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Application of multiple imputation using the two-fold fully conditional specification algorithm in longitudinal clinical data.
The Stata journal Pub Date : 2014-04-01
Catherine Welch 1 , Jonathan Bartlett 2 , Irene Petersen 3
Affiliation  

Electronic health records of longitudinal clinical data are a valuable resource for health care research. One obstacle of using databases of health records in epidemiological analyses is that general practitioners mainly record data if they are clinically relevant. We can use existing methods to handle missing data, such as multiple imputation (mi), if we treat the unavailability of measurements as a missing-data problem. Most software implementations of MI do not take account of the longitudinal and dynamic structure of the data and are difficult to implement in large databases with millions of individuals and long follow-up. Nevalainen, Kenward, and Virtanen (2009, Statistics in Medicine 28: 3657-3669) proposed the two-fold fully conditional specification algorithm to impute missing data in longitudinal data. It imputes missing values at a given time point, conditional on information at the same time point and immediately adjacent time points. In this article, we describe a new command, twofold, that implements the two-fold fully conditional specification algorithm. It is extended to accommodate MI of longitudinal clinical records in large databases.

中文翻译:

使用双重完全条件规范算法的多重插补在纵向临床数据中的应用。

纵向临床数据的电子健康记录是医疗保健研究的宝贵资源。在流行病学分析中使用健康记录数据库的一个障碍是,全科医生主要记录与临床相关的数据。如果我们将测量的不可用性视为缺失数据问题,我们可以使用现有方法来处理缺失数据,例如多重插补 (mi)。MI的大多数软件实现都没有考虑到数据的纵向和动态结构,难以在具有数百万个人和长时间跟踪的大型数据库中实现。Nevalainen、Kenward 和 Virtanen (2009, Statistics in Medicine 28: 3657-3669) 提出了二重完全条件规范算法来估算纵向数据中的缺失数据。它以同一时间点和紧邻时间点的信息为条件,估算给定时间点的缺失值。在本文中,我们描述了一个新命令,double,它实现了二重完全条件指定算法。它被扩展以适应大型数据库中纵向临床记录的 MI。
更新日期:2019-11-01
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