Precision and Future Medicine · 2020 · 46 citations · 16 references
Public HealthBiostatisticsClinical SciencesClinical Decision SupportClinical EpidemiologyEpidemiologic MethodsBiomarker AnalysisBiomarker DevelopmentBiomarker Cutoff OptimizationBiomarker Cutoff ValuesBiomarker Threshold OptimizationCutoff Threshold DeterminationCutoff Value SelectionDiagnostic Cutoff ValuesStatistical Significance TestingStatistical Significance Thresholds
In clinical and epidemiological studies, biomarkers are associated with disease diagnosis and prognosis. Using biomarkers to classify subjects into groups, such as high-risk or low-risk, may help with the application of the most appropriate care or procedure within each group. In the case of a continuous biomarker, a cutoff value to define the groups should be determined. A widespread and straightforward method is to select a cutoff value that minimizes the P-value when comparing the outcomes between the two groups. However, a problem that arises with this procedure is that of multiple testing, which leads to an increase in false positive error rate, and thus the significance of the obtained cutoff value tends to be overestimated. In this article, we introduce several methods to correct the P-value for determining the statistical significance of an optimal cutoff value for a quantitatively measured biomarker with applications to clinical data.
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