The detection of statistically significant reductions in radiographic progression during clinical studies in patients with rheumatoid arthritis (RA) has become increasingly difficult over the past decade due to early-escape study designs and declining rates of progression in control-group patients. We investigated the impact of extremes of radiographic data (outliers) and baseline prognostic factors on detection of treatment effects, to provide guidance on future analysis of joint structural data in RA clinical trials.
Data were from two, phase 3, randomized, double-blind, placebo-controlled trials of tofacitinib in adult patients with moderate to severe RA: ORAL Scan (NCT00847613) and ORAL Start (NCT01039688). These studies detected significant reductions in radiographic progression with tofacitinib 10 mg twice daily (BID) plus background methotrexate (ORAL Scan), and with tofacitinib 5 or 10 mg BID as monotherapy (ORAL Start). We evaluated mean changes from baseline in van der Heijde modified total Sharp score (mTSS) at month 6 and month 12, using analysis of covariance (ANCOVA). A trimmed analysis was used to deal with extremes of data. The impact of baseline prognostic factors on radiographic progression was evaluated using ANCOVA to analyze the mean change from baseline in mTSS for each factor in turn.
The analysis included data from 720 patients from ORAL Scan and 880 patients from ORAL Start. Trimmed analyses were unbiased for the true mean estimate and enabled us to remove the effect of influential extreme observations in the data set. Almost all patients had at least one poor prognostic factor at baseline (e.g., high level of disease activity, or positive for rheumatoid factor). The strongest predictor of treatment effect was the severity of radiographic damage at baseline.
Clinicaltrials.gov NCT00847613 (registered 17 February 2009) and NCT01039688 (registered 23 December 2009).
A trimmed analysis can establish whether any significant inhibition of structural damage is being driven by extremes of data, and should be one of the sensitivity analyses of choice for structural data in RA clinical trials. Furthermore, analysis of radiographic data based on baseline prognostic factors may reveal increased treatment effects. Application of these methods to analysis of radiographic data from clinical trials in patients with RA, allows a more complete interpretation of data.