Application of multiple imputation using the two-fold fully conditional specification algorithm in longitudinal clinical data
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.
View all articles by these authors:
Catherine Welch, Jonathan Bartlett, Irene Petersen
View all articles with these keywords:
twofold, multiple imputation, longitudinal data
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