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

Optimal designs for two-parameter nonlinear models with application to survival models
Optimal designs for two-parameter nonlinear models with application to survival models
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
c-optimality, d-optimality, proportional hazards, survival analysis
1017-0405
415-428
Konstantinou, Maria
242713ea-332c-466d-82e5-b2d6aa8dad07
Biedermann, Stefanie
fe3027d2-13c3-4d9a-bfef-bcc7c6415039
Kimber, Alan
40ba3a19-bbe3-47b6-9a8d-68ebf4cea774
Konstantinou, Maria
242713ea-332c-466d-82e5-b2d6aa8dad07
Biedermann, Stefanie
fe3027d2-13c3-4d9a-bfef-bcc7c6415039
Kimber, Alan
40ba3a19-bbe3-47b6-9a8d-68ebf4cea774

Konstantinou, Maria, Biedermann, Stefanie and Kimber, Alan (2014) Optimal designs for two-parameter nonlinear models with application to survival models. Statistica Sinica, 24 (1), 415-428. (doi:10.5705/ss.2011.271).

Record type: Article

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

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Published date: January 2014
Keywords: c-optimality, d-optimality, proportional hazards, survival analysis
Organisations: Statistics, Statistical Sciences Research Institute, Southampton Statistical Research Inst.

Identifiers

Local EPrints ID: 346869
URI: http://eprints.soton.ac.uk/id/eprint/346869
ISSN: 1017-0405
PURE UUID: 424b2c2e-19ab-40f2-8b18-408e4ec9a9c5
ORCID for Stefanie Biedermann: ORCID iD orcid.org/0000-0001-8900-8268

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Date deposited: 11 Jan 2013 15:22
Last modified: 15 Mar 2024 03:26

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Contributors

Author: Maria Konstantinou
Author: Alan Kimber

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