ePrints

Mixed-effects Gaussian process functional regression models with application to dose-response curve prediction

Lookup NU author(s): Dr Jian Shi, Dr Bo Wang

Downloads

Full text for this publication is not currently held within this repository. Alternative links are provided below where available.

Abstract

We propose a new semiparametric model for functional regression analysis, combining a parametric mixed-effects model with a nonparametric Gaussian process regression model, namely a mixed-effects Gaussian process functional regression model. The parametric component can provide explanatory information between the response and the covariates, whereas the nonparametric component can add nonlinearity. We can model the mean and covariance structures simultaneously, combining the information borrowed from other subjects with the information collected from each individual subject. We apply the model to doseresponse curves that describe changes in the responses of subjects for differing levels of the dose of a drug or agent and have a wide application in many areas. We illustrate the method for the management of renal anaemia. An individual doseresponse curve is improved when more information is included by this mechanism from the subject/patient over time, enabling a patient-specific treatment regime. Copyright (c) 2012 John Wiley & Sons, Ltd.

Publication metadata

Author(s): Shi JQ, Wang B, Will EJ, West RM

Publication type: Article

Publication status: Published

Journal: Statistics in Medicine

Year: 2012

Volume: 31

Issue: 26

Pages: 3165-3177

Print publication date: 02/08/2012

ISSN (print): 0277-6715

ISSN (electronic): 1097-0258

Publisher: John Wiley & Sons Ltd.

URL: http://dx.doi.org/10.1002/sim.4502

DOI: 10.1002/sim.4502

Altmetrics

Share