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Assessing robustness of crossover designs to subjects dropping out.

Assessing robustness of crossover designs to subjects dropping out.
Assessing robustness of crossover designs to subjects dropping out.
In some crossover experiments, particularly in medical applications, subjects may fail to complete their sequences of treatments for reasons unconnected with the treatments received. A method is described of assessing the robustness of a planned crossover design, with more than two periods, to subjects leaving the study prematurely. The method involves computing measures of efficiency for every possible design that can result, and is therefore very computationally intensive. Summaries of these measures are used to choose between competing designs. The computational problem is reduced to a manageable size by a software implementation of Polya theory. The method is applied to comparing designs for crossover studies involving four treatments and four periods. Designs are identified that are more robust to subjects dropping out in the final period than those currently favoured in medical and clinical trials.
a-criterion, crossover experiments, dropout, missing value, mv-criterion, williams squares
0960-3174
219-227
Low, B.J.L.
e09e59f5-3863-4dc1-a2e9-16189258c3c4
Lewis, S.M.
a69a3245-8c19-41c6-bf46-0b3b02d83cb8
Prescott, P.
cf0adfdd-989b-4f15-9e60-ef85eed817b2
Low, B.J.L.
e09e59f5-3863-4dc1-a2e9-16189258c3c4
Lewis, S.M.
a69a3245-8c19-41c6-bf46-0b3b02d83cb8
Prescott, P.
cf0adfdd-989b-4f15-9e60-ef85eed817b2

Low, B.J.L., Lewis, S.M. and Prescott, P. (1999) Assessing robustness of crossover designs to subjects dropping out. Statistics and Computing, 9 (3), 219-227. (doi:10.1023/A:1008974031782).

Record type: Article

Abstract

In some crossover experiments, particularly in medical applications, subjects may fail to complete their sequences of treatments for reasons unconnected with the treatments received. A method is described of assessing the robustness of a planned crossover design, with more than two periods, to subjects leaving the study prematurely. The method involves computing measures of efficiency for every possible design that can result, and is therefore very computationally intensive. Summaries of these measures are used to choose between competing designs. The computational problem is reduced to a manageable size by a software implementation of Polya theory. The method is applied to comparing designs for crossover studies involving four treatments and four periods. Designs are identified that are more robust to subjects dropping out in the final period than those currently favoured in medical and clinical trials.

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Published date: 1999
Keywords: a-criterion, crossover experiments, dropout, missing value, mv-criterion, williams squares
Organisations: Statistics

Identifiers

Local EPrints ID: 30066
URI: http://eprints.soton.ac.uk/id/eprint/30066
ISSN: 0960-3174
PURE UUID: 446bf056-cfde-4b7c-b483-3a5c1d0ed8ab

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Date deposited: 15 Mar 2007
Last modified: 15 Jul 2019 19:07

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Contributors

Author: B.J.L. Low
Author: S.M. Lewis
Author: P. Prescott

University divisions

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