title: Statistical considerations when analyzing biomarker data.
abstract: Biomarkers have become, and will continue to become, increasingly important to clinical immunology research. Yet, biomarkers often present new problems and raise new statistical and study design issues to scientists working in clinical immunology. In this paper I discuss statistical considerations related to the important biomarker problems of: 1) The design and analysis of clinical studies which seek to determine whether changes from baseline in a biomarker are associated with changes in a metabolic outcome; 2) The conditions that are required for a biomarker to be considered a "surrogate"; 3) Considerations that arise when analyzing whether or not a predictive biomarker could act as a surrogate endpoint; 4) Biomarker timing relative to the clinical endpoint; 5) The problem of analyzing studies that measure many biomarkers from few subjects; and, 6) The use of statistical models when analyzing biomarker data arising from count data.
authors.affiliation: Division of Epidemiology and Biostatistics, Department of Biomedical Sciences, Western Michigan University Homer Stryker M.D. School of Medicine, 1000 Oakland Dr. Kalamazoo, MI 49009, USA. Electronic address: craig.beam@med.wmich.edu.
authors.forename: Craig A
authors.initials: CA
authors.lastname: Beam
mesh_terms: D015415:Biomarkers / Q000032:analysis*; D002096:C-Peptide / Q000032:analysis; D003922:Diabetes Mellitus, Type 1 / Q000175:diagnosis / Q000188:drug therapy / Q000378:metabolism*; D006801:Humans; D007155:Immunologic Factors / Q000627:therapeutic use; D015233:Models, Statistical; D017063:Outcome Assessment, Health Care / Q000379:methods / Q000706:statistics & numerical data*; D012107:Research Design / Q000706:statistics & numerical data*; D000069283:Rituximab / Q000627:therapeutic use; D013997:Time Factors
chemical_list: D015415:Biomarkers; D002096:C-Peptide; D007155:Immunologic Factors; D000069283:Rituximab
keywords: Biomarker; Statistical design and analysis; Surrogate