Split-plot designs: a useful tool for the statistical analysis of real medical data and repeated measurement problems
Split-plot designs: a useful tool for the statistical analysis of real medical data and repeated measurement problems
In this article, a split-plot design is applied for the collection of real medical data concerning biochemical and vital measurements of 283 patients in Intensive Care Unit, on a repeated measurement problem. Practical limitations and issues related to cost reveals frequently the practical usefulness of split-plot designs. When an experiment is planned, factors that are difficult or time consuming to manipulate appear and make complete randomization impractical. A split-plot structure is used when it is impractical to change the levels of some of the experimental factors. Our dataset, selected with the aid of a split-plot design, consists of 2256 observations 17 factors and 8 repetitions. A statistical analysis on the resulting data is performed and useful biological justification of our results is provided.
61-66
Koukouvinos, C.
3c626a53-575f-4c62-9b9a-a949f717764b
Lygkoni, E.
dddc0cfa-6ae3-416b-a52a-cb17944124ed
Mylona, K.
b44af287-2d9f-4df8-931c-32d8ab117864
2009
Koukouvinos, C.
3c626a53-575f-4c62-9b9a-a949f717764b
Lygkoni, E.
dddc0cfa-6ae3-416b-a52a-cb17944124ed
Mylona, K.
b44af287-2d9f-4df8-931c-32d8ab117864
Koukouvinos, C., Lygkoni, E. and Mylona, K.
(2009)
Split-plot designs: a useful tool for the statistical analysis of real medical data and repeated measurement problems.
Bulletin of Statistics & Economics, 3 (S09), Spring Issue, .
Abstract
In this article, a split-plot design is applied for the collection of real medical data concerning biochemical and vital measurements of 283 patients in Intensive Care Unit, on a repeated measurement problem. Practical limitations and issues related to cost reveals frequently the practical usefulness of split-plot designs. When an experiment is planned, factors that are difficult or time consuming to manipulate appear and make complete randomization impractical. A split-plot structure is used when it is impractical to change the levels of some of the experimental factors. Our dataset, selected with the aid of a split-plot design, consists of 2256 observations 17 factors and 8 repetitions. A statistical analysis on the resulting data is performed and useful biological justification of our results is provided.
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Published date: 2009
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Statistics
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Local EPrints ID: 336716
URI: http://eprints.soton.ac.uk/id/eprint/336716
ISSN: 0973-7022
PURE UUID: d5fe37ce-8298-49b9-a01f-8aba92f6ca73
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Date deposited: 04 Apr 2012 14:19
Last modified: 11 Dec 2021 00:04
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Author:
C. Koukouvinos
Author:
E. Lygkoni
Author:
K. Mylona
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