title: Efficient logistic regression designs under an imperfect population identifier.
abstract: Motivated by actual study designs, this article considers efficient logistic regression designs where the population is identified with a binary test that is subject to diagnostic error. We consider the case where the imperfect test is obtained on all participants, while the gold standard test is measured on a small chosen subsample. Under maximum-likelihood estimation, we evaluate the optimal design in terms of sample selection as well as verification. We show that there may be substantial efficiency gains by choosing a small percentage of individuals who test negative on the imperfect test for inclusion in the sample (e.g., verifying 90% test-positive cases). We also show that a two-stage design may be a good practical alternative to a fixed design in some situations. Under optimal and nearly optimal designs, we compare maximum-likelihood and semi-parametric efficient estimators under correct and misspecified models with simulations. The methodology is illustrated with an analysis from a diabetes behavioral intervention trial.
authors.affiliation: Biostatistics and Bioinformatics Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, 6100 Executive Blvd. Room 7B05, Bethesda, Maryland, 20892, U.S.A.
authors.forename: Paul S
authors.initials: PS
authors.lastname: Albert
authors.forename: Aiyi
authors.initials: A
authors.lastname: Liu
authors.forename: Tonja
authors.initials: T
authors.lastname: Nansel
mesh_terms: D000293:Adolescent; D001521:Behavior Therapy / Q000592:standards; D002648:Child; D003198:Computer Simulation; D003922:Diabetes Mellitus, Type 1 / Q000523:psychology; D003951:Diagnostic Errors / Q000009:adverse effects*; D006442:Glycated Hemoglobin / Q000032:analysis; D006801:Humans; D016013:Likelihood Functions; D016015:Logistic Models; D012107:Research Design
chemical_list: D006442:Glycated Hemoglobin
keywords: Case-control designs; Diagnostic accuracy; Epidemiologic designs; Measurement error; Misclassification