Using disease progression models as a tool to detect drug effect

Mould, D. R., Denman, N. G. and Duffull, S. (2007) Using disease progression models as a tool to detect drug effect. Clinical Pharmacology and Therapeutics, 82 1: 81-86. doi:10.1038/sj.clpt.6100228


Author Mould, D. R.
Denman, N. G.
Duffull, S.
Title Using disease progression models as a tool to detect drug effect
Journal name Clinical Pharmacology and Therapeutics   Check publisher's open access policy
ISSN 0009-9236
1532-6535
Publication date 2007
Sub-type Article (original research)
DOI 10.1038/sj.clpt.6100228
Volume 82
Issue 1
Start page 81
End page 86
Total pages 6
Place of publication New York, United States
Publisher Nature Publishing Group
Collection year 2008
Language eng
Subject C1
Abstract Generally, information required for approval of new drugs is dichotomous in that the drug is either efficacious and safe or not. Consequently, the purpose of most confirmatory clinical trials is to test the null hypothesis. The primary reasons for designing hypothesis testing trials are to provide the information required for approval using analyses techniques that are relatively straightforward and free of apparent assumptions. However, the information required for approval is very different from that used by prescribers for decision making. In the clinic, decisions must be made about dose adjustment for individual patients in the presence of additional therapies and co-morbidities. Choice of drug and dosing regimen is therefore a classical risk to benefit decision that is often poorly informed from the results of confirmatory trials. Therefore, providing answers to the more difficult question of how to use the drug in a clinical setting is essential.
Keyword Labeling decisions
Clinical-trials
Time-course
Approval
Impact
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: 2008 Higher Education Research Data Collection
School of Pharmacy Publications
 
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Citation counts: TR Web of Science Citation Count  Cited 24 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 24 times in Scopus Article | Citations
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Created: Mon, 18 Feb 2008, 17:31:24 EST