Abstract
AbstractThis study proposes the comprehensive index of biomarker (CIB), based on the consistency of a biomarker in case control (Youden index, J) and cohort studies (Crc), to evaluate biomarker efficacy. CIB was calculated as the mean of J and Crc. Analysis of the effect of sensitivity and specificity on CIB and ROC analysis of CIB were performed in simulated and actual datasets. J and CIB had similar values for high-probability events (say probability was 0.50), but there was a significant difference between J and CIB for low-probability events (say probability was 0.05). Therefore, as the subjects considered for diagnosis are usually symptomatic, the occurrence of a disease can be assumed to be a high-probability event. In contrast, as the subjects considered in screening for a disease are usually healthy and asymptomatic, the occurrence of a disease is assumed to be a low-probability event. Although J is the common index used to evaluate the diagnostic effectiveness, unfortunately, the J value is significantly larger than CIB value in a low-probability event, showing overestimation for screening purpose. CIB could have more potential than J for determining the screening efficacy of a biomarker. The efficacy of a biomarker could differ for diagnostic, screening, predictive, and prognostic purposes, and it would be better to evaluate the efficacy of biomarkers for specific systems or contexts.
Funder
Special Grant for Scientific and Technological Innovation of Dalian
Publisher
Springer Science and Business Media LLC
Reference13 articles.
1. Hui, L., Rixv, L. & Xiuying, Z. A system for tumor heterogeneity evaluation and diagnosis based on tumor markers measured routinely in the laboratory. Clin. Biochem. 48, 1241–1245 (2015).
2. Wan, L., Li, S. & Liu, H. Diagnostic usefulness of trait specific IgE and multiple immunoglobulin production in allergic diseases. Int. J. Clin. Exp. Med. 10(9), 13577–13587 (2017).
3. Hui, L. & Liping, G. Statistical estimation of diagnosis with genetic markers based on decision tree analysis of complex disease. Comput. Biol. Med. 39(11), 989–992 (2009).
4. Hui, L., Qigui, L., Sashuang, R., Xiliang, L. & Guihong, L. Nonspecific changes in clinical laboratory indicators in unselected terminally ill patients and a model to predict survival time based on a prospective observational study. J. Transl. Med. 12, 78 (2014).
5. Palmas, W. The CONSORT guidelines for noninferiority trials should be updated to go beyond the absolute risk difference. J. Clin. Epidemiol. 83, 6–7 (2017).
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献