Identifying Heterogeneous Treatment Effects in Multiple Outcomes using Joint Confidence Intervals

Peniel N. Argaw, Elizabeth Healey, Isaac S. Kohane
Proceedings of the 2nd Machine Learning for Health symposium, PMLR 193:141-170, 2022.

Abstract

Heterogeneous treatment effects (HTEs) are commonly identified during randomized controlled trials (RCTs). Identifying subgroups of patients with similar treatment effects is of high interest in clinical research to advance precision medicine. Often, multiple clinical outcomes are measured during an RCT, each having a potentially heterogeneous effect. Recently there has been high interest in identifying subgroups from HTEs, however, there has been less focus on developing tools in settings where there are multiple outcomes. In this work, we propose a framework for partitioning the covariate space to identify subgroups across multiple outcomes based on the joint CIs. We test our algorithm on synthetic and semi-synthetic data where there are two outcomes, and demonstrate that our algorithm is able to capture the HTE in both outcomes simultaneously.

Cite this Paper

BibTeX
@InProceedings{pmlr-v193-argaw22a, title = {Identifying Heterogeneous Treatment Effects in Multiple Outcomes using Joint Confidence Intervals}, author = {Argaw, Peniel N. and Healey, Elizabeth and Kohane, Isaac S.}, booktitle = {Proceedings of the 2nd Machine Learning for Health symposium}, pages = {141--170}, year = {2022}, editor = {Parziale, Antonio and Agrawal, Monica and Joshi, Shalmali and Chen, Irene Y. and Tang, Shengpu and Oala, Luis and Subbaswamy, Adarsh}, volume = {193}, series = {Proceedings of Machine Learning Research}, month = {28 Nov}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v193/argaw22a/argaw22a.pdf}, url = {https://proceedings.mlr.press/v193/argaw22a.html}, abstract = {Heterogeneous treatment effects (HTEs) are commonly identified during randomized controlled trials (RCTs). Identifying subgroups of patients with similar treatment effects is of high interest in clinical research to advance precision medicine. Often, multiple clinical outcomes are measured during an RCT, each having a potentially heterogeneous effect. Recently there has been high interest in identifying subgroups from HTEs, however, there has been less focus on developing tools in settings where there are multiple outcomes. In this work, we propose a framework for partitioning the covariate space to identify subgroups across multiple outcomes based on the joint CIs. We test our algorithm on synthetic and semi-synthetic data where there are two outcomes, and demonstrate that our algorithm is able to capture the HTE in both outcomes simultaneously.} }
Endnote
%0 Conference Paper %T Identifying Heterogeneous Treatment Effects in Multiple Outcomes using Joint Confidence Intervals %A Peniel N. Argaw %A Elizabeth Healey %A Isaac S. Kohane %B Proceedings of the 2nd Machine Learning for Health symposium %C Proceedings of Machine Learning Research %D 2022 %E Antonio Parziale %E Monica Agrawal %E Shalmali Joshi %E Irene Y. Chen %E Shengpu Tang %E Luis Oala %E Adarsh Subbaswamy %F pmlr-v193-argaw22a %I PMLR %P 141--170 %U https://proceedings.mlr.press/v193/argaw22a.html %V 193 %X Heterogeneous treatment effects (HTEs) are commonly identified during randomized controlled trials (RCTs). Identifying subgroups of patients with similar treatment effects is of high interest in clinical research to advance precision medicine. Often, multiple clinical outcomes are measured during an RCT, each having a potentially heterogeneous effect. Recently there has been high interest in identifying subgroups from HTEs, however, there has been less focus on developing tools in settings where there are multiple outcomes. In this work, we propose a framework for partitioning the covariate space to identify subgroups across multiple outcomes based on the joint CIs. We test our algorithm on synthetic and semi-synthetic data where there are two outcomes, and demonstrate that our algorithm is able to capture the HTE in both outcomes simultaneously.
APA
Argaw, P.N., Healey, E. & Kohane, I.S.. (2022). Identifying Heterogeneous Treatment Effects in Multiple Outcomes using Joint Confidence Intervals. Proceedings of the 2nd Machine Learning for Health symposium, in Proceedings of Machine Learning Research 193:141-170 Available from https://proceedings.mlr.press/v193/argaw22a.html.

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