. Hence, it is desirable to have alternative analgesics, even if no better on average than aspirin. Third, it may become necessary to withdraw treatments from the market and one can never predict when this may happen. There are now several statins on the market. The fact that this is so means that withdrawal of cerivastatin does not make it impossible for physicians to continue to treat their patients with this class of drug. Fourth, introduction of further equivalent therapies before patent expiry of an innovator in the class may permit price competition to the advantage of reimbursors (although such competition is probably not particularly effective; Senn and Rosati, 2003). However, the fifth reason is probably the most important. Drug regulation is designed to satisfy some minimum requirements for pharmaceuticals: that they are of sufficient quality, are safe and efficacious. Efficacy is demonstrated if the treatment is better than placebo, even if it is not as good as some other treatments. The comparison of a new drug to an active treatment may be dictated by ethics but the object of the trial may simply be an indirect proof that the treatment is better than placebo through comparison to an agent whose efficacy is accepted.