@article{fe5cd8099c7f457eb87e25696b1fdd57,
title = "Conditional concordance-assisted learning under matched case-control design for combining biomarkers for population screening",
abstract = "Incorporating promising biomarkers into cancer screening practices for early-detection is increasingly appealing because of the unsatisfactory performance of current cancer screening strategies. The matched case-control design is commonly adopted in biomarker development studies to evaluate the discriminative power of biomarker candidates, with an intention to eliminate confounding effects. Data from matched case-control studies have been routinely analyzed by the conditional logistic regression, although the assumed logit link between biomarker combinations and disease risk may not always hold. We propose a conditional concordance-assisted learning method, which is distribution-free, for identifying an optimal combination of biomarkers to discriminate cases and controls. We are particularly interested in combinations with a clinically and practically meaningful specificity to prevent disease-free subjects from unnecessary and possibly intrusive diagnostic procedures, which is a top priority for cancer population screening. We establish asymptotic properties for the derived combination and confirm its favorable finite sample performance in simulations. We apply the proposed method to the prostate cancer data from the carotene and retinol efficacy trial (CARET).",
keywords = "conditional concordance-assisted learning, conditional logistic regression, matched case-control studies, sensitivity, specificity",
author = "Wen Li and Ruosha Li and Qingxiang Yan and Ziding Feng and Jing Ning",
note = "Funding Information: This work was partially supported by the National Institutes of Health (NIH) grants R01CA269696 (Jing Ning), U24CA230144 (Jing Ning, Ziding Feng) and R01DK117209 (Ruosha Li), and CPRIT grant RP200633 (Jing Ning). We acknowledge the support provided by the Biostatistics/ Epidemiology/ Research Design (BERD) component of the Center for Clinical and Translational Sciences (CCTS) for this project that is currently funded through a grant (UL1TR003167), funded by the National Center for Advancing Translational Sciences (NCATS), awarded to the University of Texas Health Science Center at Houston. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NCATS. The authors acknowledge the Texas Advanced Computing Center (TACC) at The University of Texas at Austin for providing HPC resources that have contributed to the results reported in this paper. Funding Information: information National Institutes of Health (NIH), Grant/Award Numbers: U24CA230144, R01CA269696, R01DK117209; CPRIT, Grant/Award Number: RP200633; Clinical and Translational Sciences (CCTS), Grant/Award Number: UL1TR003167; National Center for Advancing Translational Sciences (NCATS)This work was partially supported by the National Institutes of Health (NIH) grants R01CA269696 (Jing Ning), U24CA230144 (Jing Ning, Ziding Feng) and R01DK117209 (Ruosha Li), and CPRIT grant RP200633 (Jing Ning). We acknowledge the support provided by the Biostatistics/ Epidemiology/ Research Design (BERD) component of the Center for Clinical and Translational Sciences (CCTS) for this project that is currently funded through a grant (UL1TR003167), funded by the National Center for Advancing Translational Sciences (NCATS), awarded to the University of Texas Health Science Center at Houston. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NCATS. The authors acknowledge the Texas Advanced Computing Center (TACC) at The University of Texas at Austin for providing HPC resources that have contributed to the results reported in this paper. Publisher Copyright: {\textcopyright} 2023 John Wiley & Sons Ltd.",
year = "2023",
month = apr,
day = "30",
doi = "10.1002/sim.9677",
language = "English (US)",
volume = "42",
pages = "1398--1411",
journal = "Statistics in Medicine",
issn = "0277-6715",
publisher = "John Wiley and Sons Ltd",
number = "9",
}