PDF(Pubmed)
Abstract：
Nearly 300,000 older adults experience a hip fracture every year, the majority of which occur following a fall. Unfortunately, recovery after fall-related trauma such as hip fracture is poor, where older adults diagnosed with Alzheimer\'s disease and related dementia (ADRD) spend a particularly long time in hospitals or rehabilitation facilities during the post-operative recuperation period. Because older adults value functional recovery and spending time at home versus facilities as key outcomes after hospitalization, identifying factors that influence days spent at home after hospitalization is imperative. While several individual-level factors have been identified, the characteristics of the treating hospital have recently been identified as contributors. However, few methodological rigorous approaches are available to help overcome potential sources of bias such as hospital-level unmeasured confounders, informative hospital size, and loss to follow-up due to death. This article develops a useful tool equipped with unsupervised learning to simultaneously handle statistical complexities that are often encountered in health services research, especially when using large administrative claims databases. The proposed estimator has a closed form, thus only requiring light computation load in a large-scale study. We further develop its asymptotic properties with stabilized inference assisted by unsupervised clustering. Extensive simulation studies demonstrate superiority of the proposed estimator compared to existing estimators.
摘要：
每年有近30万老年人经历髋部骨折,其中大部分发生在跌倒之后。不幸的是,跌倒相关创伤如髋部骨折后恢复不良，被诊断患有阿尔茨海默病和相关痴呆（ADRD）的老年人在术后休养期间在医院或康复机构中度过的时间特别长。因为老年人重视功能恢复和花时间在家里与设施作为住院后的关键结果，确定影响住院后在家度过的天数的因素是必要的。虽然已经确定了几个个人层面的因素，治疗医院的特征最近被确定为贡献者。然而,很少有方法严格的方法可以帮助克服潜在的偏见来源，如医院层面的未测量的混杂因素，翔实的医院规模，以及因死亡而失去随访。本文开发了一种有用的工具，该工具配备了无监督学习，可以同时处理在卫生服务研究中经常遇到的统计复杂性。尤其是在使用大型行政索赔数据库时。所提出的估计器具有封闭形式，因此，在大规模研究中只需要轻计算负荷。我们通过无监督聚类辅助的稳定推理进一步发展了其渐近性质。大量的仿真研究证明了所提出的估计器与现有估计器相比的优越性。
