New Study Finds Way to Improve Lupus Clinical Trials Treatment Research
New research finds that applying a multiple imputation (MI) statistical analysis method may be advantageous in clinical trials for lupus in which data are missing. The authors propose this method may both reduce study bias in treatment effect estimates and improve measures of precision to properly reflect uncertainty in studies where information is missing.
Gaps in data pose a frequent and significant challenge in clinical trials assessing new lupus treatments, and current statistical approaches often introduce bias and reduce precision in study results. These challenges then render the trial inconclusive or impede its completion altogether. Missing data may be due to participant dropout, loss to follow-up, skipped visits, and other factors.
Mimi Kim, Sc.D., lead author, Lupus Foundation of America (LFA) grantee and member of the LFA’s Medical-Scientific Advisory Committee, adds, “To increase the ability to discover effective new therapies for lupus, we need to continue to investigate more powerful study designs and analytic methods to address the many challenges that can arise in lupus clinical trials. These challenges include the heterogeneity in type and severity of symptoms in the study population, use of concomitant medications, non-adherence to assigned therapy, patient drop-out, and missing data.”
While people are living longer and better lives with lupus than ever before, there is still much to learn about the disease and its effective treatments. That’s why clinical trials and strong data are so important. Learn more about Dr. Kim’s work with the LFA through the Collective Data Analysis Initiative.