Prediction of Real-World Drug Effectiveness Prelaunch: Case Study in Rheumatoid Arthritis

Abstract

Background: 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.

Keywords: effect modifier; efficacy-effectiveness gap; prediction model; prognostic factor; rheumatoid arthritis; treatment predictor.