CSTEapp: An interactive R-Shiny application of the covariate-specific treatment effect curve for visualizing individualized treatment rule
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912546469969920 |
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| author | Zhou, Yi Deng, Yuhao Tian, Yu-Shi Wu, Peng Hu, Wenjie Wang, Haoxiang Steyerberg, Ewout Zhou, Xiao-Hua |
| author_facet | Zhou, Yi Deng, Yuhao Tian, Yu-Shi Wu, Peng Hu, Wenjie Wang, Haoxiang Steyerberg, Ewout Zhou, Xiao-Hua |
| contents | In precision medicine, deriving the individualized treatment rule (ITR) is crucial for recommending the optimal treatment based on patients' baseline covariates. The covariate-specific treatment effect (CSTE) curve presents a graphical method to visualize an ITR within a causal inference framework. Recent advancements have enhanced the causal interpretation of the CSTE curves and provided methods for deriving simultaneous confidence bands for various study types. To facilitate the implementation of these methods and make ITR estimation more accessible, we developed CSTEapp, a web-based application built on the R Shiny framework. CSTEapp allows users to upload data and create CSTE curves through simple point and click operations, making it the first application for estimating the ITRs. CSTEapp simplifies the analytical process by providing interactive graphical user interfaces with dynamic results, enabling users to easily report optimal treatments for individual patients based on their covariates information. Currently, CSTEapp is applicable to studies with binary and time-to-event outcomes, and we continually expand its capabilities to accommodate other outcome types as new methods emerge. We demonstrate the utility of CSTEapp using real-world examples and simulation datasets. By making advanced statistical methods more accessible, CSTEapp empowers researchers and practitioners across various fields to advance precision medicine and improve patient outcomes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_15265 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CSTEapp: An interactive R-Shiny application of the covariate-specific treatment effect curve for visualizing individualized treatment rule Zhou, Yi Deng, Yuhao Tian, Yu-Shi Wu, Peng Hu, Wenjie Wang, Haoxiang Steyerberg, Ewout Zhou, Xiao-Hua Computation Applications In precision medicine, deriving the individualized treatment rule (ITR) is crucial for recommending the optimal treatment based on patients' baseline covariates. The covariate-specific treatment effect (CSTE) curve presents a graphical method to visualize an ITR within a causal inference framework. Recent advancements have enhanced the causal interpretation of the CSTE curves and provided methods for deriving simultaneous confidence bands for various study types. To facilitate the implementation of these methods and make ITR estimation more accessible, we developed CSTEapp, a web-based application built on the R Shiny framework. CSTEapp allows users to upload data and create CSTE curves through simple point and click operations, making it the first application for estimating the ITRs. CSTEapp simplifies the analytical process by providing interactive graphical user interfaces with dynamic results, enabling users to easily report optimal treatments for individual patients based on their covariates information. Currently, CSTEapp is applicable to studies with binary and time-to-event outcomes, and we continually expand its capabilities to accommodate other outcome types as new methods emerge. We demonstrate the utility of CSTEapp using real-world examples and simulation datasets. By making advanced statistical methods more accessible, CSTEapp empowers researchers and practitioners across various fields to advance precision medicine and improve patient outcomes. |
| title | CSTEapp: An interactive R-Shiny application of the covariate-specific treatment effect curve for visualizing individualized treatment rule |
| topic | Computation Applications |
| url | https://arxiv.org/abs/2508.15265 |