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Novel approaches for the analysis of SLE clinical trials








Novel approaches for the analysis of SLE clinical trials


Mimi Kim


Albert Einstein College of Medicine






13 August 2018


Lupus is a chronic autoimmune disease that is difficult to treat because the type and severity of symptoms that occur may vary considerably between patients and also within patients over time. Safer and more effective medications for lupus are urgently needed, but the fluctuating nature of the symptoms makes it very challenging for clinical trials to prove that experimental treatments are superior to standard therapies. As a result, many lupus trials in the past have failed, and it is often unclear whether this failure is due to the drug not working or because the study designs, outcome measures, and methods for analyzing the data were not able to detect the treatment signals in the midst of considerable disease heterogeneity and other sources of background “noise”. The primary objective of this project is to use existing data from completed randomized SLE trials to obtain a greater understanding of the impact of disease heterogeneity on trial results, and to devise better methodological strategies for addressing this issue in future studies. We will apply various statistical methods to investigate the magnitude of between- and within-patient variability in treatment response patterns, and evaluate if clinical and laboratory patient characteristics, such as exposure to specific standard of care therapies, predict the likelihood and duration of response. We will also explore novel methods that make more efficient use of clinical trial data in evaluating treatment effects. Our expectation is that more stringent endpoints and analytic approaches that maximize information about a patient's disease activity during follow up will better discriminate experimental treatments from control treatments. The long-term goal in accomplishing our study aims is to substantially improve the methodological aspects of lupus trials and ultimately accelerate the discovery and approval of effective new therapies.



[{ "PostingID": 1416, "Title": "GSK-HGS1006-C1056", "Description": "A Phase 3, Multi-Center, Randomized, Double-Blind, Placebo-Controlled, 76-Week Study to Evaluate the Efficacy and Safety of Belimumab (HGS1006, LymphoStat-B™), a Fully Human Monoclonal Anti-BLyS Antibody, in Subjects with Systemic Lupus Erythematosus (SLE)" },{ "PostingID": 1417, "Title": "GSK-HGS1006-C1057", "Description": "A Phase 3, Multi-Center, Randomized, Double-Blind, Placebo-Controlled, 52-Wk Study to Evaluate the Efficacy and Safety of Belimumab (HGS1006, LymphoStat-B™), a Fully Human Monoclonal Anti-BLyS Antibody, in Subjects With Systemic Lupus Erythematosus (SLE)" }]

Statistical Analysis Plan


Kim M, Pradhan K, Izmirly P, Kalunian K, Hanrahan L, Merrill J. Identifying Subgroups of SLE Patients with Differential Responses to a BLyS Inhibitor: Application of a Machine Learning Algorithm to Clinical Trial Data [abstract]. Arthritis Rheumatol. 2019; 71 (suppl 10).
https://acrabstracts.org/abstract/identifying-subgroups-of-sle-patients-with-differential-responses-to-a-blys-inhibitor-application-of-a-machine-learning-algorithm-to-clinical-trial-data/