TreatmentEstimatoR: a Dashboard for Estimating Treatment Effects from Observational Health Data

TL;DR

TreatmentEstimatoR is an accessible R Shiny dashboard that enables clinicians and researchers to estimate treatment effects from observational health data without programming, supporting multiple outcome types and comprehensive analysis features.

Contribution

It introduces a user-friendly, code-free dashboard for estimating treatment effects from observational health data, integrating multiple algorithms and flexible covariate selection.

Findings

Supports binary, continuous, and time-to-event outcomes.

Provides multiple effect estimates simultaneously.

Includes model performance metrics and exploratory tools.

Abstract

Observational health data can be leveraged to measure the real-world use and potential benefits or risks of existing medical interventions. However, lack of programming proficiency and advanced knowledge of causal inference methods excludes some clinicians and non-computational researchers from performing such analyses. Code-free dashboard tools provide accessible means to estimate and visualize treatment effects from observational health data. We present TreatmentEstimatoR, an R Shiny dashboard that facilitates the estimation of treatment effects from observational data without any programming knowledge required. The dashboard provides effect estimates from multiple algorithms simultaneously and accommodates binary, continuous, and time-to-event outcomes. TreatmentEstimatoR allows for flexible covariate selection for treatment and outcome models, comprehensive model performance metrics, and an exploratory data analysis tool. TreatmentEstimatoR is available at https://github.com/CollinSakal/TreatmentEstimatoR. We provide full installation instructions and detailed vignettes for how to best use the dashboard.