Key result
Stepwise biomarker selection maximizing pAUC successfully identifies significant biomarkers in moderate-sized datasets.
Why the study?
Finding ideal biomarkers with sufficient discriminatory power is challenging, and combining multiple biomarkers using optimal linear combinations maximizing the partial area under the ROC curve (pAUC) may improve assessment.
A novel statistical approach maximizing the partial area under the ROC curve provides a method for selecting significant biomarkers from datasets with a moderate number of candidates.
May aid diagnostic biomarker selection in moderate panels; leaves open prospective validation before clinical use.
BACKGROUND: A biomarker is usually used as a diagnostic or assessment tool in medical research. Finding an ideal biomarker is not easy and combining multiple biomarkers provides a promising alternative. Moreover, some biomarkers based on the optimal linear combination do not have enough discriminatory power. As a result, the aim of this study was to find the significant biomarkers based on the optimal linear combination maximizing the pAUC for assessment of the biomarkers. METHODS: Under the binormality assumption we obtain the optimal linear combination of biomarkers maximizing the partial area under the receiver operating characteristic curve (pAUC). Related statistical tests are developed for assessment of a biomarker set and of an individual biomarker. Stepwise biomarker selections are introduced to identify those biomarkers of statistical significance. RESULTS: The results of simulation study and three real examples, Duchenne Muscular Dystrophy disease, heart disease, and breast tissue example are used to show that our methods are most suitable biomarker selection for the data sets of a moderate number of biomarkers. CONCLUSIONS: Our proposed biomarker selection approaches can be used to find the significant biomarkers based on hypothesis testing.
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Hsu et al. (2014) studied Medical diagnosis. Stepwise biomarker selection using pAUC vs. Full biomarker set / LASSO was evaluated on Maximal partial area under the ROC curve (pAUC). The proposed stepwise biomarker selection approaches based on maximizing the partial area under the ROC curve successfully identified significant biomarkers in datasets with a moderate number of biomarkers.
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