Simulating Colorectal Cancer Trials Using Real-World Data.

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Chen, Z; Zhang, H; George, TJ; Guo, Y; Prosperi, M; Guo, J; Braithwaite, D; Wang, F; Kibbe, W; Wagner, L; Bian, J

Published Date

  • July 2022

Published In

Volume / Issue

  • 6 /

Start / End Page

  • e2100195 -

PubMed ID

  • 35839432

Electronic International Standard Serial Number (EISSN)

  • 2473-4276

Digital Object Identifier (DOI)

  • 10.1200/CCI.21.00195

Language

  • eng

Conference Location

  • United States