title: Clinical utility estimation for assay cutoffs in early phase oncology enrichment trials.
abstract: Predictive enrichment strategies use biomarkers to selectively enroll oncology patients into clinical trials to more efficiently demonstrate therapeutic benefit. Because the enriched population differs from the patient population eligible for screening with the biomarker assay, there is potential for bias when estimating clinical utility for the screening eligible population if the selection process is ignored. We write estimators of clinical utility as integrals averaging regression model predictions over the conditional distribution of the biomarker scores defined by the assay cutoff and discuss the conditions under which consistent estimation can be achieved while accounting for some nuances that may arise as the biomarker assay progresses toward a companion diagnostic. We outline and implement a Bayesian approach in estimating these clinical utility measures and use simulations to illustrate performance and the potential biases when estimation naively ignores enrichment. Results suggest that the proposed integral representation of clinical utility in combination with Bayesian methods provide a practical strategy to facilitate cutoff decision-making in this setting.
authors.affiliation: Merck Research Laboratories, Kenilworth, NJ 07033, USA.
authors.forename: Jared K
authors.initials: JK
authors.lastname: Lunceford
mesh_terms: D001499:Bayes Theorem; D014408:Biomarkers, Tumor / Q000032:analysis / Q000592:standards*; D006801:Humans; D015233:Models, Statistical; D009369:Neoplasms / Q000175:diagnosis / Q000188:drug therapy*; D016032:Randomized Controlled Trials as Topic / Q000379:methods* / Q000592:standards; D012044:Regression Analysis; D016896:Treatment Outcome
chemical_list: D014408:Biomarkers, Tumor
keywords: Bayesian; biomarkers; enrichment; missing data; oncology