Kavli Affiliate: Yi Zhou
| First 5 Authors: , , , ,
| Summary:
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.
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