Presentation and analysis of data on radiographic outcome in clinical trials -: Experience from the TEMPO study

Objective. To evaluate different methods of presentation and analysis of radiographic data in a rheumatoid arthritis (RA) randomized controlled trial. Methods. A double-blind randomized controlled trial including 682 patients with active RA who were treated with methotrexate, etanercept, or a combination of the 2 drugs was used for this study. Probability plots of the change from baseline to year I were produced to visualize progression, and were compared with usual descriptive statistics. The primary analysis of the trial (based on annualized actual mean change from baseline in total Sharp score at I year, using linear imputation) was challenged using various ways of handling missing information with alternative imputation methods, and by various statistical analyses including analysis of covariance (ANCOVA) and mixed model analysis on both raw and log-transformed data. Results. Probability plots provided detailed insight into the differentiated treatment effects between the 3 arms of this study. As adjuncts to formal hypothesis testing, these plots were more useful for presenting data than were summary descriptive statistics or use of preset cutoff points to define lack of progression. Additional analyses presented here support the results obtained with the per-protocol analysis that showed an advantage of the combination treatment compared with the monotherapy arms and for etanercept versus methotrexate alone. Various ways of handling missing information confirmed the robustness of the results. In addition, both ANCOVA and mixed model analyses on raw and on log-transformed data produced similar results. Conclusion. We suggest a panel of alternative analysis methods and alternative ways of handling missing information to verify that the radiographic results reported in an randomized controlled trial are not influenced by technical factors, such as interpolation, handling of missing data, and choice of statistical tests.