Model robust designs for survival trials

Maria Konstantinou, Stefanie Biedermann and Alan Kimber

Computational Statistics & Data Analysis, 2017, vol. 113, issue C, 239-250

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.

Keywords: Proportional hazards models; Minimax optimal designs; D-optimality; c-optimality; Type-I censoring (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947316302407
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:113:y:2017:i:c:p:239-250

DOI: 10.1016/j.csda.2016.10.013

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().