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Definitions and Validation Criteria for Biomarkers and Surrogate Endpoints: Development and Testing of a Quantitative Hierarchical Levels of Evidence Schema
MARISSA N. LASSERE, KENT R. JOHNSON, MAARTEN BOERS, PETER TUGWELL, PETER BROOKS, LEE SIMON, VIBEKE STRAND, PHILIP G. CONAGHAN, MIKKEL ØSTERGAARD, WALTER P. MAKSYMOWYCH, ROBERT LANDEWÉ, BARRY BRESNIHAN, PAUL-PETER TAK, RICHARD WAKEFIELD, PHILIP MEASE, CLIFTON O. BINGHAM III, MICHAEL HUGHES, DOUG ALTMAN, MARC BUYSE, SALLY GALBRAITH, and GEORGE WELLS ABSTRACT. Objective. There are clear advantages to using biomarkers and surrogate endpoints, but concerns about clinical and statistical validity and systematic methods to evaluate these aspects hinder their efficient application. Our objective was to review the literature on biomarkers and surrogates to develop a hierarchical schema that systematically evaluates and ranks the surrogacy status of biomarkers and surrogates; and to obtain feedback from stakeholders. Methods. After a systematic search of Medline and Embase on biomarkers, surrogate (outcomes, endpoints, markers, indicators), intermediate endpoints, and leading indicators, a quantitative surrogate validation schema was developed and subsequently evaluated at a stakeholder workshop. Results. The search identified several classification schema and definitions. Components of these were incorporated into a new quantitative surrogate validation level of evidence schema that evaluates biomarkers along 4 domains: Target, Study Design, Statistical Strength, and Penalties. Scores derived from 3 domains — the Target that the marker is being substituted for, the Design of the (best) evidence, and the Statistical strength — are additive. Penalties are then applied if there is serious counterevidence. A total score (0 to 15) determines the level of evidence, with Level 1 the strongest and Level 5 the weakest. It was proposed that the term "surrogate" be restricted to markers attaining Levels 1 or 2 only. Most stakeholders agreed that this operationalization of the National Institutes of Health definitions of biomarker, surrogate endpoint, and clinical endpoint was useful. Conclusion. Further development and application of this schema provides incentives and guidance for effective biomarker and surrogate endpoint research, and more efficient drug discovery, development, and approval. (J Rheumatol 2007;34:607-15) Key Indexing Terms:
SURROGATE From the Department of Rheumatology, St. George Hospital, University of New South Wales, Sydney, Australia. M.N. Lassere, MB, BS, Grad Dip Epi, PhD, Associate Professor in Medicine, Department of Rheumatology, St. George Hospital, University of NSW, Sydney, Australia; K.R. Johnson, MD, Senior Lecturer in Medicine, University of Newcastle, Newcastle, University of New South Wales, Sydney, Australia; M. Boers, MD, PhD, Professor, Department of Clinical Epidemiology, VU University Medical Centre, Amsterdam, The Netherlands; P. Tugwell, MD, MSc, Professor, Departments of Medicine, and Epidemiology and Community Medicine, Canada Research Chair and Principal Scientist, Institute of Population Health, University of Ottawa, Ottawa, Canada; P. Brooks, MBBS, MD, Executive Dean of Health Sciences, The University of Queensland, Brisbane, Australia; L. Simon, MD, Associate Clinical Professor of Medicine, Harvard Medical School, Boston, Massachusetts, USA; V. Strand, MD, Clinical Professor of Medicine, Department of Immunology, Stanford University, Palo Alto, California, USA; P.G. Conaghan, MB, BS, PhD, Professor of Musculoskeletal Medicine, Academic Unit of Musculoskeletal Disease, University of Leeds, Leeds, United Kingdom; M. Østergaard, MD, PhD, DMSc, Professor in Rheumatology/Arthritis, Copenhagen University Hospitals at Herlev and Hvidovre, Copenhagen, Denmark; W.P. Maksymowych, FRCPC, Professor, Department of Medicine, University of Alberta, Edmonton, Alberta, Canada; R. Landewé, MD, PhD, Associate Professor, Department of Medicine, University of Maastricht, Maastricht, The Netherlands; B. Bresnihan, PhD, Professor, University College Dublin, Dublin, Ireland; P-P. Tak, MD, PhD, Professor, Division of Clinical Immunology and Rheumatology, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands; R. Wakefield, BM, Senior Lecturer in Rheumatology, Academic Department of Musculoskeletal Medicine, Faculty of Medicine and Health, University of Leeds, Leeds, United Kingdom; P. Mease, Clinical Professor, Swedish Medical Center and University of Washington School of Medicine, Seattle, Washington, USA; C.O. Bingham III, MD, Assistant Professor of Medicine, Johns Hopkins University, Baltimore, Maryland, USA; M. Hughes, PhD, Professor of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA; D. Altman, BSc, DSc, Professor of Statistics in Medicine, Centre for Statistics in Medicine, Wolfson College, Oxford, United Kingdom; M. Buyse, ScD, Executive Director, IDDI, Louvain-la-Neuve, Belgium; S. Galbraith, BMaths, DSc, Lecturer, School of Mathematics and Statistics, University of New South Wales, Sydney, Australia; G. Wells, PhD, MSc, Professor, Department of Epidemiology and Community Medicine, University of Ottawa, Ottawa, Ontario, Canada. Address reprint requests to Prof. M.N. Lassere, Department of Rheumatology, St. George Hospital, Gray Street, Kogarah 2217, Australia. E-mail: marissa.lassere@sesiahs.health.nsw.gov.au
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