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-List Of Titles -Optimal design of clinical trials comparing several treatments with a control

Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.14/106266

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Title
Optimal design of clinical trials comparing several treatments with a control
Related
Pharmaceutical statistics, Vol. 6, No. 1 (2007), p.23-33
DOI
10.1002/pst.240
Publisher
John Wiley & Sons
Date
2007
Author/Creator
Marschner, Ian C
Description
Clinical trials are often designed to compare several treatments with a common control arm in pairwise fashion. In this paper we study optimal designs for such studies, based on minimizing the total number of patients required to achieve a given level of power. A common approach when designing studies to compare several treatments with a control is to achieve the desired power for each individual pairwise treatment comparison. However, it is often more appropriate to characterize power in terms of the family of null hypotheses being tested, and to control the probability of rejecting all, or alternatively any, of these individual hypotheses. While all approaches lead to unbalanced designs with more patients allocated to the control arm, it is found that the optimal design and required number of patients can vary substantially depending on the chosen characterization of power. The methods make allowance for both continuous and binary outcomes and are illustrated with reference to two clinical trials, one involving multiple doses compared to placebo and the other involving combination therapy compared to mono-therapies. In one example a 55% reduction in sample size is achieved through an optimal design combined with the appropriate characterization of power.
Description
11 page(s)
Subject Keyword
clinical trials
Subject Keyword
combination therapy
Subject Keyword
multiple comparisons
Subject Keyword
optimal design
Subject Keyword
unbalanced allocation
Subject Keyword
power
Resource Type
journal article
Organisation
Macquarie University. Dept. of Statistics

Identifier
http://hdl.handle.net/1959.14/106266
Identifier
ISSN:1539-1604
Identifier
mq-rm-2010000381
Language
eng
Reviewed
Reviewed
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Citation Format
E-mail Address
Subject
"Pharmaceutical statistics"
 
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