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Imputation and Missing Data

  • Amir Momeni
  • Matthew Pincus
  • Jenny Libien
Chapter

Abstract

The presence of missing data is a big challenge for statisticians, especially if the distribution of the missing values is not completely random. Analysis performed on datasets with missing data can lead to erroneous conclusions and significant bias in the results.

Missing data is a common occurrence in pathology and laboratory medicine as most analyzers have multiple points of failure which can lead to errors or measurement failures. Thus dealing with missing data is a necessary skill for a pathologist.

There are different solutions for dealing with missing data. They can be as simple as dropping the observation with missing data to more complex solutions such as ‘imputing’ the missing data. In this chapter, we will explain some of these solutions.

References

  1. 1.
    Dong Y, Peng CY. Principled missing data methods for researchers. SpringerPlus. 2013;2(1):222.CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Shara N, Yassin SA, Valaitis E, Wang H, Howard BV, Wang W, Lee ET, Umans JG. Randomly and non-randomly missing renal function data in the strong heart study: a comparison of imputation methods. PLoS One. 2015;10(9):e0138923.CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Zhang Z. Missing data exploration: highlighting graphical presentation of missing pattern. Annals Transl Med. 2015;3(22):356.Google Scholar
  4. 4.
    Little RJ, Rubin DB. Single imputation methods. In: Statistical analysis with missing data. 2nd ed. Chicester: John Wiley and Sons; 2002. p. 59–74.Google Scholar
  5. 5.
    Gelman A, Hill J. Data analysis using regression and multilevel/hierarchical models. Cambridge: Cambridge university press; 2006.CrossRefGoogle Scholar
  6. 6.
    Royston P. Multiple imputation of missing values. Stata J. 2004;4(3):227–41.Google Scholar
  7. 7.
    Little RJ, Rubin DB. Bayes and multiple imputation. In: Statistical Analysis with Missing Data. 2nd ed. Chicester: John Wiley and Sons; 2002. p. 200–20.Google Scholar
  8. 8.
    Yuan YC. Multiple imputation for missing data: concepts and new development (Version 9.0), vol. 49. Rockville: SAS Institute Inc; 2010. p. 1.Google Scholar
  9. 9.
    Graham JW, Olchowski AE, Gilreath TD. How many imputations are really needed? Some practical clarifications of multiple imputation theory. Prev Sci. 2007;8(3):206–13.CrossRefPubMedGoogle Scholar
  10. 10.
    van Ginkel JR, Kroonenberg PM. Analysis of variance of multiply imputed data. Multivar Behav Res. 2014;49(1):78–91.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Amir Momeni
    • 1
  • Matthew Pincus
    • 1
  • Jenny Libien
    • 1
  1. 1.Department of PathologyState University of New York, Downstate Medical CenterBrooklynUSA

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