JOINTVIP：在R语言中通过联合变量重要性图确定观察性研究设计中的变量优先级
JOINTVIP: Prioritizing variables in observational study design with joint variable importance plot in R
AI总结：
针对观察性研究中变量多、重要性不均导致难以平衡的问题，提出R包jointVIP，通过量化可视化变量与处理及结局的关联，指导变量调整优先级以辅助因果效应估计。
摘要：
可信的因果效应估计要求处理组受试者与对照组在其他方面具有相似性。在电子健康记录分析等观察性场景中，这一点无法得到保证。研究者必须平衡背景变量，使它们在处理组和对照组中分布相似。常用方法包括匹配（将个体划分为小型同质组）或加权（调高或调低个体权重）以构建相似的特征分布。然而，若测量变量众多，且并非所有变量对结局的重要性都相同，构建完全相同的分布可能无法实现。jointVIP（联合变量重要性图）包通过量化并可视化每个变量与处理因素及结局的关联，指导研究者确定哪些变量应优先纳入调整。
英文摘要：
Credible causal effect estimation requires treated subjects and controls to be otherwise similar. In observational settings, such as analysis of electronic health records, this is not guaranteed. Investigators must balance background variables so they are similar in treated and control groups. Common approaches include matching (grouping individuals into small homogeneous sets) or weighting (upweighting or downweighting individuals) to create similar profiles. However, creating identical distributions may be impossible if many variables are measured, and not all variables are of equal importance to the outcome. The joint variable importance plot (jointVIP) package to guides decisions about which variables to prioritize for adjustment by quantifying and visualizing each variable's relationship to both treatment and outcome.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
