Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts 02120, USA.
Am J Epidemiol. 2011 Jun 15;173(12):1404-13. doi: 10.1093/aje/kwr001. Epub 2011 May 20.
To reduce bias by residual confounding in nonrandomized database studies, the high-dimensional propensity score (hd-PS) algorithm selects and adjusts for previously unmeasured confounders. The authors evaluated whether hd-PS maintains its capabilities in small cohorts that have few exposed patients or few outcome events. In 4 North American pharmacoepidemiologic cohort studies between 1995 and 2005, the authors repeatedly sampled the data to yield increasingly smaller cohorts. They identified potential confounders in each sample and estimated both an hd-PS that included 0-500 covariates and treatment effects adjusted by decile of hd-PS. For sensitivity analyses, they altered the variable selection process to use zero-cell correction and, separately, to use only the variables' exposure association. With >50 exposed patients with an outcome event, hd-PS-adjusted point estimates in the small cohorts were similar to the full-cohort values. With 25-50 exposed events, both sensitivity analyses yielded estimates closer to those obtained in the full data set. Point estimates generally did not change as compared with the full data set when selecting >300 covariates for the hd-PS. In these data, using zero-cell correction or exposure-based covariate selection allowed hd-PS to function robustly with few events. hd-PS is a flexible analytical tool for nonrandomized research across a range of study sizes and event frequencies.
为了减少非随机数据库研究中残余混杂引起的偏倚，高维倾向评分（hd-PS）算法选择和调整以前未测量的混杂因素。作者评估了在暴露患者较少或结局事件较少的小队列中，hd-PS 是否仍具有其能力。在 1995 年至 2005 年期间进行的 4 项北美的药物流行病学队列研究中，作者反复对数据进行抽样，以产生越来越小的队列。他们在每个样本中确定了潜在的混杂因素，并估计了包括 0-500 个协变量的 hd-PS 和按 hd-PS 十分位数调整的治疗效果。对于敏感性分析，他们改变了变量选择过程，使用零单元校正，并且分别仅使用变量的暴露关联。在有>50 名暴露患者发生结局事件的情况下，小队列中的 hd-PS 调整后的点估计与全队列值相似。在有 25-50 名暴露事件的情况下，两种敏感性分析都得出了更接近全数据集的估计值。与全数据集相比，当为 hd-PS 选择>300 个协变量时，点估计值通常没有变化。在这些数据中，使用零单元校正或基于暴露的协变量选择允许 hd-PS 在事件较少的情况下稳健地运行。hd-PS 是一种灵活的分析工具，适用于各种研究规模和事件频率的非随机研究。
