Informing Response Criteria for Psoriatic Arthritis. I: Discrimination Models Based on Data from 3 Anti-Tumor Necrosis Factor Randomized Studies

Abstract

To develop statistical models, based on the analysis of data from phase III randomized placebo-controlled trials of tumor necrosis factor-alpha (TNF-alpha) inhibitors over a 24-week period, that may inform the definition of response measures for clinical trials in psoriatic arthritis (PsA). Data from phase III randomized controlled trials with anti-TNF agents were used. A training set using baseline and 24-week data from 2 trials was used to derive the models, which were then tested on a dataset using baseline and interim data from the third trial, and baseline and interim data from the first 2 trials. Logistic regression, tree analysis, and factor analysis were considered in the development of the models. Receiver-operating characteristic curves were constructed and area under the curve (AUC) calculated to assess performance of the models. Two models were derived. One was based on differences between baseline and last-visit values, which identified the current 68 tender joint count (TJC68), baseline and change in C-reactive protein (CRP), and the measure with the highest difference among the patient and physician global assessment of disease activity (GDA), patient assessment of pain and the Health Assessment Questionnaire (HAQ). The second model was based on percentage change from baseline and included TJC68, CRP, physician GDA, patient global assessment of arthritis pain, and HAQ. Both models provided high AUC of at least 0.8 for both the training and testing sets. Models for discriminating joint disease response patterns in PsA were derived from data from randomized controlled trials. These models can now be used to inform further consideration of response measures for trials.