Toward Valid Generative Clinical Trial Data with Survival Endpoints

Perrine Chassat, Van Tuan Nguyen, Lucas Ducrot, Emilie Lanoy, Agathe Guilloux
Proceedings of the Fifth Machine Learning for Health Symposium, PMLR 297:759-791, 2026.

Abstract

Clinical trials face mounting challenges: fragmented patient populations, slow enrollment, and unsustainable costs, particularly for late phase trials in oncology and rare diseases. While external control arms built from real-world data have been explored, a promising alternative is the generation of synthetic control arms using generative {AI}. A central challenge is the generation of time-to-event outcomes, which constitute primary endpoints in oncology and rare disease trials, but are difficult to model under censoring and small sample sizes. Existing generative approaches, largely {GAN}-based, are data-hungry, unstable, and rely on strong assumptions such as independent censoring. We introduce a variational autoencoder ({VAE}) that jointly generates mixed-type covariates and survival outcomes within a unified latent variable framework, without assuming independent censoring. Across synthetic and real trial datasets, we evaluate our model in two realistic scenarios: (i) data sharing under privacy constraints, where synthetic controls substitute for original data, and (ii) control-arm augmentation, where synthetic patients mitigate imbalances between treated and control groups. Our method outperforms {GAN} baselines on fidelity, utility, and privacy metrics, while revealing systematic miscalibration of type {I} error and power. We propose a post-generation selection procedure that improves calibration, highlighting both progress and open challenges for generative survival modeling.

Cite this Paper

BibTeX
@InProceedings{pmlr-v297-chassat26a, title = {Toward Valid Generative Clinical Trial Data with Survival Endpoints}, author = {Chassat, Perrine and Nguyen, Van Tuan and Ducrot, Lucas and Lanoy, Emilie and Guilloux, Agathe}, booktitle = {Proceedings of the Fifth Machine Learning for Health Symposium}, pages = {759--791}, year = {2026}, editor = {Argaw, Peniel and Zhang, Haoran and Jabbour, Sarah and Chandak, Payal and Ji, Jerry and Mukherjee, Sumit and Salaudeen, Olawale and Chang, Trenton and Healey, Elizabeth and Gröger, Fabian and Adibi, Amin and Hegselmann, Stefan and Wild, Benjamin and Noori, Ayush}, volume = {297}, series = {Proceedings of Machine Learning Research}, month = {13--14 Dec}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v297/main/assets/chassat26a/chassat26a.pdf}, url = {https://proceedings.mlr.press/v297/chassat26a.html}, abstract = {Clinical trials face mounting challenges: fragmented patient populations, slow enrollment, and unsustainable costs, particularly for late phase trials in oncology and rare diseases. While external control arms built from real-world data have been explored, a promising alternative is the generation of synthetic control arms using generative {AI}. A central challenge is the generation of time-to-event outcomes, which constitute primary endpoints in oncology and rare disease trials, but are difficult to model under censoring and small sample sizes. Existing generative approaches, largely {GAN}-based, are data-hungry, unstable, and rely on strong assumptions such as independent censoring. We introduce a variational autoencoder ({VAE}) that jointly generates mixed-type covariates and survival outcomes within a unified latent variable framework, without assuming independent censoring. Across synthetic and real trial datasets, we evaluate our model in two realistic scenarios: (i) data sharing under privacy constraints, where synthetic controls substitute for original data, and (ii) control-arm augmentation, where synthetic patients mitigate imbalances between treated and control groups. Our method outperforms {GAN} baselines on fidelity, utility, and privacy metrics, while revealing systematic miscalibration of type {I} error and power. We propose a post-generation selection procedure that improves calibration, highlighting both progress and open challenges for generative survival modeling.} }
Endnote
%0 Conference Paper %T Toward Valid Generative Clinical Trial Data with Survival Endpoints %A Perrine Chassat %A Van Tuan Nguyen %A Lucas Ducrot %A Emilie Lanoy %A Agathe Guilloux %B Proceedings of the Fifth Machine Learning for Health Symposium %C Proceedings of Machine Learning Research %D 2026 %E Peniel Argaw %E Haoran Zhang %E Sarah Jabbour %E Payal Chandak %E Jerry Ji %E Sumit Mukherjee %E Olawale Salaudeen %E Trenton Chang %E Elizabeth Healey %E Fabian Gröger %E Amin Adibi %E Stefan Hegselmann %E Benjamin Wild %E Ayush Noori %F pmlr-v297-chassat26a %I PMLR %P 759--791 %U https://proceedings.mlr.press/v297/chassat26a.html %V 297 %X Clinical trials face mounting challenges: fragmented patient populations, slow enrollment, and unsustainable costs, particularly for late phase trials in oncology and rare diseases. While external control arms built from real-world data have been explored, a promising alternative is the generation of synthetic control arms using generative {AI}. A central challenge is the generation of time-to-event outcomes, which constitute primary endpoints in oncology and rare disease trials, but are difficult to model under censoring and small sample sizes. Existing generative approaches, largely {GAN}-based, are data-hungry, unstable, and rely on strong assumptions such as independent censoring. We introduce a variational autoencoder ({VAE}) that jointly generates mixed-type covariates and survival outcomes within a unified latent variable framework, without assuming independent censoring. Across synthetic and real trial datasets, we evaluate our model in two realistic scenarios: (i) data sharing under privacy constraints, where synthetic controls substitute for original data, and (ii) control-arm augmentation, where synthetic patients mitigate imbalances between treated and control groups. Our method outperforms {GAN} baselines on fidelity, utility, and privacy metrics, while revealing systematic miscalibration of type {I} error and power. We propose a post-generation selection procedure that improves calibration, highlighting both progress and open challenges for generative survival modeling.
APA
Chassat, P., Nguyen, V.T., Ducrot, L., Lanoy, E. & Guilloux, A.. (2026). Toward Valid Generative Clinical Trial Data with Survival Endpoints. Proceedings of the Fifth Machine Learning for Health Symposium, in Proceedings of Machine Learning Research 297:759-791 Available from https://proceedings.mlr.press/v297/chassat26a.html.

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