European Urology Open Science (May 2024)
Research Protocol for an Observational Health Data Analysis on the Adverse Events of Systemic Treatment in Patients with Metastatic Hormone-sensitive Prostate Cancer: Big Data Analytics Using the PIONEER Platform
- Pawel Rajwa,
- Angelika Borkowetz,
- Thomas Abbott,
- Andrea Alberti,
- Anders Bjartell,
- James T. Brash,
- Riccardo Campi,
- Andrew Chilelli,
- Mitchell Conover,
- Niculae Constantinovici,
- Eleanor Davies,
- Bertrand De Meulder,
- Sherrine Eid,
- Mauro Gacci,
- Asieh Golozar,
- Haroon Hafeez,
- Samiul Haque,
- Ayman Hijazy,
- Tim Hulsen,
- Andreas Josefsson,
- Sara Khalid,
- Raivo Kolde,
- Daniel Kotik,
- Samu Kurki,
- Mark Lambrecht,
- Chi-Ho Leung,
- Julia Moreno,
- Rossella Nicoletti,
- Daan Nieboer,
- Marek Oja,
- Soundarya Palanisamy,
- Peter Prinsen,
- Christian Reich,
- Giulio Raffaele Resta,
- Maria J. Ribal,
- Juan Gómez Rivas,
- Emma Smith,
- Robert Snijder,
- Carl Steinbeisser,
- Frederik Vandenberghe,
- Philip Cornford,
- Susan Evans-Axelsson,
- James N'Dow,
- Peter-Paul M. Willemse
Affiliations
- Pawel Rajwa
- Department of Urology, Medical University of Silesia, Zabrze, Poland; Department of Urology, Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria
- Angelika Borkowetz
- Department of Urology, University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
- Thomas Abbott
- European Association of Urology, Nijmegen, The Netherlands
- Andrea Alberti
- Unit of Urological Robotic Surgery and Renal Transplantation, University of Florence, Careggi Hospital, Florence, Italy
- Anders Bjartell
- Department of Translational Medicine, Lund University, Lund, Sweden
- James T. Brash
- IQVIA, Real World Solutions, Brighton, UK
- Riccardo Campi
- Unit of Urological Robotic Surgery and Renal Transplantation, University of Florence, Careggi Hospital, Florence, Italy
- Andrew Chilelli
- Astellas Pharma Europe Ltd, Surrey, UK
- Mitchell Conover
- Janssen Research & Development, Titusville, NJ, USA
- Niculae Constantinovici
- Bayer AG, Berlin, Germany
- Eleanor Davies
- IQVIA, Real World Solutions, Brighton, UK
- Bertrand De Meulder
- Association EISBM, Vourles, France
- Sherrine Eid
- SAS Institute, Cary, NC, USA
- Mauro Gacci
- Unit of Urological Robotic Surgery and Renal Transplantation, University of Florence, Careggi Hospital, Florence, Italy
- Asieh Golozar
- Odysseus Data Services, New York, NY, USA; OHDSI Center, Northeastern University, Boston, MA, USA
- Haroon Hafeez
- Shaukat Khanum Memorial Cancer Hospital & Research Centre, Peshawar, Pakistan
- Samiul Haque
- SAS Institute, Cary, NC, USA
- Ayman Hijazy
- Association EISBM, Vourles, France
- Tim Hulsen
- Department of Hospital Services & Informatics, Philips Research, Eindhoven, The Netherlands
- Andreas Josefsson
- Department of Urology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Wallenberg Center for Molecular Medicine, Umeå University, Umeå, Sweden
- Sara Khalid
- University of Oxford, Oxford, UK
- Raivo Kolde
- Institute of Computer Science, University of Tartu, Tartu, Estonia
- Daniel Kotik
- Center for Advanced Systems Understanding, Görlitz, Germany; Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
- Samu Kurki
- Bayer OY, Turku, Finland
- Mark Lambrecht
- SAS Institute, Cary, NC, USA
- Chi-Ho Leung
- S.H. Ho Urology Centre, Department of Surgery, The Chinese University of Hong Kong, Hong Kong, China
- Julia Moreno
- SAS Institute, Cary, NC, USA
- Rossella Nicoletti
- Unit of Urological Robotic Surgery and Renal Transplantation, University of Florence, Careggi Hospital, Florence, Italy
- Daan Nieboer
- Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Marek Oja
- Institute of Computer Science, University of Tartu, Tartu, Estonia
- Soundarya Palanisamy
- SAS Institute, Cary, NC, USA
- Peter Prinsen
- Netherlands Comprehensive Cancer Organisation (IKNL), Utrecht, The Netherlands
- Christian Reich
- Odysseus Data Services, New York, NY, USA; OHDSI Center, Northeastern University, Boston, MA, USA
- Giulio Raffaele Resta
- Unit of Urological Robotic Surgery and Renal Transplantation, University of Florence, Careggi Hospital, Florence, Italy
- Maria J. Ribal
- Uro-Oncology Unit, Hospital Clinic, University of Barcelona, Barcelona, Spain
- Juan Gómez Rivas
- Department of Urology, Hospital Clinico San Carlos, Madrid, Spain
- Emma Smith
- Guidelines Office, European Association of Urology, Arnhem, The Netherlands
- Robert Snijder
- Janssen Research & Development, Titusville, NJ, USA
- Carl Steinbeisser
- Collaborate Project Management, Munich, Germany
- Frederik Vandenberghe
- SAS Institute, Cary, NC, USA
- Philip Cornford
- Liverpool University Hospitals NHS Trust, Liverpool, UK
- Susan Evans-Axelsson
- Bayer AG, Berlin, Germany
- James N'Dow
- Academic Urology Unit, University of Aberdeen, Aberdeen, UK
- Peter-Paul M. Willemse
- Department of Urology, Cancer Center, University Medical Center Utrecht, Utrecht, The Netherlands; Corresponding author. Department of Urology, Cancer Center University Medical Center Utrecht, Universiteitsweg 100, 3584 CG Utrecht, The Netherlands. Tel. +31 88 75 555 55.
- Journal volume & issue
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Vol. 63
pp. 81 – 88
Abstract
Combination therapies in metastatic hormone-sensitive prostate cancer (mHSPC), which include the addition of an androgen receptor signaling inhibitor and/or docetaxel to androgen deprivation therapy, have been a game changer in the management of this disease stage. However, these therapies come with their fair share of toxicities and side effects. The goal of this observational study is to report drug-related adverse events (AEs), which are correlated with systemic combination therapies for mHSPC. Determining the optimal treatment option requires large cohorts to estimate the tolerability and AEs of these combination therapies in “real-life” patients with mHSPC, as provided in this study. We use a network of databases that includes population-based registries, electronic health records, and insurance claims, containing the overall target population and subgroups of patients defined by unique certain characteristics, demographics, and comorbidities, to compute the incidence of common AEs associated with systemic therapies in the setting of mHSPC. These data sources are standardised using the Observational Medical Outcomes Partnership Common Data Model. We perform the descriptive statistics as well as calculate the AE incidence rate separately for each treatment group, stratified by age groups and index year. The time until the first event is estimated using the Kaplan-Meier method within each age group. In the case of episodic events, the anticipated mean cumulative counts of events are calculated. Our study will allow clinicians to tailor optimal therapies for mHSPC patients, and they will serve as a basis for comparative method studies.
