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

Author

Listed:
  • Catherine Welch

    (University College London)

  • Jonathan Bartlett

    (London School of Hygiene & Tropical Medicine)

  • Irene Petersen

    (University College London)

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.

Suggested Citation

  • Catherine Welch & Jonathan Bartlett & Irene Petersen, 2014. "Application of multiple imputation using the two-fold fully conditional specification algorithm in longitudinal clinical data," Stata Journal, StataCorp LP, vol. 14(2), pages 418-431, June.
    • Handle: RePEc:tsj:stataj:v:14:y:2014:i:2:p:418-431
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    Cited by:

    1. Kravitz-Wirtz, Nicole, 2016. "A discrete-time analysis of the effects of more prolonged exposure to neighborhood poverty on the risk of smoking initiation by age 25," Social Science & Medicine, Elsevier, vol. 148(C), pages 79-92.
    2. Antonio Zinilli, 2021. "Imputation methods for estimating public R&D funding: evidence from longitudinal data," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(2), pages 707-729, April.

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