Background High-dimensional propensity scoring (HDPS) is a method for empirically identifying potential confounders within large healthcare databases such as administrative claims data. However, this method has not yet been applied to large national health surveys such as the National Health and Aging Trends Study (NHATS), an ongoing nationally representative survey of older adults in the United States and important resource in gerontology research. Methods In this Research Practice article, we present an overview of HDPS and describe the specific data transformation steps and analytic considerations needed to apply it to national health surveys. We applied HDPS within NHATS to investigate the association between self-reported visual difficulty and incident dementia, comparing HDPS to conventional confounder selection methods. Results Among 7 207 dementia-free NHATS Wave 1 respondents, 528 (7.3%) had self-reported visual difficulty. In an unadjusted discrete time proportional hazards model accounting for the complex survey design of NHATS, self-reported visual difficulty was strongly associated with incident dementia (odds ratio [OR] 2.34, 95% confidence interval [CI]: 1.95–2.81). After adjustment for standard investigator-selected covariates via inverse probability weighting, the magnitude of this association decreased, but evidence of an association remained (OR 1.44, 95% CI: 1.11–1.85). Adding 75 HDPS-prioritized variables to the investigator-selected propensity score model resulted in further attenuation of the association between visual impairment and dementia (OR 0.94, 95% CI: 0.70–1.23). Conclusions HDPS can be successfully applied to national health surveys such as NHATS and may improve confounder adjustment. We hope developing this framework will encourage future consideration of HDPS in this setting.

中文翻译：

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高维倾向评分方法在全国健康和老龄化趋势研究中的应用


背景 高维倾向评分 （HDPS） 是一种在大型医疗保健数据库（如行政索赔数据）中凭经验识别潜在混杂因素的方法。然而，这种方法尚未应用于大型全国性健康调查，例如国家健康与老龄化趋势研究 （NHATS），这是一项正在进行的具有全国代表性的美国老年人调查，也是老年学研究的重要资源。方法 在这篇研究实践文章中，我们概述了 HDPS，并描述了将其应用于国家健康调查所需的具体数据转换步骤和分析注意事项。我们在 NHATS 中应用 HDPS 来研究自我报告的视觉困难与事件痴呆之间的关联，将 HDPS 与传统的混杂选择方法进行比较。结果 在 7 207 名无痴呆 NHATS Wave 1 受访者中，528 名 （7.3%） 有自我报告的视觉困难。在考虑 NHATS 复杂调查设计的未调整离散时间比例风险模型中，自我报告的视觉困难与痴呆事件密切相关 （比值比 [OR] 2.34,95% 置信区间 [CI]： 1.95-2.81）。通过逆概率加权调整标准研究者选择的协变量后，这种关联的幅度降低，但关联的证据仍然存在 （OR 1.44,95% CI： 1.11–1.85）。在研究者选择的倾向评分模型中添加 75 个 HDPS 优先变量导致视力障碍与痴呆之间的关联进一步减弱 （OR 0.94,95% CI： 0.70-1.23）。结论 HDPS 可以成功应用于 NHATS 等国家健康调查，并可能改善混杂因素调整。 我们希望开发此框架将鼓励将来在这种情况下考虑 HDPS。