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.
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.
Authors: Maria-Antonietta D'Agostino; Maarten Boers; John Kirwan; Désirée van der Heijde; Mikkel Østergaard; Georg Schett; Robert B Landewé; Walter P Maksymowych; Esperanza Naredo; Maxime Dougados; Annamaria Iagnocco; Clifton O Bingham; Peter M Brooks; Dorcas E Beaton; Frederique Gandjbakhch; Laure Gossec; Francis Guillemin; Sarah E Hewlett; Margreet Kloppenburg; Lyn March; Philip J Mease; Ingrid Moller; Lee S Simon; Jasvinder A Singh; Vibeke Strand; Richard J Wakefield; George A Wells; Peter Tugwell; Philip G Conaghan Journal: J Rheumatol Date: 2014-03-01 Impact factor: 4.666