Department of Methodology and Statistics, Utrecht University, The Netherlands.
Department of Methodology and Statistics, Tilburg University, The Netherlands.
Multivariate Behav Res. 2022 Mar-May;57(2-3):513-523. doi: 10.1080/00273171.2021.1912582. Epub 2021 May 7.
Multiple imputation is a recommended technique to deal with missing data. We study the problem where the investigator has already created imputations before the arrival of the next wave of data. The newly arriving data contain missing values that need to be imputed. The standard method (RE-IMPUTE) is to combine the new and old data before imputation, and re-impute all missing values in the combined data. We study the properties of two methods that impute the missing data in the new part only, thus preserving the historic imputations. Method NEST multiply imputes the new data conditional on each filled-in old data times. Method APPEND is the special case of NEST with thus appending each filled-in data by single imputation. We found that NEST and APPEND have the same validity as RE-IMPUTE for monotone missing data-patterns. NEST and APPEND also work well when relations within waves are stronger than between waves and for moderate percentages of missing data. We do not recommend the use of NEST or APPEND when relations within time points are weak and when associations between time points are strong.
多重插补是处理缺失数据的推荐技术。我们研究了这样一个问题：在新一波数据到来之前，调查人员已经创建了插补值。新到达的数据包含需要插补的缺失值。标准方法（RE-IMPUTE）是在插补之前将新数据和旧数据合并，并对合并后的数据中的所有缺失值进行重新插补。我们研究了两种仅对新数据中缺失数据进行插补的方法的特性，从而保留了历史插补值。方法 NEST 对每个填充的旧数据进行多次插补新数据的条件。方法 APPEND 是 NEST 的特殊情况，即通过单一插补对每个填充的数据进行附加。我们发现，对于单调缺失数据模式，NEST 和 APPEND 的有效性与 RE-IMPUTE 相同。当波内关系强于波间关系且缺失数据百分比适中时，NEST 和 APPEND 也能很好地工作。当时间点内的关系较弱且时间点之间的关联较强时，我们不建议使用 NEST 或 APPEND。
