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Articles

Medical diagnostics accuracy measures and cut-point selection: an innovative approach based on relative net benefit

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Pages 5010-5025 | Received 16 Jun 2021, Accepted 27 Oct 2021, Published online: 08 Nov 2021

References

  • Baker, S. G., N. R. Cook, A. Vickers, and B. S. Kramer. 2009. Using relative utility curves to evaluate risk prediction. Journal of the Royal Statistical Society: Series A (Statistics in Society) 172 (4):729–48. doi:10.1111/j.1467-985X.2009.00592.x.
  • Baker, S. G., and B. S. Kramer. 2012. Evaluating a new marker for risk prediction: Decision analysis to the rescue. Discovery Medicine 14 (76):181–8.
  • Bossuyt, P. M., J. G. Lijmer, and B. W. Mol. 2000. Randomized comparisons of medical tests: Sometimes invalid, not always efficient. The Lancet 356 (9244):1844–7. doi:10.1016/S0140-6736(00)03246-3.
  • Evans, S. R., G. Pennello, N. Pantoja-Galicia, H. Jiang, A. M. Hujer, K. M. Hujer, C. Manca, C. Hill, M. R. Jacobs, L. Chen, et al. 2016. Benefit-risk evaluation for diagnostics: A framework (BED-FRAME). Clinical Infectious Diseases 63 (6):812–7. doi:10.1093/cid/ciw329.
  • Gail, M. H., and R. M. Pfeiffer. 2005. On criteria for evaluating models of absolute risk. Biostatistics 6 (2):227–39. doi:10.1093/biostatistics/kxi005.
  • Hoering, A., M. Leblanc, and J. Crowley. 2008. Randomized Phase III clinical trial designs for targeted agents. Clinical Cancer Research 14 (14):4358–67. doi:10.1158/1078-0432.CCR-08-0288.
  • Kadam, V. J., S. M. Jadhav, and K. Vijayakumar. 2019. Breast cancer diagnosis using feature ensemble learning based on stacked sparse autoencoders and softmax regression. Journal of Medical Systems 43 (8):263–74. doi:10.1007/s10916-019-1397-z.
  • Kerr, K. F., T. L. Marsh, and H. Janes. 2019. The importance of uncertainty and opt-in v. opt-out: best practices for decision curve analysis. Medical Decision Making 39 (5):491–2. doi:10.1177/0272989X19849436.
  • Li, J., J. P. Fine, and N. Safdar. 2007. Prevalence-dependent diagnostic accuracy measures. Statistics in Medicine 26 (17):3258–73. doi:10.1002/sim.2812.
  • Li, J., and J. P. Fine. 2011. Assessing the dependence of sensitivity and specificity on prevalence in meta-analysis. Biostatistics 12 (4):710–22. doi:10.1093/biostatistics/kxr008.
  • Marsh, T. L., H. Janes, and M. S. Pepe. 2020. Statistical inference for net benefit measures in biomarker validation studies. Biometrics 76 (3):843–52. doi:10.1111/biom.13190.
  • Packer, C. H., C. G. Zhou, A. R. Hersh, A. J. Allen, A. C. Hermesch, and A. B. Caughey. 2020. Antenatal corticosteroids for pregnant women at high risk of preterm delivery with COVID-19 Infection: A decision analysis. American Journal of Perinatology 37 (10):1015–21.
  • Pennello, G., N. Pantoja-Galicia, and S. Evans. 2016. Comparing diagnostic tests on benefit-risk. Journal of Biopharmaceutical Statistics 26 (6):1083–97. doi:10.1080/10543406.2016.1226335.
  • Pepe, M. S., H. Janes, C. I. Li, P. M. Bossuyt, Z. Feng, and J. Hilden. 2016. Early-phase studies of biomarkers: What target sensitivity and specificity values might confer clinical utility? Clinical Chemistry 62 (5):737–42. doi:10.1373/clinchem.2015.252163.
  • Samawi, H. M., J. Yin, X. Zhang, L. Yu, H. Rochani, R. Vogel, and C. Mo. 2020. Kullback-Leibler divergence for medical diagnostics accuracy and cut-point selection criterion: How it is related to the Youden index. Journal of Applied Bioinformatics & Computational Biology 9 (2):1–10.
  • Sande, S. Z., J. Li, R. D’Agostino, T. Yin Wong, and C. Y. Cheng. 2020. Statistical inference for decision curve analysis, with applications to cataract diagnosis. Statistics in Medicine 39 (22):2980–3002. doi:10.1002/sim.8588.
  • Simon, R. 2010. Clinical trial designs for evaluating the medical utility of prognostic and predictive biomarkers in oncology. Personalized Medicine 7 (1):33–47. doi:10.2217/pme.09.49.
  • Rapsomaniki, E., I. R. White, A. M. Wood, and S. G. Thompson, Emerging Risk Factors Collaboration. 2012. A framework for quantifying net benefits of alternative prognostic models. Statistics in Medicine 31 (2):114–30. doi:10.1002/sim.4362.
  • Tsalik, E. L., Y. Li, L. L. Hudson, V. H. Chu, T. Himmel, A. T. Limkakeng, J. N. Katz, S. W. Glickman, M. T. McClain, K. E. Welty-Wolf, et al. 2016. Potential cost-effectiveness of early identification of hospital-acquired infection in critically Ill patients. Annals of the American Thoracic Society 13 (3):401–13. doi:10.1513/AnnalsATS.201504-205OC.
  • Vickers, A. J., and E. B. Elkin. 2006. Decision curve analysis: A novel method for evaluating prediction models. Medical Decision Making 26 (6):565–74. doi:10.1177/0272989X06295361.
  • Wynants, L., R. D. Riley, D. Timmerman, and B. Van Calster. 2018. Random-effects meta-analysis of the clinical utility of tests and prediction models. Statistics in Medicine 37 (12):2034–52. doi:10.1002/sim.7653.
  • Zweig, M. H., and G. Campbell. 1993. Receiver-operating characteristic (ROC) plots: A Fundamental evaluation tool in clinical medicine. Clinical Chemistry 39 (4):561–77.

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