title: Biomarker validation: common data analysis concerns.
abstract: Biomarker validation, like any other confirmatory process based on statistical methodology, must discern associations that occur by chance from those reflecting true biological relationships. Validity of a biomarker is established by authenticating its correlation with clinical outcome. Validated biomarkers can lead to targeted therapy, improve clinical diagnosis, and serve as useful prognostic and predictive factors of clinical outcome. Statistical concerns such as confounding and multiplicity are common in biomarker validation studies. This article discusses four major areas of concern in the biomarker validation process and some of the proposed solutions. Because present-day statistical packages enable the researcher to address these common concerns, the purpose of this discussion is to raise awareness of these statistical issues in the hope of improving the reproducibility of validation study findings.
authors.affiliation: The University of Texas MD Anderson Cancer Center, Houston, Texas, USA joensor@mdanderson.org.
authors.forename: Joe E
authors.initials: JE
authors.lastname: Ensor
mesh_terms: D014408:Biomarkers, Tumor / Q000235:genetics* / Q000627:therapeutic use; D006801:Humans; D058990:Molecular Targeted Therapy; D009369:Neoplasms / Q000175:diagnosis / Q000188:drug therapy / Q000453:epidemiology* / Q000235:genetics* / Q000473:pathology; D011379:Prognosis; D016896:Treatment Outcome
chemical_list: D014408:Biomarkers, Tumor
keywords: Biomarker; Confounding factors; Selection bias; Validation studies