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

Catherine Welch (), Jonathan Bartlett () and Irene Petersen ()
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Catherine Welch: University College London
Jonathan Bartlett: London School of Hygiene & Tropical Medicine
Irene Petersen: University College London

Stata Journal, 2014, vol. 14, issue 2, 418-431

Abstract: 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. Copyright 2014 by StataCorp LP.

Keywords: twofold; multiple imputation; longitudinal data (search for similar items in EconPapers)
Date: 2014
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