Adaptive Experiment Design with Synthetic Controls

Alihan Hüyük, Zhaozhi Qian, Mihaela van der Schaar
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, PMLR 238:1180-1188, 2024.

Abstract

Clinical trials are typically run in order to understand the effects of a new treatment on a given population of patients. However, patients in large populations rarely respond the same way to the same treatment. This heterogeneity in patient responses necessitates trials that investigate effects on multiple subpopulations—especially when a treatment has marginal or no benefit for the overall population but might have significant benefit for a particular subpopulation. Motivated by this need, we propose Syntax, an exploratory trial design that identifies subpopulations with positive treatment effect among many subpopulations. Syntax is sample efficient as it (i) recruits and allocates patients adaptively and (ii) estimates treatment effects by forming synthetic controls for each subpopulation that combines control samples from other subpopulations. We validate the performance of Syntax and provide insights into when it might have an advantage over conventional trial designs through experiments.

Cite this Paper

BibTeX
@InProceedings{pmlr-v238-huyuk24a, title = { Adaptive Experiment Design with Synthetic Controls }, author = {H\"{u}y\"{u}k, Alihan and Qian, Zhaozhi and van der Schaar, Mihaela}, booktitle = {Proceedings of The 27th International Conference on Artificial Intelligence and Statistics}, pages = {1180--1188}, year = {2024}, editor = {Dasgupta, Sanjoy and Mandt, Stephan and Li, Yingzhen}, volume = {238}, series = {Proceedings of Machine Learning Research}, month = {02--04 May}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v238/huyuk24a/huyuk24a.pdf}, url = {https://proceedings.mlr.press/v238/huyuk24a.html}, abstract = { Clinical trials are typically run in order to understand the effects of a new treatment on a given population of patients. However, patients in large populations rarely respond the same way to the same treatment. This heterogeneity in patient responses necessitates trials that investigate effects on multiple subpopulations—especially when a treatment has marginal or no benefit for the overall population but might have significant benefit for a particular subpopulation. Motivated by this need, we propose Syntax, an exploratory trial design that identifies subpopulations with positive treatment effect among many subpopulations. Syntax is sample efficient as it (i) recruits and allocates patients adaptively and (ii) estimates treatment effects by forming synthetic controls for each subpopulation that combines control samples from other subpopulations. We validate the performance of Syntax and provide insights into when it might have an advantage over conventional trial designs through experiments. } }
Endnote
%0 Conference Paper %T Adaptive Experiment Design with Synthetic Controls %A Alihan Hüyük %A Zhaozhi Qian %A Mihaela van der Schaar %B Proceedings of The 27th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2024 %E Sanjoy Dasgupta %E Stephan Mandt %E Yingzhen Li %F pmlr-v238-huyuk24a %I PMLR %P 1180--1188 %U https://proceedings.mlr.press/v238/huyuk24a.html %V 238 %X Clinical trials are typically run in order to understand the effects of a new treatment on a given population of patients. However, patients in large populations rarely respond the same way to the same treatment. This heterogeneity in patient responses necessitates trials that investigate effects on multiple subpopulations—especially when a treatment has marginal or no benefit for the overall population but might have significant benefit for a particular subpopulation. Motivated by this need, we propose Syntax, an exploratory trial design that identifies subpopulations with positive treatment effect among many subpopulations. Syntax is sample efficient as it (i) recruits and allocates patients adaptively and (ii) estimates treatment effects by forming synthetic controls for each subpopulation that combines control samples from other subpopulations. We validate the performance of Syntax and provide insights into when it might have an advantage over conventional trial designs through experiments.
APA
Hüyük, A., Qian, Z. & van der Schaar, M.. (2024). Adaptive Experiment Design with Synthetic Controls . Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 238:1180-1188 Available from https://proceedings.mlr.press/v238/huyuk24a.html.

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