1 University of Wisconsin-Madison, USA.
2 Johns Hopkins University, Baltimore, MD, USA.
J Aging Health. 2019 Apr;31(4):685-708. doi: 10.1177/0898264317747079. Epub 2017 Dec 14.
We offer a strategy for quantifying the impact of mortality and attrition on inferences from later-life health trajectory models.
Using latent class growth analysis (LCGA), we identify functional limitation trajectory classes in the Health and Retirement Study. We compare results from complete case and full information maximum likelihood (FIML) analyses, and demonstrate a method for producing upper- and lower-bound estimates of the impact of attrition on results.
LCGA inferences vary substantially depending on the handling of missing data. For older adults who die during the follow-up period, the widely used FIML approach may underestimate functional limitations by up to 20%.
The most commonly used approaches to handling missing data likely underestimate the extent of poor health in aging populations. Although there is no single solution for nonrandom missingness, we show that bounding estimates can help analysts to better characterize patterns of health in later life.
我们提供了一种策略，用于量化死亡率和损耗对老年健康轨迹模型推论的影响。
使用潜在类别增长分析（LCGA），我们在健康与退休研究中确定了功能限制轨迹类别。我们比较了完整案例和完全信息极大似然（FIML）分析的结果，并展示了一种产生损耗对结果影响的上下限估计的方法。
LCGA 推论结果因缺失数据的处理方式而有很大差异。对于在随访期间死亡的老年人，广泛使用的 FIML 方法可能会低估高达 20%的功能限制。
目前常用的处理缺失数据的方法可能低估了老年人群中健康状况不佳的程度。虽然对于非随机缺失没有单一的解决方案，但我们表明，边界估计可以帮助分析师更好地描述晚年的健康模式。
