Skip to main navigation Skip to main content
The University of Southampton
Southampton Statistical Sciences Research Institute

S3RI Seminar - Bayesian nonparametric inference for the covariate-adjusted ROC curve, Dr Vanda Inacio De Carvalho (University of Edinburgh) Seminar

Origin:
Mathematical Sciences
S3RI Seminar
Time:
14:00 - 15:00
Date:
26 April 2018
Venue:
Room 8031, Lecture Theatre 8C, Building 54, Mathematical Sciences, University of Southampton, Highfield Campus, SO17 1BJ

For more information regarding this seminar, please email Dr Helen Ogden at H.E.Ogden@southampton.ac.uk .

Event details

Accurate diagnosis of disease is of fundamental importance in clinical practice and medical research. Before a medical diagnostic test is routinely used in practice, its ability to distinguish between diseased and nondiseased states must be rigorously assessed through statistical analysis. The receiver operating characteristic (ROC) curve is the most popular used tool for evaluating the discriminatory ability of continuous-outcome diagnostic tests. It has been acknowledged that several factors (e.g., subject-specific characteristics, such as age and/or gender) can affect the test's accuracy beyond disease status. Recently, the covariate-adjusted ROC curve has been proposed and successfully applied as a global summary measure of diagnostic accuracy that takes covariate information into account. We develop a highly flexible nonparametric model for the covariate-adjusted ROC curve, based on a combination of a B-splines dependent Dirichlet process mixture model and the Bayesian bootstrap, that can respond to unanticipated features of the data (e.g., nonlinearities, skewness, multimodality, and/or excess of variability). Multiple simulation studies demonstrate the ability of our model to successfully recover the true covariate-adjusted ROC curve and to produce valid inferences in a variety of complex scenarios. Our methods are motivated by and applied to an endocrine dataset where the main goal is to assess the accuracy of the body mass index, adjusted for age and gender, for predicting clusters of cardiovascular disease risk factors.

The seminar will also be available via a live web-cast at
https://cours ecast.soton.ac. uk/Panopto/Page s/Viewer.aspx?i d=5cf86a96-7216 -4c95-9793-c909 9c36ff41

Speaker information

Dr Vanda Inacio De Carvalho , University of Edinburgh. Research interests: Bayesian (nonparametric) statistics, Biostatistics, Functional data, Statistical Computing

Privacy Settings