AdimariDToDChiognaM. Letter to the editor: non-monotone transformation of biomarkers. Stat Methods Med Resin press.
2.
YangJKuanPLiJ. Non-monotone transformation of biomarkers to improve diagnostic and screening accuracy in a DNA methylation study with trichotomous phenotypes. Stat Methods Med Resin press.
3.
PepeMS.The statistical evaluation of medical tests for classification and prediction.
Cambridge:
Cambridge Press, 2003.
4.
NewtonMKendziorskiCRichmondC, et al.
On differential variability of expression ratios: improving statistical inference about gene expression changes from microarray data.J Comput Biol2001;
8: 37–52.
5.
ChurchBVWilliamsHTMarJC.Investigating skewness to understand gene expression heterogeneity in large patient cohorts.BMC Bioinformatics2019;
20: 668.
6.
KalbfleishJDPrenticeRL.The statistical analysis of failure time data.
New York:
John Wiley & Sons, Inc., 1980.
7.
LiJMaS.Survival analysis in medicine and genetics.
Boca Raton:
CRC Press, 2013.
8.
OrlenkoAMooreJOrzechowskiP, et al.
Considerations for automated machine learning in clinical metabolic profiling: altered homocysteine plasma concentration associated with metformin exposure. Pac Symp Biocomput2018;
23: 460–471.
9.
WangTLiBNelsonCE, et al.
Comparative analysis of differential gene expression analysis tools for single-cell RNA sequencing data.BMC Bioinformatics2019;
20: 40.
10.
JinHLuY.A procedure for determining whether a simple combination of diagnostic tests may be noninferior to the theoretical optimum combination.Med Decis Making2008;
28: 909–916.
11.
LuQObuchowskiNWonS, et al.
Using the optimal robust receiver operating characteristic (ROC) curve for predictive genetic tests.Biometrics2010;
66: 586–593.
