Impact of informative censoring on the treatment effect estimate of disability worsening in multiple sclerosis clinical trials

Objective: To examine the impact of missing data when evaluating the confirmed disability worsening (CDW) endpoint in multiple sclerosis clinical trials and explore analytical methods for handling censored participants (those with missing confirmation data).

Methods: CDW risk factors were assessed among participants with an initial disability worsening (≥ 1.0-point increase in Expanded Disability Status Scale [EDSS] score from a baseline score of ≥ 1.0; ≥ 1.5-point increase from a baseline of 0) using data from the DECIDE trial of daclizumab beta. A post-hoc simulation study was performed to evaluate three strategies for imputing confirmation status in censored participants: assume all were confirmed; assume none were confirmed (standard analytical approach); or use an observed rate multiple imputation (ORMI) approach based on treatment group and similar participant risk factors. Simulation study results were used to evaluate pre-specified analyses in DECIDE.

Results: In DECIDE, larger change from baseline to initial disability worsening in EDSS score (p = 0.0003), higher baseline EDSS score (p = 0.0013), age (p = 0.004), and preceding relapse (p < 0.0001) were associated with 12-week CDW. In the simulation study, relative to the full dataset (no missing data), the strategy of assuming no censored participants were confirmed underestimated the treatment effect, and the strategy of assuming all censored participants were confirmed overestimated the treatment effect (hazard ratio 0.749 and 0.713 vs 0.733). ORMI correctly estimated treatment effect and increased study power by ~5-10% compared with the standard analytical approach.

Conclusion: The ORMI approach based on CDW risk factors minimizes bias and is expected to provide the most accurate treatment effect estimate for the CDW endpoint.