Testing a global null hypothesis using ensemble machine learning methods

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.

Automated summary

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.