Beyond Clinical Trials: Using Real World Evidence to Investigate Heterogeneous, Time-Varying Treatment Effects

Isabel Chien, Cliff Wong, Zelalem Gero, Jaspreet Bagga, Risa Ueno, Richard E. Turner, Roshanthi K. Weerasinghe, Brian Piening, Tristan Naumann, Carlo Bifulco, Hoifung Poon, Javier González Hernández
Proceedings of the 9th Machine Learning for Healthcare Conference, PMLR 252, 2024.

Abstract

Randomized controlled trials (RCTs), though essential for evaluating the efficacy of novel treatments, are costly and time-intensive. Due to strict eligibility criteria, RCTs may not adequately represent diverse patient populations, leading to equity issues and limited generalizability. Additionally, conventional trial analysis methods are limited by strict assumptions and biases. Real-world evidence (RWE) offers a promising avenue to explore treatment effects beyond trial settings, addressing gaps in representation and providing additional insights into patient outcomes over time. We introduce TRIALSCOPE-X and TRIALSCOPE-XL, machine learning pipelines designed to analyze treatment outcomes using RWE by mitigating biases that arise from observational data and addressing the limitations of conventional methods. We estimate causal, time-varying treatment effects across heterogeneous patient populations and varied timeframes. Preliminary results investigating the treatment benefit of Keytruda, a widely-used cancer immunotherapy drug, demonstrate the utility of our methods in evaluating treatment outcomes under novel settings and uncovering potential disparities. Our findings highlight the potential of RWE-based analysis to provide data-driven insights that inform evidence-based medicine and shape more inclusive and comprehensive clinical research, supplementing traditional clinical trial findings.

Cite this Paper

BibTeX
@InProceedings{pmlr-v252-chien24a, title = {Beyond Clinical Trials: Using Real World Evidence to Investigate Heterogeneous, Time-Varying Treatment Effects}, author = {Chien, Isabel and Wong, Cliff and Gero, Zelalem and Bagga, Jaspreet and Ueno, Risa and Turner, Richard E. and Weerasinghe, Roshanthi K. and Piening, Brian and Naumann, Tristan and Bifulco, Carlo and Poon, Hoifung and Hern\'andez, Javier Gonz\'alez}, booktitle = {Proceedings of the 9th Machine Learning for Healthcare Conference}, year = {2024}, editor = {Deshpande, Kaivalya and Fiterau, Madalina and Joshi, Shalmali and Lipton, Zachary and Ranganath, Rajesh and Urteaga, Iñigo}, volume = {252}, series = {Proceedings of Machine Learning Research}, month = {16--17 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v252/main/assets/chien24a/chien24a.pdf}, url = {https://proceedings.mlr.press/v252/chien24a.html}, abstract = {Randomized controlled trials (RCTs), though essential for evaluating the efficacy of novel treatments, are costly and time-intensive. Due to strict eligibility criteria, RCTs may not adequately represent diverse patient populations, leading to equity issues and limited generalizability. Additionally, conventional trial analysis methods are limited by strict assumptions and biases. Real-world evidence (RWE) offers a promising avenue to explore treatment effects beyond trial settings, addressing gaps in representation and providing additional insights into patient outcomes over time. We introduce TRIALSCOPE-X and TRIALSCOPE-XL, machine learning pipelines designed to analyze treatment outcomes using RWE by mitigating biases that arise from observational data and addressing the limitations of conventional methods. We estimate causal, time-varying treatment effects across heterogeneous patient populations and varied timeframes. Preliminary results investigating the treatment benefit of Keytruda, a widely-used cancer immunotherapy drug, demonstrate the utility of our methods in evaluating treatment outcomes under novel settings and uncovering potential disparities. Our findings highlight the potential of RWE-based analysis to provide data-driven insights that inform evidence-based medicine and shape more inclusive and comprehensive clinical research, supplementing traditional clinical trial findings.} }
Endnote
%0 Conference Paper %T Beyond Clinical Trials: Using Real World Evidence to Investigate Heterogeneous, Time-Varying Treatment Effects %A Isabel Chien %A Cliff Wong %A Zelalem Gero %A Jaspreet Bagga %A Risa Ueno %A Richard E. Turner %A Roshanthi K. Weerasinghe %A Brian Piening %A Tristan Naumann %A Carlo Bifulco %A Hoifung Poon %A Javier González Hernández %B Proceedings of the 9th Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2024 %E Kaivalya Deshpande %E Madalina Fiterau %E Shalmali Joshi %E Zachary Lipton %E Rajesh Ranganath %E Iñigo Urteaga %F pmlr-v252-chien24a %I PMLR %U https://proceedings.mlr.press/v252/chien24a.html %V 252 %X Randomized controlled trials (RCTs), though essential for evaluating the efficacy of novel treatments, are costly and time-intensive. Due to strict eligibility criteria, RCTs may not adequately represent diverse patient populations, leading to equity issues and limited generalizability. Additionally, conventional trial analysis methods are limited by strict assumptions and biases. Real-world evidence (RWE) offers a promising avenue to explore treatment effects beyond trial settings, addressing gaps in representation and providing additional insights into patient outcomes over time. We introduce TRIALSCOPE-X and TRIALSCOPE-XL, machine learning pipelines designed to analyze treatment outcomes using RWE by mitigating biases that arise from observational data and addressing the limitations of conventional methods. We estimate causal, time-varying treatment effects across heterogeneous patient populations and varied timeframes. Preliminary results investigating the treatment benefit of Keytruda, a widely-used cancer immunotherapy drug, demonstrate the utility of our methods in evaluating treatment outcomes under novel settings and uncovering potential disparities. Our findings highlight the potential of RWE-based analysis to provide data-driven insights that inform evidence-based medicine and shape more inclusive and comprehensive clinical research, supplementing traditional clinical trial findings.
APA
Chien, I., Wong, C., Gero, Z., Bagga, J., Ueno, R., Turner, R.E., Weerasinghe, R.K., Piening, B., Naumann, T., Bifulco, C., Poon, H. & Hernández, J.G.. (2024). Beyond Clinical Trials: Using Real World Evidence to Investigate Heterogeneous, Time-Varying Treatment Effects. Proceedings of the 9th Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 252 Available from https://proceedings.mlr.press/v252/chien24a.html.

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