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

Didden, EM; Ruffieux, Y; Hummel, N; Efthimiou, O; Reichenbach, S; Gsteiger, Sandro; Finckh, Axel; Fletcher, Christine; Salanti, G; Egger, Matthias; Work Package, IMI GetReal (2018). Prediction of Real-World Drug Effectiveness Prelaunch: Case Study in Rheumatoid Arthritis. Medical decision making, 38(6), pp. 719-729. Sage Publications 10.1177/0272989X18775975

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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.


To develop a method to predict drug effectiveness prelaunch and to apply it in a case study in rheumatoid arthritis (RA).


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.


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.


The approach performed well in the RA case study, but further work is required to better define its strengths and limitations.

Item Type:

Journal Article (Original Article)


04 Faculty of Medicine > Department of Dermatology, Urology, Rheumatology, Nephrology, Osteoporosis (DURN) > Clinic of Rheumatology, Clinical Immunology and Allergology
04 Faculty of Medicine > Pre-clinic Human Medicine > Institute of Social and Preventive Medicine (ISPM)

UniBE Contributor:

Didden, Eva-Maria, Ruffieux, Yann, Hummel, Noemi, Efthimiou, Orestis, Reichenbach, Stephan, Salanti, Georgia, Egger, Matthias


600 Technology > 610 Medicine & health
300 Social sciences, sociology & anthropology > 360 Social problems & social services




Sage Publications




Tanya Karrer

Date Deposited:

28 Aug 2018 12:43

Last Modified:

05 Dec 2022 15:17

Publisher DOI:


PubMed ID:


Uncontrolled Keywords:

effect modifier efficacy-effectiveness gap prediction model prognostic factor rheumatoid arthritis treatment predictor




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