Department of Methodology and Statistics, CAPHRI Care and Public Health Research Institute, Maastricht University, Maastricht, The Netherlands.
Department of Methodology and Statistics, CAPHRI Care and Public Health Research Institute, Maastricht University, Maastricht, The Netherlands.
J Clin Epidemiol. 2018 Oct;102:107-114. doi: 10.1016/j.jclinepi.2018.06.006. Epub 2018 Jun 28.
We provide guidelines for handling the most common missing data problems in repeated measurements in observational studies and deal with practicalities in producing imputations when there are many partly missing time-varying variables and repeated measurements.
The Maastricht Study on long-term dementia care environments was used as a case study. The data contain 84 momentary assessments for each of 115 participants. A continuous outcome and several time-varying covariates were involved containing missing observations varying from 4% to 25% per time point. A multiple imputation procedure is advocated with restrictions imposed on the relation within and between partially missing variables over time.
Multiple imputation is a better approach to deal with missing observations in both outcome and independent variables. Furthermore, using the statistical package R-MICE, it is possible to deal with the limitations of current statistical software in imputation of missing observations in more complex data.
In observational studies, the direct likelihood approach (i.e., the standard longitudinal data methods) is sufficient to obtain valid inferences in the presence of missing data only in the outcome. In contrast, multiple imputation is required when dealing with partly missing time-varying covariates and repeated measurements.
我们提供了处理观察性研究中重复测量中最常见缺失数据问题的指南，并处理了当存在许多部分缺失的时变变量和重复测量时产生插补的实际问题。
马斯特里赫特长期痴呆护理环境研究被用作案例研究。数据包含 115 名参与者的每个参与者 84 次瞬间评估。涉及连续结果和几个时变协变量，每个时间点的缺失观察值从 4%到 25%不等。提倡使用多重插补程序，并对随时间部分缺失变量之间和之间的关系施加限制。
多重插补是处理因变量和自变量中缺失观察值的更好方法。此外，使用 R-MICE 统计软件包，可以解决当前统计软件在更复杂数据中插补缺失观察值的局限性。
在观察性研究中，直接似然方法（即标准纵向数据方法）在仅存在缺失数据的情况下足以对结果进行有效推断。相比之下，当处理部分缺失的时变协变量和重复测量时，需要进行多重插补。
