Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints

Cong Shen, Zhiyang Wang, Sofia Villar, Mihaela Van Der Schaar
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8730-8740, 2020.

Abstract

Phase I dose-finding trials are increasingly challenging as the relationship between efficacy and toxicity of new compounds (or combination of them) becomes more complex. Despite this, most commonly used methods in practice focus on identifying a Maximum Tolerated Dose (MTD) by learning only from toxicity events. We present a novel adaptive clinical trial methodology, called Safe Efficacy Exploration Dose Allocation (SEEDA), that aims at maximizing the cumulative efficacies while satisfying the toxicity safety constraint with high probability. We evaluate performance objectives that have operational meanings in practical clinical trials, including cumulative efficacy, recommendation/allocation success probabilities, toxicity violation probability, and sample efficiency. An extended SEEDA-Plateau algorithm that is tailored for the increase-then-plateau efficacy behavior of molecularly targeted agents (MTA) is also presented. Through numerical experiments using both synthetic and real-world datasets, we show that SEEDA outperforms state-of-the-art clinical trial designs by finding the optimal dose with higher success rate and fewer patients.

Cite this Paper

BibTeX
@InProceedings{pmlr-v119-shen20d, title = {Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints}, author = {Shen, Cong and Wang, Zhiyang and Villar, Sofia and Van Der Schaar, Mihaela}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {8730--8740}, year = {2020}, editor = {III, Hal Daumé and Singh, Aarti}, volume = {119}, series = {Proceedings of Machine Learning Research}, month = {13--18 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v119/shen20d/shen20d.pdf}, url = {https://proceedings.mlr.press/v119/shen20d.html}, abstract = {Phase I dose-finding trials are increasingly challenging as the relationship between efficacy and toxicity of new compounds (or combination of them) becomes more complex. Despite this, most commonly used methods in practice focus on identifying a Maximum Tolerated Dose (MTD) by learning only from toxicity events. We present a novel adaptive clinical trial methodology, called Safe Efficacy Exploration Dose Allocation (SEEDA), that aims at maximizing the cumulative efficacies while satisfying the toxicity safety constraint with high probability. We evaluate performance objectives that have operational meanings in practical clinical trials, including cumulative efficacy, recommendation/allocation success probabilities, toxicity violation probability, and sample efficiency. An extended SEEDA-Plateau algorithm that is tailored for the increase-then-plateau efficacy behavior of molecularly targeted agents (MTA) is also presented. Through numerical experiments using both synthetic and real-world datasets, we show that SEEDA outperforms state-of-the-art clinical trial designs by finding the optimal dose with higher success rate and fewer patients.} }
Endnote
%0 Conference Paper %T Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints %A Cong Shen %A Zhiyang Wang %A Sofia Villar %A Mihaela Van Der Schaar %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E Aarti Singh %F pmlr-v119-shen20d %I PMLR %P 8730--8740 %U https://proceedings.mlr.press/v119/shen20d.html %V 119 %X Phase I dose-finding trials are increasingly challenging as the relationship between efficacy and toxicity of new compounds (or combination of them) becomes more complex. Despite this, most commonly used methods in practice focus on identifying a Maximum Tolerated Dose (MTD) by learning only from toxicity events. We present a novel adaptive clinical trial methodology, called Safe Efficacy Exploration Dose Allocation (SEEDA), that aims at maximizing the cumulative efficacies while satisfying the toxicity safety constraint with high probability. We evaluate performance objectives that have operational meanings in practical clinical trials, including cumulative efficacy, recommendation/allocation success probabilities, toxicity violation probability, and sample efficiency. An extended SEEDA-Plateau algorithm that is tailored for the increase-then-plateau efficacy behavior of molecularly targeted agents (MTA) is also presented. Through numerical experiments using both synthetic and real-world datasets, we show that SEEDA outperforms state-of-the-art clinical trial designs by finding the optimal dose with higher success rate and fewer patients.
APA
Shen, C., Wang, Z., Villar, S. & Van Der Schaar, M.. (2020). Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints. Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:8730-8740 Available from https://proceedings.mlr.press/v119/shen20d.html.

Related Material