Harel Ofer, Hofer Scott M, Hoffman Lesa, Pedersen Nancy L, Johansson Boo
Department of Statistics, University of Connecticut, Storrs, Connecticut 06269-4120, USA.
Exp Aging Res. 2007 Apr-Jun;33(2):187-203. doi: 10.1080/03610730701239004.
A major challenge for inference regarding aging-related change in longitudinal studies is that of study attrition and population mortality. Inferences in longitudinal studies can account for attrition and mortality-related change as distinct processes, but this is made difficult when follow-up of all individuals (i.e., age at death) is not complete. This is a common problem because most longitudinal studies of aging either have incomplete follow-up or are still collecting data on subsequent outcomes, including time of death. A statistical approach is suggested for including time-to-death as a predictor in models with incomplete follow-up using a two-stage multiple-imputation procedure. An empirical example using data from the OCTO-Twin study is presented that shows the utility of his procedure for making inferences conditional on mortality when mortality data are incomplete.
纵向研究中关于衰老相关变化的推断面临的一个主要挑战是研究对象的流失和总体死亡率问题。纵向研究中的推断可以将流失和与死亡率相关的变化视为不同的过程，但当对所有个体的随访（即死亡年龄）不完整时，这就变得困难了。这是一个常见问题，因为大多数关于衰老的纵向研究要么随访不完整，要么仍在收集包括死亡时间在内的后续结果数据。本文提出了一种统计方法，使用两阶段多重填补程序，将死亡时间作为预测变量纳入随访不完整的模型中。文中给出了一个使用来自八旬双胞胎研究数据的实证例子，该例子表明，当死亡率数据不完整时，此程序在基于死亡率进行推断方面的效用。
