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Abstract
It has become increasingly common in epidemiological studies to pool specimens across subjects as a useful cot-cutting technique to achieve accurate quantification of biomarkers and certain environmental chemicals. The data collected from these pooled samples can then be utilized to estimate the Youden Index, which measures biomarker's effectiveness and aids in the selection of an optimal threshold value, as a summary measure of the Receiver Operating Characteristic curve. The aim of this paper is to make use of generalized approach to estimate and testing of the Youden index. This goal is accomplished by the comparison of classical and generalized procedures for the Youden Index with the aid of pooled samples from the shifted-exponentially distributed biomarkers for the low-risk and high-risk patients. These are juxtaposed using confidence intervals, p-values, power of the test, size of the test, and coverage probability with a wide-ranging simulation study featuring a selection of various scenarios. In order to demonstrate the advantages of the proposed generalized procedures over its classical counterpart, an illustrative example is discussed using the Duchenne Muscular Dystrophy data available at http://biostat.mc.vanderbilt.edu/wiki/Main/DataSets or http://lib.stat.cmu.edu/datasets/.
Acknowledgements
The content is solely the responsibility of the authors and does not necessarily represent the official views of the SimCenter – The Center of Excellence for Applied Computational Science and Engineering, Tennessee Higher Education Commission (THEC). We are greatly indebted to the Editor-in-Chief, Associate Editor, and the Reviewers for the very constructive suggestions and the useful comments which improved the earlier version of the manuscript.
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This article has been republished with minor changes. These changes do not impact the academic content of the article.
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Sumith Gunasekera
Sumith Gunasekera received the (Bachelor of Science with Honors) B.Sc. (Hons.) degree in Physics in 1995 from the University of Colombo, Colpetty, Colombo 03, District of Colombo, Western Province, Democratic Socialist Republic of Sri Lanka (DSRSL) (formerly known as Seelan (in Latin), Ceilao (in Portuguese under their rule), Ceilan (in Spanish), Selon, Ceylan (in French), Zeilan, Ceilan, Seylan, and Seylon (by Dutch under their rule), and Ceylon (by British under their rule), and the (Doctor of Philosophy) Ph.D. degree in Statistics in 2009 from the University of Nevada at Las Vegas (UNLV), Las Vegas, NV, The United States of America (U.S.A.). Sumith joined the Department of Mathematics at The University of Tennessee at Chattanooga (UTC), Chattanooga TN, U.S.A. in 2009, and has been an Associate Professor of Statistics since 2015. He is the author of many seminal statistical articles and is the recipient of several grants and awards. His research interests include statistical inference, reliability, survival analysis, design of experiments under various statistical inference frameworks, such as classical, Bayesian, parametric, nonparametric, semiparametric, generalized variable, and fiducial frameworks.
Lakmali Weerasena
Lakmali Weerasena received the (Bachelor of Science, Honors) B.Sc. (Hons.) degree in Mathematics in 2003 from the University of Peradeniya, Peradeniya, District of Kandy, Central Province, Democratic Socialist Republic of Sri Lanka (DSRSL) (formerly known as Ceylon by British under their rule), and the (Doctor of Philosophy) Ph.D. degree in Operations Research in 2013 from Clemson University, Clemson, South Carolina, The United States of America (U.S.A.). Lakmali joined as a tenure-track Assistant Professor in the Department of Mathematics at The University of Tennessee at Chattanooga, Chattanooga TN, U.S.A. in 2016. She is the author of several operations research, mathematical, and statistical articles and is the recipient of several grants and awards. Her research interests include developing approximation algorithms for optimization problems, multi-objective Optimization, combinatorial optimization, integer optimization, Numerical optimization, applications of optimization, mathematical modeling, statistical approach for optimization problems, and statistical simulations and analysis.
Aruna Saram
Aruna Saram joined the doctoral program in Computational Science with concentration in Computational & Applied Mathematics offered jointly by the College of Engineering, Graduate School, and the Department of Mathematics at the University of Tennessee at Chattanooga (UTC) in August 2017 after finishing his Master's degree (MS) in Statistics in August 2006 at Sam Houston State University, TX, and Master of Business Administration (MBA) with concentration in Actuarial Science at St. John's University in Manhattan, NY in August 2000. He is currently doing research and doctoral dissertation under the supervision of Dr Sumith Gunasekera and plans to graduate in December 2019.
Oluwakorede Ajumobi
Oluwakorede Ajumobi is a native of Lagos, Nigeria. In 2017, he enrolled at the University of Tennessee at Chattanooga, to pursue a Masters of Science degree in Mathematics with an emphasis in Applied Statistics. His research interest focuses on how reliability in a multicomponent stress-strength systemcan be estimated based on progressively-censored data from Chen distribution.He plans to graduate under the supervision of Dr Sumith Guansekera in May 2019.