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Statistical methods of determining a cut off value between normal and disease groups

 Inyoung Kim  ;  Yeon-ho Choi  ;  Byung Soo Kim  ;  Sun Young Rha  ;  Hyun Cheol Chung 
 Bulletin of Informatics and Cybernetics, Vol.36 : 63-72, 2004 
Journal Title
 Bulletin of Informatics and Cybernetics 
Issue Date
Biomarker ; Cross validation ; Cut off value ; ELISA ; Linear mixed effect model
The classical method to determine the cut off value between normal and disease group is to calculate two standard deviations of the difference between mean values of two groups under the independence assumption. However, this independence assumption does not hold in general, and in our study in particular, when two biomarker proteins of breast cancer are measured several times for a few years. In this paper we propose a method to determine the cut off value for this case by implementing the inherent nature of the study using linear mixed effect model. We use a linear mixed effect model to take it into consideration that the subject is of a random effect. Furthermore, we can also estimate the growth curve of the biomarker values as time elapses. For a fixed type I error rate we calculate the conditional probability that the test is positive given that the subject does have the disease and then compare the sensitivity of our method with that of classical method using a leave-one-out cross validation. We observe that our method is more efficient than the classical method.
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1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
Yonsei Authors
Rha, Sun Young(라선영) ORCID logo https://orcid.org/0000-0002-2512-4531
Chung, Hyun Cheol(정현철) ORCID logo https://orcid.org/0000-0002-0920-9471
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