Simulating Colorectal Cancer Trials Using Real-World Data

Author:

Chen Zhaoyi12ORCID,Zhang Hansi1,George Thomas J.3ORCID,Guo Yi12ORCID,Prosperi Mattia4,Guo Jingchuan5ORCID,Braithwaite Dejana46ORCID,Wang Fei7ORCID,Kibbe Warren8ORCID,Wagner Lynne9ORCID,Bian Jiang12

Affiliation:

1. Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL

2. Cancer Informatics Share Resource, University of Florida Health Cancer Center, Gainesville, FL

3. Division of Hematology & Oncology, Department of Medicine, College of Medicine, University of Florida, Gainesville, FL

4. Department of Epidemiology, College of Medicine & College of Public Health and Health Professions, University of Florida, Gainesville, FL

5. Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL

6. Department of Surgery, College of Medicine, University of Florida, Gainesville, FL

7. Department of Population Health Sciences, Cornell University, New York, NY

8. Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina

9. Department of Social Sciences and Health Policy, Wake Forest School of Medicine, Winston-Salem, NC

Abstract

PURPOSE Using real-world data (RWD)–based trial simulation approach, we aim to simulate colorectal cancer (CRC) trials and examine both effectiveness and safety end points in different simulation scenarios. METHODS We identified five phase III trials comparing new treatment regimens with an US Food and Drug Administration–approved first-line treatment in patients with metastatic CRC (ie, fluorouracil, leucovorin, and irinotecan) as the standard-of-care (SOC) control arm. Using Electronic Health Record–derived data from the OneFlorida network, we defined the study populations and outcome measures using the protocols from the original trials. Our design scenarios were (1) simulation of the SOC fluorouracil, leucovorin, and irinotecan arm and (2) comparative effectiveness research (CER) simulation of the control and experimental arms. For each scenario, we adjusted for random assignment, sampling, and dropout. We used overall survival (OS) and severe adverse events (SAEs) to measure effectiveness and safety. RESULTS We conducted CER simulations for two trials, and SOC simulations for three trials. The effect sizes of our simulated trials were stable across all simulation runs. Compared with the original trials, we observed longer OS and higher mean number of SAEs in both CER and SOC simulation. In the two CER simulations, hazard ratios associated with death from simulations were similar to that reported in the original trials. Consistent with the original trials, we found higher risk ratios of SAEs in the experiment arm, suggesting potentially higher toxicities from the new treatment regimen. We also observed similar SAE rates across all simulations compared with the original trials. CONCLUSION In this study, we simulated five CRC trials, and tested two simulation scenarios with several different configurations demonstrated that our simulations can robustly generate effectiveness and safety outcomes comparable with the original trials using real-world data.

Publisher

American Society of Clinical Oncology (ASCO)

Subject

General Medicine

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