Receiver Operating Characteristic (ROC) Curves in their New Habitats - Albert Vexler

DCEG Events

March 20, 2019 | 12:00 AM – 12:00 AM

NCI Shady Grove Rockville, MD

BIOSTATISTICS BRANCH SEMINAR SERIES PRESENTS

Speaker

Albert Vexler, Ph.D.
Professor, Department of Biostatistics, School of Public Health and Health Professions
The State University of New York at Buffalo

Abstract

Receiver operating characteristic (ROC) curve based techniques are well-established tools in biostatistics for evaluating the ability of biomarkers to discriminate between populations.
The authors consider how ROC curve based organisms can reside in areas of p-values, statistical tests’ comparisons, compound test-statistics’ developments and measures of dependence between random variables with depicting complex structures of variables’ associations. Naturalizing ROC curve-type entities, the authors assemble novel, correct and efficient mechanisms for testing and measuring procedures in various statistical frameworks.

Keywords

AUC; Benjamini-Hochberg procedure; Best combination; Bonferroni procedure; Expected p-value; Multiple testing; P-value; Partial AUC; Partial expected p-Value; Power; ROC curve;. t-Test; Wilcoxon test;. Youden’s Index, Axioms for measuring dependence; Dependence measure; Kendall distribution function; Kendall's pho; Kendall plot, Nonparametric Association; Positive/Negative dependence.

**The mission of the Biostatistics Branch (BB) is to be an outstanding biostatistics unit that can contribute to the understanding of cancer etiology and to improve public health by the development and application of quantitative methods. The BB Investigators develop statistical methods and data resources to strengthen observational studies, intervention trials, and laboratory investigations of cancer.**