Journal
STATISTICAL METHODS IN MEDICAL RESEARCH
Volume 30, Issue 1, Pages 87-98Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/0962280220938077
Keywords
Diagnostic study; ROC curve; area under ROC curve; biomarker evaluation; pooling strategy
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This paper examines the potential misleading effects of commonly used pooling strategies in biomarker evaluation, proposing a new diagnostic framework and accuracy measures for such settings. Additionally, an ovarian cancer dataset is analyzed to demonstrate these concepts.
In practice, it is common to evaluate biomarkers in binary classification settings (e.g. non-cancer vs. cancer) where one or both main classes involve multiple subclasses. For example, non-cancer class might consist of healthy subjects and benign cases, while cancer class might consist of subjects at early and late stages. The standard practice is pooling within each main class, i.e. all non-cancer subclasses are pooled together to create a control group, and all cancer subclasses are pooled together to create a case group. Based on the pooled data, the area under ROC curve (AUC) and other characteristics are estimated under binary classification for the purpose of biomarker evaluation. Despite the popularity of this pooling strategy in practice, its validity and implication in biomarker evaluation have never been carefully inspected. This paper aims to demonstrate that pooling strategy can be seriously misleading in biomarker evaluation. Furthermore, we present a new diagnostic framework as well as new accuracy measures appropriate for biomaker evaluation under such settings. In the end, an ovarian cancer data set is analyzed.
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