Meta-analysis of individualized treatment rules via sign-coherency

Jay Jojo Cheng, Jared D. Huling, Guanhua Chen
Proceedings of the 2nd Machine Learning for Health symposium, PMLR 193:171-198, 2022.

Abstract

Medical treatments tailored to a patient’s baseline characteristics hold the potential of improving patient outcomes while reducing negative side effects. Learning individualized treatment rules (ITRs) often requires aggregation of multiple datasets(sites); however, current ITR methodology does not take between-site heterogeneity into account, which can hurt model generalizability when deploying back to each site. To address this problem, we develop a method for individual-level meta-analysis of ITRs, which jointly learns site-specific ITRs while borrowing information about feature sign-coherency via a scientifically-motivated directionality principle. We also develop an adaptive procedure for model tuning, using information criteria tailored to the ITR learning problem. We study the proposed methods through numerical experiments to understand their performance under different levels of between-site heterogeneity and apply the methodology to estimate ITRs in a large multi-center database of electronic health records. This work extends several popular methodologies for estimating ITRs (A-learning, weighted learning) to the multiple-sites setting.

Cite this Paper

BibTeX
@InProceedings{pmlr-v193-cheng22a, title = {Meta-analysis of individualized treatment rules via sign-coherency}, author = {Cheng, Jay Jojo and Huling, Jared D. and Chen, Guanhua}, booktitle = {Proceedings of the 2nd Machine Learning for Health symposium}, pages = {171--198}, 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/cheng22a/cheng22a.pdf}, url = {https://proceedings.mlr.press/v193/cheng22a.html}, abstract = {Medical treatments tailored to a patient’s baseline characteristics hold the potential of improving patient outcomes while reducing negative side effects. Learning individualized treatment rules (ITRs) often requires aggregation of multiple datasets(sites); however, current ITR methodology does not take between-site heterogeneity into account, which can hurt model generalizability when deploying back to each site. To address this problem, we develop a method for individual-level meta-analysis of ITRs, which jointly learns site-specific ITRs while borrowing information about feature sign-coherency via a scientifically-motivated directionality principle. We also develop an adaptive procedure for model tuning, using information criteria tailored to the ITR learning problem. We study the proposed methods through numerical experiments to understand their performance under different levels of between-site heterogeneity and apply the methodology to estimate ITRs in a large multi-center database of electronic health records. This work extends several popular methodologies for estimating ITRs (A-learning, weighted learning) to the multiple-sites setting.} }
Endnote
%0 Conference Paper %T Meta-analysis of individualized treatment rules via sign-coherency %A Jay Jojo Cheng %A Jared D. Huling %A Guanhua Chen %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-cheng22a %I PMLR %P 171--198 %U https://proceedings.mlr.press/v193/cheng22a.html %V 193 %X Medical treatments tailored to a patient’s baseline characteristics hold the potential of improving patient outcomes while reducing negative side effects. Learning individualized treatment rules (ITRs) often requires aggregation of multiple datasets(sites); however, current ITR methodology does not take between-site heterogeneity into account, which can hurt model generalizability when deploying back to each site. To address this problem, we develop a method for individual-level meta-analysis of ITRs, which jointly learns site-specific ITRs while borrowing information about feature sign-coherency via a scientifically-motivated directionality principle. We also develop an adaptive procedure for model tuning, using information criteria tailored to the ITR learning problem. We study the proposed methods through numerical experiments to understand their performance under different levels of between-site heterogeneity and apply the methodology to estimate ITRs in a large multi-center database of electronic health records. This work extends several popular methodologies for estimating ITRs (A-learning, weighted learning) to the multiple-sites setting.
APA
Cheng, J.J., Huling, J.D. & Chen, G.. (2022). Meta-analysis of individualized treatment rules via sign-coherency. Proceedings of the 2nd Machine Learning for Health symposium, in Proceedings of Machine Learning Research 193:171-198 Available from https://proceedings.mlr.press/v193/cheng22a.html.

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