title: Biomarker studies and other difficult inferential problems: statistical caveats.
abstract: The inferential issues associated with biomarker studies are enormously complex. False-positive conclusions are rampant in the literature. It is wonderful to have many potential biomarkers in trying to explain the heterogeneity of cancer and outcomes of its treatment. But a large number of biomarkers give rise to statistical headaches. False-positives proliferate. A useful approach is to reduce many biomarkers into a single dimension, and to then attempt to confirm the prognostic or predictive value of the single-dimensional quantity. This is not a panacea for all statistical and scientific ailments, but it minimizes some of the problems. A related concern is subset analysis. I give a statistical argument that estrogen-receptor status is predictive of the benefits of chemotherapy in node-positive breast cancer.
authors.affiliation: The University of Texas, M. D. Anderson Cancer Center, Houston, TX 77030, USA. dberry@mdanderson.org
authors.forename: Donald A
authors.initials: DA
authors.lastname: Berry
mesh_terms: D014408:Biomarkers, Tumor / Q000032:analysis*; D001943:Breast Neoplasms / Q000175:diagnosis / Q000188:drug therapy* / Q000473:pathology; D002986:Clinical Trials as Topic; D005189:False Positive Reactions; D005260:Female; D006801:Humans; D053208:Kaplan-Meier Estimate; D011237:Predictive Value of Tests; D011379:Prognosis; D011960:Receptors, Estrogen / Q000032:analysis*; D012107:Research Design
chemical_list: D014408:Biomarkers, Tumor; D011960:Receptors, Estrogen