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Imputing missing laboratory results may return erroneous values because they are not missing at random.

Carl van Walraven1, Christopher McCudden2, Peter C Austin3

  • 1Professor of Medicine and Epidemiology & Community Medicine, University of Ottawa, Ontario, Canada; Senior Scientist, Ottawa Hospital Research Institute, Ontario, Canada; Senior Scientist, ICES, Ontario, Canada; Department of Medicine, University of Ottawa, Ontario, Canada; Department of Epidemiology & Community Medicine, University of Ottawa, Ottawa Hospital Research Institute, ICES (formerly Institute for Clinical Evaluative Sciences), Ontario, Canada.

Journal of Clinical Epidemiology
|December 17, 2022
PubMed
Summary
This summary is machine-generated.

Laboratory test results are often not missing at random, challenging imputation methods. Test abnormality increased with decreased testing likelihood, suggesting potential bias in imputed laboratory data.

Keywords:
Generalized estimating equationsImputationLaboratory testingMissing at randomMissing dataMultiple linear regression

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Area of Science:

  • Biochemistry
  • Medical Informatics
  • Statistical Modeling

Background:

  • Regression models often impute missing laboratory test data.
  • Imputation assumes data are missing at random (MAR).
  • This study validates the MAR assumption for laboratory tests.

Purpose of the Study:

  • To examine the validity of the missing at random (MAR) assumption in laboratory testing.
  • To investigate the relationship between laboratory test results and their order status.

Main Methods:

  • Included 14 biochemistry tests, analyzing all measured results.
  • Used test-stratified multiple linear regression adjusting for patient factors.
  • Developed testing likelihood models using hospital-wide data.

Main Results:

  • In 64.2% of tests, results were significantly associated with order status after adjustment.
  • Test results were more abnormal when ordered for 6 tests and more normal for 3 tests.
  • Test abnormality increased as testing likelihood decreased.

Conclusions:

  • Laboratory data are frequently not missing at random (MAR).
  • Differences in missing laboratory test values vary by test.
  • Imputing missing laboratory data may introduce bias.