The submission deadline for non-MEDDEK faculties has been extended to April 30, 2026. | Eingabefrist für nicht MEDDEK Fakultäten verlängert bis 30. April 2026.
Prediction of Real-World Drug Effectiveness Prelaunch: Case Study in Rheumatoid Arthritis.
Decision makers often need to assess the real-world effectiveness of new drugs prelaunch, when phase II/III randomized controlled trials (RCTs) but no other data are available.
OBJECTIVE
To develop a method to predict drug effectiveness prelaunch and to apply it in a case study in rheumatoid arthritis (RA).
METHODS
The approach 1) identifies a market-approved treatment ( S) currently used in a target population similar to that of the new drug ( N); 2) quantifies the impact of treatment, prognostic factors, and effect modifiers on clinical outcome; 3) determines the characteristics of patients likely to receive N in routine care; and 4) predicts treatment outcome in simulated patients with these characteristics. Sources of evidence include expert opinion, RCTs, and observational studies. The framework relies on generalized linear models.
RESULTS
The case study assessed the effectiveness of tocilizumab (TCZ), a biologic disease-modifying antirheumatic drug (DMARD), combined with conventional DMARDs, compared to conventional DMARDs alone. Rituximab (RTX) combined with conventional DMARDs was identified as treatment S. Individual participant data from 2 RCTs and 2 national registries were analyzed. The model predicted the 6-month changes in the Disease Activity Score 28 (DAS28) accurately: the mean change was -2.101 (standard deviation [SD] = 1.494) in the simulated patients receiving TCZ and conventional DMARDs compared to -1.873 (SD = 1.220) in retrospectively assessed observational data. It was -0.792 (SD = 1.499) in registry patients treated with conventional DMARDs.
CONCLUSION
The approach performed well in the RA case study, but further work is required to better define its strengths and limitations.
