A method of power assessment for tests comparing several treatments with a control
A method of power assessment for tests comparing several treatments with a control
A method of power assessment for the problem of comparing several treatments with a control is considered. Power assessment is based on the power function of a two-sided hypothesis test that none of the treatment is different from the control. Normally distributed data and binary response data are considered. Minimum power levels are found under certain easily interpretable range conditions on the treatment and control means or success probabilities. Expressions are provided allowing simple computer evaluation of minimum guaranteed power levels, and some illustrative tables of power levels are given.
normal data, bernoulli data, least favourable configuration, sample size determination, power function
1871-1889
Hayter, A.J.
55bd07a5-db1d-4d3d-8c87-b307485420d9
Liu, Wei
b64150aa-d935-4209-804d-24c1b97e024a
1992
Hayter, A.J.
55bd07a5-db1d-4d3d-8c87-b307485420d9
Liu, Wei
b64150aa-d935-4209-804d-24c1b97e024a
Hayter, A.J. and Liu, Wei
(1992)
A method of power assessment for tests comparing several treatments with a control.
Communications in Statistics: Theory and Methods, 21 (7), .
(doi:10.1080/03610929208830885).
Abstract
A method of power assessment for the problem of comparing several treatments with a control is considered. Power assessment is based on the power function of a two-sided hypothesis test that none of the treatment is different from the control. Normally distributed data and binary response data are considered. Minimum power levels are found under certain easily interpretable range conditions on the treatment and control means or success probabilities. Expressions are provided allowing simple computer evaluation of minimum guaranteed power levels, and some illustrative tables of power levels are given.
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Published date: 1992
Keywords:
normal data, bernoulli data, least favourable configuration, sample size determination, power function
Organisations:
Statistical Sciences Research Institute
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Local EPrints ID: 381072
URI: http://eprints.soton.ac.uk/id/eprint/381072
ISSN: 0361-0926
PURE UUID: 64886c9e-ee8a-4e89-ab3b-4cf1d11def4a
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Date deposited: 05 Oct 2015 11:09
Last modified: 15 Mar 2024 02:43
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Author:
A.J. Hayter
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