SLE clinical trials: impact of missing data on estimating treatment effects

Mimi Kim; Joan T Merrill; Cuiling Wang; Shankar Viswanathan; Ken Kalunian; Leslie Hanrahan; Peter Izmirly

doi:10.1136/lupus-2019-000348

SLE clinical trials: impact of missing data on estimating treatment effects

Lupus Sci Med. 2019 Oct 3;6(1):e000348. doi: 10.1136/lupus-2019-000348. eCollection 2019.

Authors

Mimi Kim¹, Joan T Merrill², Cuiling Wang¹, Shankar Viswanathan¹, Ken Kalunian³, Leslie Hanrahan⁴, Peter Izmirly⁵

Affiliations

¹ Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, USA.
² Arthritis & Clinical Immunology Program, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, USA.
³ Rheumatology, UCSD, San Diego, California, USA.
⁴ Research and Education, Lupus Foundation of America, Washington, District of Columbia, USA.
⁵ Medicine, Division of Rheumatology, New York University School of Medicine, New York City, New York, USA.

Abstract

Objective: A common problem in clinical trials is missing data due to participant dropout and loss to follow-up, an issue which continues to receive considerable attention in the clinical research community. Our objective was to examine and compare current and alternative methods for handling missing data in SLE trials with a particular focus on multiple imputation, a flexible technique that has been applied in different disease settings but not to address missing data in the primary outcome of an SLE trial.

Methods: Data on 279 patients with SLE randomised to standard of care (SoC) and also receiving mycophenolate mofetil (MMF), azathioprine or methotrexate were obtained from the Lupus Foundation of America-Collective Data Analysis Initiative Database. Complete case analysis (CC), last observation carried forward (LOCF), non-responder imputation (NRI) and multiple imputation (MI) were applied to handle missing data in an analysis to assess differences in SLE Responder Index-5 (SRI-5) response rates at 52 weeks between patients on SoC treated with MMF versus other immunosuppressants (non-MMF).

Results: The rates of missing data were 32% in the MMF and 23% in the non-MMF groups. As expected, the NRI missing data approach yielded the lowest estimated response rates. The smallest and least significant estimates of differences between groups were observed with LOCF, and precision was lowest with the CC method. Estimated between-group differences were magnified with the MI approach, and imputing SRI-5 directly versus deriving SRI-5 after separately imputing its individual components yielded similar results.

Conclusion: The potential advantages of applying MI to address missing data in an SLE trial include reduced bias when estimating treatment effects, and measures of precision that properly reflect uncertainty in the imputations. However, results can vary depending on the imputation model used, and the underlying assumptions should be plausible. Sensitivity analysis should be conducted to demonstrate robustness of results, especially when missing data proportions are high.

Keywords: Systemic lupus erythematosus; clinical trial; missing data.