title: The Fundamental Difficulty With Evaluating the Accuracy of Biomarkers for Guiding Treatment.
abstract: Developing biomarkers that can predict whether patients are likely to benefit from an intervention is a pressing objective in many areas of medicine. Recent guidance documents have recommended that the accuracy of predictive biomarkers, ie, sensitivity, specificity, and positive and negative predictive values, should be assessed. We clarify the meanings of these entities for predictive markers and demonstrate that generally they cannot be estimated from data without making strong untestable assumptions. Language suggesting that predictive biomarkers can identify patients who benefit from an intervention is also widespread. We show that in general one cannot estimate the chance that a patient will benefit from treatment. We recommend instead that predictive biomarkers be evaluated with respect to their ability to predict clinical outcomes among patients treated and among patients receiving standard of care, and the population impact of treatment rules based on those predictions. Ideally these entities are estimated from a randomized trial comparing the experimental intervention with standard of care.
authors.affiliation: Divisions of Vaccine and Infectious Disease and Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington (HJ, MSP); Department of Biostatistics, University of Washington, Seattle, Washington (HJ, MSP, PJH); Biometric Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Rockville, Maryland (LMM); Department of Health Science Research, Mayo Clinic, Rochester, Minnesota (DJS). hjanes@fhcrc.org.
authors.forename: Holly
authors.initials: H
authors.lastname: Janes
authors.affiliation: Divisions of Vaccine and Infectious Disease and Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington (HJ, MSP); Department of Biostatistics, University of Washington, Seattle, Washington (HJ, MSP, PJH); Biometric Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Rockville, Maryland (LMM); Department of Health Science Research, Mayo Clinic, Rochester, Minnesota (DJS).
authors.forename: Margaret S
authors.initials: MS
authors.lastname: Pepe
authors.affiliation: Divisions of Vaccine and Infectious Disease and Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington (HJ, MSP); Department of Biostatistics, University of Washington, Seattle, Washington (HJ, MSP, PJH); Biometric Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Rockville, Maryland (LMM); Department of Health Science Research, Mayo Clinic, Rochester, Minnesota (DJS).
authors.forename: Lisa M
authors.initials: LM
authors.lastname: McShane
authors.affiliation: Divisions of Vaccine and Infectious Disease and Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington (HJ, MSP); Department of Biostatistics, University of Washington, Seattle, Washington (HJ, MSP, PJH); Biometric Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Rockville, Maryland (LMM); Department of Health Science Research, Mayo Clinic, Rochester, Minnesota (DJS).
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Sargent
authors.affiliation: Divisions of Vaccine and Infectious Disease and Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington (HJ, MSP); Department of Biostatistics, University of Washington, Seattle, Washington (HJ, MSP, PJH); Biometric Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Rockville, Maryland (LMM); Department of Health Science Research, Mayo Clinic, Rochester, Minnesota (DJS).
authors.forename: Patrick J
authors.initials: PJ
authors.lastname: Heagerty
mesh_terms: D000970:Antineoplastic Agents / Q000494:pharmacology; D014408:Biomarkers, Tumor; D003210:Concept Formation; D006801:Humans; D058990:Molecular Targeted Therapy / Q000379:methods; D011237:Predictive Value of Tests; D016032:Randomized Controlled Trials as Topic; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016896:Treatment Outcome
chemical_list: D000970:Antineoplastic Agents; D014408:Biomarkers, Tumor