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Model robust designs for survival trials

Model robust designs for survival trials
Model robust designs for survival trials
The exponential-based proportional hazards model is often assumed in time-to-event experiments but may only approximately hold. Deviations in different neighbourhoods of this model are considered that include other widely used parametric proportional hazards models and the data are assumed to be subject to censoring. Minimax designs are then found explicitly, based on criteria corresponding to classical c- and D-optimality. Analytical characterisations of optimal designs are provided which, unlike optimal designs for related problems in the literature, have finite support and thus avoid the issues of implementing a density-based design in practice. Finally, the proposed designs are compared with the balanced design that is traditionally used in practice, and recommendations for practitioners are given.
0167-9473
239-250
Konstantinou, Maria
5ccc65a5-c4d0-4781-97fa-e1cf33fb0d32
Biedermann, Stefanie
fe3027d2-13c3-4d9a-bfef-bcc7c6415039
Kimber, Alan
40ba3a19-bbe3-47b6-9a8d-68ebf4cea774
Konstantinou, Maria
5ccc65a5-c4d0-4781-97fa-e1cf33fb0d32
Biedermann, Stefanie
fe3027d2-13c3-4d9a-bfef-bcc7c6415039
Kimber, Alan
40ba3a19-bbe3-47b6-9a8d-68ebf4cea774

Konstantinou, Maria, Biedermann, Stefanie and Kimber, Alan (2017) Model robust designs for survival trials. Computational Statistics and Data Analysis, 113, 239-250. (doi:10.1016/j.csda.2016.10.013).

Record type: Article

Abstract

The exponential-based proportional hazards model is often assumed in time-to-event experiments but may only approximately hold. Deviations in different neighbourhoods of this model are considered that include other widely used parametric proportional hazards models and the data are assumed to be subject to censoring. Minimax designs are then found explicitly, based on criteria corresponding to classical c- and D-optimality. Analytical characterisations of optimal designs are provided which, unlike optimal designs for related problems in the literature, have finite support and thus avoid the issues of implementing a density-based design in practice. Finally, the proposed designs are compared with the balanced design that is traditionally used in practice, and recommendations for practitioners are given.

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Accepted/In Press date: 14 October 2016
e-pub ahead of print date: 24 October 2016
Published date: September 2017
Organisations: Statistics

Identifiers

Local EPrints ID: 388537
URI: http://eprints.soton.ac.uk/id/eprint/388537
ISSN: 0167-9473
PURE UUID: f0001c32-7317-44a6-877e-5d487bded98a
ORCID for Stefanie Biedermann: ORCID iD orcid.org/0000-0001-8900-8268

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Date deposited: 29 Feb 2016 10:40
Last modified: 28 Apr 2022 04:21

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Contributors

Author: Maria Konstantinou
Author: Alan Kimber

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