Department of Health Care Policy, Harvard Medical School, Boston, MA 02115, USA.
Stat Med. 2011 May 10;30(10):1137-56. doi: 10.1002/sim.4201. Epub 2011 Feb 22.
In designed longitudinal studies, information from the same set of subjects are collected repeatedly over time. The longitudinal measurements are often subject to missing data which impose an analytic challenge. We propose a functional multiple imputation approach modeling longitudinal response profiles as smooth curves of time under a functional mixed effects model. We develop a Gibbs sampling algorithm to draw model parameters and imputations for missing values, using a blocking technique for an increased computational efficiency. In an illustrative example, we apply a multiple imputation analysis to data from the Panel Study of Income Dynamics and the Child Development Supplement to investigate the gradient effect of family income on children's health status. Our simulation study demonstrates that this approach performs well under varying modeling assumptions on the time trajectory functions and missingness patterns.
在设计的纵向研究中，同一组受试者的信息会随着时间的推移被反复收集。纵向测量数据通常会存在缺失，这给分析带来了挑战。我们提出了一种功能多重插补方法，即在功能混合效应模型下，将纵向响应曲线建模为平滑的时间曲线。我们开发了一种 Gibbs 抽样算法来抽取模型参数和缺失值的插补值，使用阻塞技术来提高计算效率。在一个说明性的例子中，我们将多重插补分析应用于来自收入动态面板研究和儿童发展补充调查的数据，以研究家庭收入对儿童健康状况的梯度效应。我们的模拟研究表明，在时间轨迹函数和缺失模式的不同建模假设下，这种方法表现良好。
