使用联合变量重要性图优先确定观察性研究设计的变量
Prioritizing Variables for Observational Study Design using the Joint Variable Importance Plot
AI总结：
提出联合变量重要性图，结合结果关联与标准化均值差及偏差曲线指导观察性研究的变量优先级排序，并为匹配和加权方法提供调参推荐值。
摘要：
关于治疗效果的观察性研究需要对混杂变量进行调整。然而，因果推断方法通常无法在所有测量的基线变量上实现完美调整，并且关于应优先考虑哪些变量往往存在模糊性。仅基于治疗不平衡的标准优先级排序方法忽略了变量与结果之间的关系。我们提出联合变量重要性图来指导观察性研究的变量优先级排序。由于并非所有变量都与结果同等相关，该图添加了结果关联，以与标准化均值差共同量化潜在混杂。为了增强图上具有不同混杂关系的变量之间的比较，我们还推导并绘制了偏差曲线。使用该图进行变量优先级排序可以为许多现有匹配和加权方法中的调参参数生成推荐值。我们在平衡约束匹配研究的设计中展示了联合变量重要性图的使用，该研究旨在评估服用抗糖尿病药物glyburide是否会增加患有妊娠期糖尿病的孕妇剖宫产的发生率。
英文摘要：
Observational studies of treatment effects require adjustment for confounding variables. However, causal inference methods typically cannot deliver perfect adjustment on all measured baseline variables, and there is often ambiguity about which variables should be prioritized. Standard prioritization methods based on treatment imbalance alone neglect variables' relationships with the outcome. We propose the joint variable importance plot to guide variable prioritization for observational studies. Since not all variables are equally relevant to the outcome, the plot adds outcome associations to quantify the potential confounding jointly with the standardized mean difference. To enhance comparisons on the plot between variables with different confounding relationships, we also derive and plot bias curves. Variable prioritization using the plot can produce recommended values for tuning parameters in many existing matching and weighting methods. We showcase the use of the joint variable importance plots in the design of a balance-constrained matched study to evaluate whether taking an antidiabetic medication, glyburide, increases the incidence of C-section delivery among pregnant individuals with gestational diabetes.
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。
