Being Transparent About Brilliant Failures: An Attempt to Use Real-World Data in a Disease Model for Patients with Castration-Resistant Prostate Cancer.

Real-world disease models spanning multiple treatment lines can provide insight into the (cost) effectiveness of treatment sequences in clinical practice.

Our objective was to explore whether a disease model based solely on real-world data (RWD) could be used to estimate the effectiveness of treatments for patients with castration-resistant prostate cancer (CRPC) that could then be suitably used in a cost-effectiveness analysis.

We developed a patient-level simulation model using patient-level data from the Dutch CAPRI registry as input parameters. Time to event (TTE) and overall survival (OS) were estimated with multivariate regression models, and type of event (i.e., next treatment or death) was estimated with multivariate logistic regression models. To test internal validity, TTE and OS from the simulation model were compared with the observed outcomes in the registry.

Although patient characteristics and survival outcomes of the simulated data were comparable to those in the observed data (median OS 20.6 vs. 19.8 months, respectively), the disease model was less accurate in estimating differences between treatments (median OS simulated vs. observed population: 18.6 vs. 17.9 [abiraterone acetate plus prednisone], 24.0 vs. 25.0 [enzalutamide], 20.2 vs. 18.7 [docetaxel], and 20.0 vs. 23.8 months [radium-223]).

Overall, the disease model accurately approximated the observed data in the total CRPC population. However, the disease model was unable to predict differences in survival between treatments due to unobserved differences. Therefore, the model is not suitable for cost-effectiveness analysis of CRPC treatment. Using a combination of RWD and data from randomised controlled trials to estimate treatment effectiveness may improve the model.

Drugs - real world outcomes. 2022 Mar 21 [Epub ahead of print]

Marscha S Holleman, Simone A Huygens, Maiwenn J Al, Malou C P Kuppen, Hans M Westgeest, Alfonsus C M van den Bergh, Andries M Bergman, Alfonsus J M van den Eertwegh, Mathijs P Hendriks, Menuhin I Lampe, Niven Mehra, Reindert J A van Moorselaar, Inge M van Oort, Diederik M Somford, Ronald de Wit, Agnes J van de Wouw, Winald R Gerritsen, Carin A Uyl-de Groot

Erasmus School of Health Policy and Management, Erasmus University Rotterdam, Rotterdam, The Netherlands. ., Institute for Medical Technology Assessment, Erasmus University Rotterdam, Rotterdam, The Netherlands., Erasmus School of Health Policy and Management, Erasmus University Rotterdam, Rotterdam, The Netherlands., Department of Radiation Oncology, Maastro, Maastricht, The Netherlands., Department of Internal Medicine, Amphia Hospital, Breda, The Netherlands., Department of Radiation Oncology, University Medical Centre, Groningen, The Netherlands., Division of Internal Medicine (MOD) and Oncogenomics, The Netherlands Cancer Institute Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands., Department of Medical Oncology, Cancer Centre Amsterdam, Amsterdam University Medical Centre, VU Amsterdam, Amsterdam, The Netherlands., Department of Medical Oncology, Northwest Clinics, Alkmaar, The Netherlands., Department of Urology, Medical Centre, Leeuwarden, The Netherlands., Department of Medical Oncology, Radboud University Medical Centre, Nijmegen, The Netherlands., Department of Urology, VU University Medical Centre, Amsterdam, The Netherlands., Department of Urology, Radboud University Medical Centre, Nijmegen, The Netherlands., Department of Urology, Canisius-Wilhelmina Hospital, Nijmegen, The Netherlands., Department of Medical Oncology, Erasmus Medical Centre, Rotterdam, The Netherlands., Department of Medical Oncology, VieCuri Medical Centre, Venlo, The Netherlands.

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