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Open Access Highly Accessed Open Badges Research article

Numbers needed to treat calculated from responder rates give a better indication of efficacy in osteoarthritis trials than mean pain scores

R Andrew Moore1*, Owen A Moore2, Sheena Derry1 and Henry J McQuay1

Author Affiliations

1 Pain Research and Nuffield Department of Anaesthetics, University of Oxford, Oxford Radcliffe Hospital, Oxford OX3 7LJ, UK

2 Department of Rheumatology, Musgrove Park Hospital, Stockman's Lane, Belfast BT0 7JB, UK

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Arthritis Research & Therapy 2008, 10:R39  doi:10.1186/ar2394

Published: 2 April 2008



Osteoarthritis trials usually report average changes in visual analogue scale (VAS) pain, and examine the difference between treatment and placebo. We investigated whether dichotomous responder analysis provides a more informative interpretation of drug efficacy.


Merck supplied the number of patients who, by 6 weeks, had achieved pain relief compared with a baseline of 0% or more, 10% or more, 20% or more, and so on at equal intervals up to 90% or more. These different levels of pain relief were used to distinguish different definitions of responders, for example at least 50% pain relief from baseline. Numbers and percentages of patients achieving each level were identified. Information was sought from a dose–response trial over 6 weeks in osteoarthritis using placebo and using etoricoxib at 5, 10, 30 and 60 mg daily.


With placebo, the proportions of patients achieving at least 20%, 50% and 70% pain relief over baseline at 6 weeks were 30%, 11% and 2%. With 60 mg etoricoxib the equivalent percentages were 74%, 49% and 29%. The numbers needed to treat for 30 mg and 60 mg etoricoxib to produce at least 50% pain relief at 6 weeks compared with placebo were 4.2 (95% confidence interval 3.8 to 8.6) and 2.6 (2.0 to 3.9), respectively. Levels of pain relief of 50% and above discriminated best between different doses of etoricoxib.


Responder analysis seemed to be more sensitive than examination of average changes in VAS pain scores. Validation would require calculations to be performed on a set of trials using individual patient data not available in publications.