CSTEapp: An interactive R-Shiny application of the covariate-specific treatment effect curve for visualizing individualized treatment rule
Yi Zhou, Yuhao Deng, Yu-Shi Tian, Peng Wu, Wenjie Hu, Haoxiang Wang, Ewout Steyerberg, Xiao-Hua Zhou

TL;DR
CSTEapp is an interactive R-Shiny tool that visualizes covariate-specific treatment effects to help derive individualized treatment rules in precision medicine, making advanced causal inference methods accessible and user-friendly.
Contribution
It introduces the first web-based application for estimating and visualizing individualized treatment rules using covariate-specific treatment effect curves.
Findings
Enables easy creation of CSTE curves via a graphical interface.
Supports analysis of binary and time-to-event outcomes.
Demonstrates utility with real-world and simulated data.
Abstract
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…
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Taxonomy
TopicsAdvanced Causal Inference Techniques · Mental Health Research Topics · Statistical Methods and Bayesian Inference
