The Potential of High‐Dimensional Propensity Scores in Health Services Research: An Exemplary Study on the Quality of Care for Elective Percutaneous Coronary Interventions
本研究评估高维倾向评分（HDPS）在基于行政健康保险数据分析护理质量时控制残余混杂的潜力，发现HDPS通过限制使队列更可比，但结果不能推广至全人群。
OBJECTIVE: Evaluating the potential of the high-dimensional propensity score (HDPS) to control for residual confounding in studies analyzing quality of care based on administrative health insurance data. DATA SOURCE: Secondary data from 2004 to 2009 from three German statutory health insurance providers. STUDY DESIGN: We conducted a retrospective cohort study in patients with elective percutaneous coronary interventions (PCIs) and compared the mortality risk between the in- and outpatient setting using Cox regression. Adjustment for predefined confounders was performed using conventional propensity score (PS) techniques. Further, an HDPS was calculated based on predefined and empirically selected confounders from the database. PRINCIPAL FINDINGS: Conventional PS methods showed a decreased mortality risk for outpatient compared to inpatient PCIs, while trimming of patients with nonoverlap in the HDPS distribution and weighting resulted in a comparable risk. Most comorbidities were less prevalent in the HDPS-trimmed population compared to the original one. CONCLUSION: The HDPS methodology may reduce residual confounding by rendering the studied cohort more comparable through restriction. However, results cannot be generalized for the entire study population. To provide unbiased results, full assessment of all unmeasured confounders from proxy information in the database would be necessary.