Multiple Imputation of Multilevel Data 来自 ResearchGate 喜欢 0 阅读量: 88 作者: SV Buuren 摘要: 10.1 IntroductIon In the early days of multilevel analysis, Goldstein wrote: "We shall require and assume that all the necessary data at each level are available" (Goldstein, 1987). Despite the ...
Unfortunately, this MNAR situation is rather common, in observational studies, registries and other sources of real‐world data. While several imputation methods have been proposed for addressing individual studies when data are MNAR, their application and validity in large datasets with multilevel ...
We focus on performing multiple imputation by chained equations when data contain multiple incomplete multi-item scales. Recent authors have proposed imputing such data at the level of the individual item, but this can lead to infeasibly large imputation models. We use data gathered from a large ...
Multiple Imputation for Longitudinal Data Under a Bayesian Multilevel ModelLongitudinal dataMissing dataMixed-modelsMultiple imputationPattern-mixture modelsPrimary 62F40Secondary 62P10In this article, I establish a connection between Bayesian random-coefficient pattern-mixture models that were described by ...
Joint modeling and chained equations imputation are the principal imputation frameworks for single-level data, and both have multilevel counterparts. These approaches differ algorithmically and in their functionality; both are appropriate for simple random intercept analyses with normally distributed data, ...
Author(s): He, Ren | Advisor(s): Belin, Thomas R | Abstract: It is common in applied research to have large numbers of variables with mixed data types (continuous, binary, ordinal or nomial) measures on a modest number of cases. Also, even a simple imputation model can be overparamete...
任意缺失数据(arbitrary data missing, generalized pattern of missing data),是指数据集中的缺失模式没有特定的结构或规律,是指数据集中的缺失模式没有特定的结构或规律,数据缺失可以在任何时间点、任何变量上发生。这种是最常见的也是处理最麻烦的。 单调缺失数据(monotonic missing data,monotone missing data pattern...
that has not been investigated previously. We develop communication-efficient distributed multiple imputation methods for incomplete data that are horizontally partitioned. Since subject-level data are not shared or transferred outside of each site in the proposed methods, they enhance protection of ...
in a single step, estimate parameters using the imputed datasets, and combine results. Fit a linear model, logit model, Poisson model, multilevel model, survival model, or one of the many other supported models. Use themicommand, or let the Control Panel interface guide you through your enti...
A simulation study using the observed data is also conducted as part of the model diagnostics. Finally, some real data analyses are performed to compare the before and after imputation results. Published 2016. This article is a U.S. Government work and is in the public domain in the USA. ...