Comparison of Synthetic Data Generation Techniques for Control Group Survival Data in Oncology Clinical Trials: Simulation Study

Journals

1. Fukuda M, Kotaki S, Nozawa M, Tsuji K, Watanabe M, Akiyama H, Ariji Y. An attempt to generate panoramic radiographs including jaw cysts using StyleGAN3. Dentomaxillofacial Radiology 2024;53(8):535 View
2. El Kababji S, Mitsakakis N, Jonker E, Beltran-Bless A, Pond G, Vandermeer L, Radhakrishnan D, Mosquera L, Paterson A, Shepherd L, Chen B, Barlow W, Gralow J, Savard M, Fesl C, Hlauschek D, Balic M, Rinnerthaler G, Greil R, Gnant M, Clemons M, El Emam K. Augmenting Insufficiently Accruing Oncology Clinical Trials Using Generative Models: Validation Study. Journal of Medical Internet Research 2025;27:e66821 View
3. Mishra H, Gupta R. Leveraging Generative AI for Drug Safety and Pharmacovigilance. Current Reviews in Clinical and Experimental Pharmacology 2025;20(2):89 View
4. Cipriani M, Di Rocco L, Puopolo M, Alfò M. A flexible parametric approach to synthetic patients generation using health data. Statistical Methods & Applications 2025;34(4):639 View
5. Isasa I, Catalina M, Epelde G, Aginako N, Beristain A. Synthetic Tabular Data Generation Under Horizontal Federated Learning Environments in Acute Myeloid Leukemia: Case-Based Simulation Study. JMIR Medical Informatics 2025;13:e74116 View
6. Demuth S, Rousseau O, Faddeenkov I, Paris J, De Sèze J, Baciotti B, Payet M, Guillaudeux M, Barreteau A, Laplaud D, Edan G, Gourraud P. Privacy-by-Design Approach to Generate Two Virtual Clinical Trials for Multiple Sclerosis and Release Them as Open Datasets: Evaluation Study. Journal of Medical Internet Research 2025;27:e71297 View