论文标题
治疗试验器：仪表板，用于估算观察性健康数据的治疗效果
TreatmentEstimatoR: a Dashboard for Estimating Treatment Effects from Observational Health Data
论文作者
论文摘要
可以利用观察健康数据来衡量现实的使用以及现有医疗干预措施的潜在益处或风险。但是，缺乏针对因果推理方法的编程水平和高级知识，排除了一些临床医生和非竞争研究人员进行此类分析。无代码的仪表板工具提供了可访问的手段，以估算和可视化观察性健康数据的治疗效果。我们提出了治疗试剂，这是一个闪亮的仪表板，可促进观察数据中的治疗效果的估计，而无需任何编程知识。仪表板同时提供了来自多种算法的效果估计，并适应二进制，连续和事件的结果。治疗试验器可为治疗和结果模型，全面的模型性能指标以及探索性数据分析工具提供灵活的协变量选择。治疗试验器可从https://github.com/collinsakal/treatmentestimator获得。我们为如何最好地使用仪表板提供完整的安装说明和详细的小插图。
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.
