Application of multiple imputation using the two-fold fully conditional specification algorithm in longitudinal clinical data

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 han...

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Bibliographic Details
Published in:The Stata journal Vol. 14; no. 2; p. 418
Main Authors: Welch, Catherine, Bartlett, Jonathan, Petersen, Irene
Format: Journal Article
Language:English
Published: United States 01.06.2014
Subjects:
multiple imputation
twofold
st0345
longitudinal data
ISSN:1536-867X
Online Access:Get more information
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Summary: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, 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, , that implements the two-fold fully conditional specification algorithm. It is extended to accommodate MI of longitudinal clinical records in large databases.
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ISSN:1536-867X
DOI:10.1177/1536867x1401400213