Relapse–remission and remission–relapse switches in rheumatoid arthritis patients were modeled by random effects

Abstract

Objective To compare statistical models for the analysis of two-state disease processes. Study Design and Setting A two-armed randomized trial of patients with early rheumatoid arthritis (RA) treated by either combination therapy (sulfasazaline, methotrextate, prednisolone) or monotherapy (sulfasazaline). Disease activity (remission or relapse) was analyzed with the logistic regression model, the proportional hazards regression model, and the continuous-time Markov process model for panel data. The dependence among the switching times was studied by (1) including correlated normal random patient effects for the relapse–remission and remission–relapse switching probabilities; (2) assuming the population to be a mixture of patients responsive and nonresponsive to therapy; (3) including separate parameters for the first and subsequent relapse–remission switch; and (4) combining (1) and (3). The four approaches were compared using parametric bootstrap checks. Results The logistic regression model, the proportional hazards regression model, and the continuous-time Markov process model for panel data yielded similar combination therapy effects. The inclusion of random patient effects (approaches 1 and 4) gave the best fit to the observed disease activity pattern. Conclusion Models with correlated random effects can provide a satisfactory fit to two-state disease patterns.