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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

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Official URL: https://doi.org/10.1002/pst.351

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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
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
Journal or Publication Title: Pharmaceutical Statistics
Publisher: Wiley-Blackwell
ISSN: 1539-1604
Official Date: April 2009
Dates:
DateEvent
April 2009Published
23 October 2008Accepted
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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