期刊
STATISTICS IN MEDICINE
卷 41, 期 13, 页码 2417-2426出版社
WILEY
DOI: 10.1002/sim.9362
关键词
hypothesis test; vaccine efficacy trial; cross validation; AUC; random forest; stacking
类别
资金
- National Institute of Allergy and Infectious Diseases [R01-AI122991, UM1-AI068635]
- National Institutes of Health [S10OD028685]
Testing the global null hypothesis that there are no significant predictors for a binary outcome of interest among a large set of biomarker measurements is crucial in biomedical studies. This paper proposes to enhance the power of such testing methods by utilizing ensemble machine learning techniques. The effectiveness of the proposed methods is demonstrated through Monte Carlo studies and the application to immunologic biomarkers dataset from the RV144 HIV vaccine efficacy trial.
Testing a global null hypothesis that there are no significant predictors for a binary outcome of interest among a large set of biomarker measurements is an important task in biomedical studies. We seek to improve the power of such testing methods by leveraging ensemble machine learning methods. Ensemble machine learning methods such as random forest, bagging, and adaptive boosting model the relationship between the outcome and the predictor nonparametrically, while stacking combines the strength of multiple learners. We demonstrate the power of the proposed testing methods through Monte Carlo studies and show the use of the methods by applying them to the immunologic biomarkers dataset from the RV144 HIV vaccine efficacy trial.
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