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Clinical trials and drug discovery
Original research
SLE clinical trials: impact of missing data on estimating treatment effects
  1. Mimi Kim1,
  2. Joan T Merrill2,
  3. Cuiling Wang1,
  4. Shankar Viswanathan1,
  5. Ken Kalunian3,
  6. Leslie Hanrahan4 and
  7. Peter Izmirly5
  1. 1Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, USA
  2. 2Arthritis & Clinical Immunology Program,Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, USA
  3. 3Rheumatology, UCSD, San Diego, California, USA
  4. 4Research and Education, Lupus Foundation of America, Washington, District of Columbia, USA
  5. 5Medicine, Division of Rheumatology, New York University School of Medicine, New York City, New York, USA
  1. Correspondence to Dr Mimi Kim; mimi.kim{at}einstein.yu.edu

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.

  • Systemic lupus erythematosus
  • clinical trial
  • missing data

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/lupus-2019-000348

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    Footnotes

    • Contributors MK, JTM, CW, SV, KK and PI contributed to the study design, statistical analysis and interpretation of results. LH contributed to the acquisition of data. All authors reviewed and approved the final manuscript.

    • Funding This work was supported by a grant from the Lupus Foundation of America.

    • Competing interests JTM and KK received consulting fees from Eli Lilly. MK received consulting fees from Celgene.

    • Patient consent for publication Not required.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Data availability statement Data may be obtained from a third party and are not publicly available.