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Imputing missing repeated measures data: how should we proceed?

Peter Elliott1, Graeme Hawthorne

  • 1Department of Psychiatry, Australian Centre for Posttraumatic Mental Health, The University of Melbourne, PO Box 5444, West Heidelberg, Melbourne, Victoria 3081, Australia.

The Australian and New Zealand Journal of Psychiatry
|July 6, 2005
PubMed
Summary
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For repeated measures data, the closest match imputation method is recommended for handling missing data. Other methods like last value carried forward (LVCF) and listwise deletion performed poorly.

Area of Science:

  • Statistics
  • Biostatistics
  • Data Science

Background:

  • Missing data is a common challenge in statistical analyses, particularly with repeated measures.
  • Several imputation methods exist, but their efficacy varies significantly.

Purpose of the Study:

  • To compare the performance of six different missing data imputation methods for statistical tests on repeated measures data.
  • To identify the most reliable and efficacious imputation method for such data.

Main Methods:

  • Six imputation methods were compared: listwise deletion, last value carried forward (LVCF), standardized score imputation, regression, and two closest match variations.
  • Efficacy was assessed using t-tests on randomly selected samples (n=804) with varying missingness levels.
  • Performance was measured by the closeness of estimated t-values to true values from complete data.

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Main Results:

  • Closest match imputation methods, using either a single closest individual or the average of four closest individuals, demonstrated reliable and efficacious performance.
  • Last value carried forward (LVCF) and standardized score imputation methods performed poorly, despite their common use.
  • Listwise deletion was found to be an inefficient method for handling missing data in this context.

Conclusions:

  • The closest match imputation method is recommended for researchers dealing with missing data in repeated measures studies.
  • Given the poor performance of listwise deletion, which is a default in many software packages, these findings have significant implications for statistical practice.