Doubly Protected Estimation for Survival Outcomes Utilizing External Controls for Randomized Clinical Trials

Chenyin Gao, Shu Yang, Mingyang Shan, Wenyu Ye, Ilya Lipkovich, Douglas Faries
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:18698-18723, 2025.

Abstract

Censored survival data are common in clinical trials, but small control groups can pose challenges, particularly in rare diseases or where balanced randomization is impractical. Recent approaches leverage external controls from historical studies or real-world data to strengthen treatment evaluation for survival outcomes. However, using external controls directly may introduce biases due to data heterogeneity. We propose a doubly protected estimator for the treatment-specific restricted mean survival time difference that is more efficient than trial-only estimators and mitigates biases from external data. Our method adjusts for covariate shifts via doubly robust estimation and addresses outcome drift using the DR-Learner for selective borrowing. The approach can incorporate machine learning to approximate survival curves and detect outcome drifts without strict parametric assumptions, borrowing only comparable external controls. Extensive simulation studies and a real-data application evaluating the efficacy of Galcanezumab in mitigating migraine headaches have been conducted to illustrate the effectiveness of our proposed framework.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-gao25s, title = {Doubly Protected Estimation for Survival Outcomes Utilizing External Controls for Randomized Clinical Trials}, author = {Gao, Chenyin and Yang, Shu and Shan, Mingyang and Ye, Wenyu and Lipkovich, Ilya and Faries, Douglas}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {18698--18723}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/gao25s/gao25s.pdf}, url = {https://proceedings.mlr.press/v267/gao25s.html}, abstract = {Censored survival data are common in clinical trials, but small control groups can pose challenges, particularly in rare diseases or where balanced randomization is impractical. Recent approaches leverage external controls from historical studies or real-world data to strengthen treatment evaluation for survival outcomes. However, using external controls directly may introduce biases due to data heterogeneity. We propose a doubly protected estimator for the treatment-specific restricted mean survival time difference that is more efficient than trial-only estimators and mitigates biases from external data. Our method adjusts for covariate shifts via doubly robust estimation and addresses outcome drift using the DR-Learner for selective borrowing. The approach can incorporate machine learning to approximate survival curves and detect outcome drifts without strict parametric assumptions, borrowing only comparable external controls. Extensive simulation studies and a real-data application evaluating the efficacy of Galcanezumab in mitigating migraine headaches have been conducted to illustrate the effectiveness of our proposed framework.} }
Endnote
%0 Conference Paper %T Doubly Protected Estimation for Survival Outcomes Utilizing External Controls for Randomized Clinical Trials %A Chenyin Gao %A Shu Yang %A Mingyang Shan %A Wenyu Ye %A Ilya Lipkovich %A Douglas Faries %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-gao25s %I PMLR %P 18698--18723 %U https://proceedings.mlr.press/v267/gao25s.html %V 267 %X Censored survival data are common in clinical trials, but small control groups can pose challenges, particularly in rare diseases or where balanced randomization is impractical. Recent approaches leverage external controls from historical studies or real-world data to strengthen treatment evaluation for survival outcomes. However, using external controls directly may introduce biases due to data heterogeneity. We propose a doubly protected estimator for the treatment-specific restricted mean survival time difference that is more efficient than trial-only estimators and mitigates biases from external data. Our method adjusts for covariate shifts via doubly robust estimation and addresses outcome drift using the DR-Learner for selective borrowing. The approach can incorporate machine learning to approximate survival curves and detect outcome drifts without strict parametric assumptions, borrowing only comparable external controls. Extensive simulation studies and a real-data application evaluating the efficacy of Galcanezumab in mitigating migraine headaches have been conducted to illustrate the effectiveness of our proposed framework.
APA
Gao, C., Yang, S., Shan, M., Ye, W., Lipkovich, I. & Faries, D.. (2025). Doubly Protected Estimation for Survival Outcomes Utilizing External Controls for Randomized Clinical Trials. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:18698-18723 Available from https://proceedings.mlr.press/v267/gao25s.html.

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