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Using short-term evidence to predict six-month outcomes in clinical trials of signs and symptoms in rheumatoid arthritis
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Nixon, R. M., Bansback, N., Stevens, J. W., Brennan, A. and Madan, Jason (2009) Using short-term evidence to predict six-month outcomes in clinical trials of signs and symptoms in rheumatoid arthritis. Pharmaceutical Statistics, 8 (2). pp. 150-162. doi:10.1002/pst.351 ISSN 1539-1604.
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Official URL: https://doi.org/10.1002/pst.351
Abstract
A model is presented to generate a distribution for the probability of an ACR response at six months for a new treatment for rheumatoid arthritis given evidence from a one- or three-month clinical trial. The model is based on published evidence from 11 randomized controlled trials on existing treatments. A hierarchical logistic regression model is used to find the relationship between the proportion of patients achieving ACR20 and ACR50 at one and three months and the proportion at six months. The model is assessed by Bayesian predictive P-values that demonstrate that the model fits the data well. The model can be used to predict the number of patients with an ACR response for proposed six-month clinical trials given data from clinical trials of one or three months duration.
Item Type: | Journal Article | ||||||
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Subjects: | R Medicine > R Medicine (General) | ||||||
Divisions: | Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School > Clinical Trials Unit Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School > Health Sciences Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School |
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Journal or Publication Title: | Pharmaceutical Statistics | ||||||
Publisher: | Wiley-Blackwell | ||||||
ISSN: | 1539-1604 | ||||||
Official Date: | April 2009 | ||||||
Dates: |
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Volume: | 8 | ||||||
Number: | 2 | ||||||
Page Range: | pp. 150-162 | ||||||
DOI: | 10.1002/pst.351 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Restricted or Subscription Access |
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