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Comparison of three‐dimensional ROC surfaces for clustered and correlated markers, with a proteomics application
Author(s) -
Sultana and Jialiang Li Mst. Papia,
Hu Jianhua
Publication year - 2015
Publication title -
statistica neerlandica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 39
eISSN - 1467-9574
pISSN - 0039-0402
DOI - 10.1111/stan.12065
Subject(s) - receiver operating characteristic , pattern recognition (psychology) , parametric statistics , correlation , sample size determination , statistical hypothesis testing , sample (material) , mathematics , artificial intelligence , statistics , computer science , chromatography , chemistry , geometry
We propose a non‐parametric test to compare two correlated diagnostic tests for a three‐category classification problem. Our development was motivated by a proteomic study where the objectives are to detect glycan biomarkers for liver cancer and to compare the discrimination ability of various markers. Three distinct disease categories need to be identified from this analysis. We therefore chose to use three‐dimensional receiver operating characteristic (ROC) surfaces and volumes under the ROC surfaces to describe the overall accuracy for different biomarkers. Each marker in this study might include a cluster of similar individual markers and thus was considered as a hierarchically structured sample. Our proposed statistical test incorporated the within‐marker correlation as well as the between‐marker correlation. We derived asymptotic distributions for three‐dimensional ROC surfaces and subsequently implemented bootstrap methods to facilitate the inferences. Simulation and real‐data analysis were included to illustrate our methods. Our distribution‐free test may be simplified for paired and independent two‐sample comparisons as well. Previously, only parametric tests were known for clustered and correlated three‐category ROC analyses.