TreatmentEstimatoR: a Dashboard for Estimating Treatment Effects from Observational Health Data
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.
Keywords
Cite
@article{arxiv.2203.10458,
title = {TreatmentEstimatoR: a Dashboard for Estimating Treatment Effects from Observational Health Data},
author = {Collin Sakal and Hon Hwang and Juan C Quiroz and Blanca Gallego},
journal= {arXiv preprint arXiv:2203.10458},
year = {2022}
}