Optimal designs for two-parameter nonlinear models with application to survival models


Konstantinou, Maria, Biedermann, Stefanie and Kimber, Alan (2011) Optimal designs for two-parameter nonlinear models with application to survival models. , Southampton Statistical Research Institute, University of Southampton, 23pp. (S3RI Working Paper, M11/08).

WarningThere is a more recent version of this item available.

Download

[img] PDF
Download (583Kb)

Description/Abstract

Censoring may occur in many industrial or biomedical time to event experiments. Efficient designs for such experiments are needed but finding such designs can be problematic since the statistical models involved will usually be nonlinear, making the optimal choice of design parameter dependent. We provide analytical characterisations of locally D- and c-optimal designs for a large class of models. Our results are illustrated using the natural proportional hazards parameterisation of the exponential regression model, thus reducing the numerical effort for design search substantially. We also determine designs based on standardised optimality criteria when a range of parameter values is provided by the experimenter. Different
censoring mechanisms are incorporated and the robustness of designs to parameter misspecification is assessed. We demonstrate that, unlike traditional designs, the designs found perform well across a broad range of scenarios.

Item Type: Monograph (Working Paper)
Keywords: c-optimality, D-optimality, proportional hazards, survival analysis. Running head: Optimal designs for two-parameter nonlinear models with application to survival models
Subjects: H Social Sciences > HA Statistics
Divisions: University Structure - Pre August 2011 > Southampton Statistical Sciences Research Institute
ePrint ID: 187821
Date Deposited: 18 May 2011 16:22
Last Modified: 27 Mar 2014 19:41
Publisher: Southampton Statistical Research Institute, University of Southampton
URI: http://eprints.soton.ac.uk/id/eprint/187821

Available Versions of this Item

Actions (login required)

View Item View Item