Nonparametric Bayesian Adjustment of Unmeasured Confounders in Cox Proportional Hazards Models
Cox 比例风险模型中未测混杂的非参数贝叶斯调整
In observational studies, unmeasured confounders present a crucial challenge in accurately estimating desired causal effects. To calculate the hazard ratio (HR) in Cox proportional hazard models for time-to-event outcomes, two-stage residual inclusion and limited information maximum likelihood are typically employed. However, these methods are known to entail difficulty in terms of potential bias of HR estimates and parameter identification. This study introduces a novel…