Wolf Douglas A, Freedman Vicki A, Ondrich Jan I, Seplaki Christopher L, Spillman Brenda C
Aging Studies Institute, Syracuse University, New York.
Institute for Social Research, University of Michigan, Ann Arbor.
J Gerontol B Psychol Sci Soc Sci. 2015 Sep;70(5):745-52. doi: 10.1093/geronb/gbu182. Epub 2015 Mar 3.
Studies of late-life disablement typically address the role of advancing age as a factor in developing disability, and in some cases have pointed out the importance of time to death (TTD) in understanding changes in functioning. However, few studies have addressed both factors simultaneously, and none have dealt satisfactorily with the problem of missing data on TTD in panel studies.
We fit latent-class trajectory models of disablement using data from the Health and Retirement Study. Among survivors (~20% of the sample), TTD is unknown, producing a missing-data problem. We use an auxiliary regression equation to impute TTD and employ multiple imputation techniques to obtain final parameter estimates and standard errors.
Our best-fitting model has 3 latent classes. In all 3 classes, the probability of having a disability increases with nearness to death; however, in only 2 of the 3 classes is age associated with disability. We find gender, race, and educational differences in class-membership probabilities.
The model reveals a complex pattern of age- and time-dependent heterogeneity in late-life disablement. The techniques developed here could be applied to other phenomena known to depend on TTD, such as cognitive change, weight loss, and health care spending.
关于晚年残疾的研究通常探讨增龄作为残疾发展因素的作用，并且在某些情况下指出了死亡时间（TTD）在理解功能变化方面的重要性。然而，很少有研究同时涉及这两个因素，而且在面板研究中，没有一项研究能令人满意地处理TTD数据缺失的问题。
我们使用健康与退休研究的数据拟合残疾的潜在类别轨迹模型。在幸存者中（约占样本的20%），TTD是未知的，这就产生了数据缺失问题。我们使用一个辅助回归方程来估算TTD，并采用多重填补技术来获得最终的参数估计值和标准误差。
我们拟合度最佳的模型有3个潜在类别。在所有3个类别中，残疾的概率都随着接近死亡而增加；然而，在3个类别中只有2个类别中年龄与残疾有关。我们发现了在类别归属概率方面存在性别、种族和教育程度的差异。
该模型揭示了晚年残疾中年龄和时间依赖性异质性的复杂模式。这里开发的技术可以应用于其他已知依赖于TTD的现象，如认知变化、体重减轻和医疗保健支出。
