title: Unbiased estimation of biomarker panel performance when combining training and testing data in a group sequential design.
abstract: Motivated by an ongoing study to develop a screening test able to identify patients with undiagnosed Sjögren's Syndrome in a symptomatic population, we propose methodology to combine multiple biomarkers and evaluate their performance in a two-stage group sequential design that proceeds as follows: biomarker data is collected from first stage samples; the biomarker panel is built and evaluated; if the panel meets pre-specified performance criteria the study continues to the second stage and the remaining samples are assayed. The design allows us to conserve valuable specimens in the case of inadequate biomarker panel performance. We propose a nonparametric conditional resampling algorithm that uses all the study data to provide unbiased estimates of the biomarker combination rule and the sensitivity of the panel corresponding to specificity of 1-t on the receiver operating characteristic curve (ROC). The Copas and Corbett (2002) correction, for bias resulting from using the same data to derive the combination rule and estimate the ROC, was also evaluated and an improved version was incorporated. An extensive simulation study was conducted to evaluate finite sample performance and propose guidelines for designing studies of this type. The methods were implemented in the National Cancer Institutes Early Detection Network Urinary PCA3 Evaluation Trial.
authors: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston Texas 77030, U.S.A.. ntayob@mdanderson.org.
authors: Nabihah
authors: N
authors: Tayob
authors: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston Texas 77030, U.S.A.
authors: Kim-Anh
authors: KA
authors: Do
authors: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston Texas 77030, U.S.A.
authors: Ziding
authors: Z
authors: Feng
mesh_terms: D000465:Algorithms; D000951:Antigens, Neoplasm / Q000652:urine; D015415:Biomarkers; D003198:Computer Simulation; D042241:Early Diagnosis; D006801:Humans; D008297:Male; D015233:Models, Statistical; D011471:Prostatic Neoplasms / Q000175:diagnosis; D018709:Statistics, Nonparametric; D000069553:Supervised Machine Learning
chemical_list: D000951:Antigens, Neoplasm; D015415:Biomarkers; C479341:prostate cancer antigen 3, human
keywords: Biomarker panel; Conditional estimation; Logistic regression; Shrinkage correction; Two-stage design