Statistical considerations when analyzing biomarker data

•Design of the metabolic outcome-biomarker clinical study•Predictive biomarker as surrogate endpoint•Biomarker timing in clinical studies•Many biomarkers, few subjects•Biomarkers from count data

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.