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Using short‐term evidence to predict six‐month outcomes in clinical trials of signs and symptoms in rheumatoid arthritis
Author(s) -
Nixon Richard M.,
Bansback Nick,
Stevens John W.,
Brennan Alan,
Madan Jason
Publication year - 2008
Publication title -
pharmaceutical statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 38
eISSN - 1539-1612
pISSN - 1539-1604
DOI - 10.1002/pst.351
Subject(s) - rheumatoid arthritis , medicine , clinical trial , logistic regression , bayesian probability , randomized controlled trial , physical therapy , statistics , mathematics
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. Copyright © 2008 John Wiley & Sons, Ltd.