title: Quantifying the treatment effect explained by markers in the presence of measurement error.
abstract: Surrogate markers or intermediate markers are important in identifying subjects with high risk of a serious disease or for monitoring disease progression of a subject on treatment. Quantifying the proportion of treatment effect (PTE) explained by markers has been studied extensively. Due to reasons such as biological variation, limited machine precision, etc. markers are generally measured with error. The estimated PTE ignoring the measurement error could be biased, which may lead to incorrect conclusions. In this article, we adjust for the measurement error using regression calibration to construct a less biased estimator of excess relative odds, a quantity to measure the treatment effect explained by markers. The method is applied to data from a clinical study in osteoporosis.
authors.affiliation: Genentech, Inc., South San Francisco, CA 94080, USA. sarkar.somnath@gene.com
authors.forename: Somnath
authors.initials: S
authors.lastname: Sarkar
authors.forename: Yongming
authors.initials: Y
authors.lastname: Qu
mesh_terms: D015502:Absorptiometry, Photon; D015415:Biomarkers / Q000032:analysis*; D015519:Bone Density / Q000187:drug effects; D050071:Bone Density Conservation Agents / Q000494:pharmacology; D002986:Clinical Trials as Topic / Q000379:methods*; D003627:Data Interpretation, Statistical; D050723:Fractures, Bone / Q000517:prevention & control; D006801:Humans; D016015:Logistic Models; D010024:Osteoporosis / Q000188:drug therapy; D020849:Raloxifene Hydrochloride / Q000494:pharmacology; D016896:Treatment Outcome
chemical_list: D015415:Biomarkers; D050071:Bone Density Conservation Agents; D020849:Raloxifene Hydrochloride