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A new semiparametric transformation approach to disease diagnosis with multiple biomarkers
Author(s) -
Lyu Ting,
Ying Zhiliang,
Zhang Hong
Publication year - 2018
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.8047
Subject(s) - computer science , transformation (genetics) , covariance , r package , disease , data mining , machine learning , artificial intelligence , statistics , mathematics , medicine , pathology , biochemistry , chemistry , computational science , gene
When multiple biomarkers are available for disease diagnosis, it is desirable to efficiently combine them to form a single index. Making use of the Neyman‐Pearson paradigm, we propose a new combination/transformation approach to disease diagnosis that efficiently combines multiple biomarkers. The proposed method does not require that the biomarkers be jointly normally distributed or the covariance matrices for the diseased and the nondiseased are nondifferential. An R package is developed to implement the proposed method. Simulations and two real data examples demonstrate advantages of the new method over existing ones.