PMID- 26010331 OWN - NLM STAT- MEDLINE DCOM- 20171106 LR - 20181202 IS - 1520-5711 (Electronic) IS - 1054-3406 (Linking) VI - 26 IP - 3 DP - 2016 TI - Binary classification using multivariate receiver operating characteristic curve for continuous data. PG - 421-31 LID - 10.1080/10543406.2015.1052479 [doi] AB - The classification scenario needs handling of more than one biomarker. The main objective of the work is to propose a multivariate receiver operating characteristic (MROC) model which linearly combines the markers to classify them into one of the two groups and also to determine an optimal cut point. Simulation studies are conducted for four sets of mean vectors and covariance matrices and a real dataset is also used to demonstrate the proposed model. Linear and quadratic discriminant analysis has also been applied to the above datasets in order to explain the ease of the proposed model. Bootstrapped estimates of the parameters of the ROC curve are also estimated. FAU - Sameera, G AU - Sameera G AD - a Department of Statistics , Pondicherry University , Pondicherry , India. FAU - Vardhan, R Vishnu AU - Vardhan RV AD - a Department of Statistics , Pondicherry University , Pondicherry , India. FAU - Sarma, K V S AU - Sarma KV AD - b Department of Statistics , Sri Venkateswara University , Tirupati , India. LA - eng PT - Journal Article DEP - 20150526 PL - England TA - J Biopharm Stat JT - Journal of biopharmaceutical statistics JID - 9200436 RN - 0 (Biomarkers) SB - IM MH - Biomarkers/*analysis MH - Humans MH - Liver Diseases/diagnosis MH - Models, Statistical MH - *ROC Curve OTO - NOTNLM OT - AUC OT - MROC OT - Optimal cutoff EDAT- 2015/05/27 06:00 MHDA- 2017/11/07 06:00 CRDT- 2015/05/27 06:00 PHST- 2015/05/27 06:00 [entrez] PHST- 2015/05/27 06:00 [pubmed] PHST- 2017/11/07 06:00 [medline] AID - 10.1080/10543406.2015.1052479 [doi] PST - ppublish SO - J Biopharm Stat. 2016;26(3):421-31. doi: 10.1080/10543406.2015.1052479. Epub 2015 May 26.