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Learning‐based biomarker‐assisted rules for optimized clinical benefit under a risk constraint
Author(s) -
Wang Yanqing,
Zhao YingQi,
Zheng Yingye
Publication year - 2020
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.13199
Subject(s) - computer science , nonparametric statistics , harm , biomarker , parametric statistics , bayes' theorem , machine learning , constraint (computer aided design) , risk analysis (engineering) , data mining , artificial intelligence , medicine , bayesian probability , mathematics , econometrics , statistics , biochemistry , chemistry , geometry , political science , law
Novel biomarkers, in combination with currently available clinical information, have been sought to improve clinical decision making in many branches of medicine, including screening, surveillance, and prognosis. Statistical methods are needed to integrate such diverse information to develop targeted interventions that balance benefit and harm. In the specific setting of disease detection, we propose novel approaches to construct a multiple‐marker‐based decision rule by directly optimizing a benefit function, while controlling harm at a maximally tolerable level. These new approaches include plug‐in and direct‐optimization‐based algorithms, and they allow for the construction of both nonparametric and parametric rules. A study of asymptotic properties of the proposed estimators is provided. Simulation results demonstrate good clinical utilities for the resulting decision rules under various scenarios. The methods are applied to a biomarker study in prostate cancer surveillance.