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A toolkit in SAS for the evaluation of multiple imputation methods

Author

Listed:
  • Jaap P.L. Brand
  • Stef van Buuren
  • Karin Groothuis‐Oudshoorn
  • Edzard S. Gelsema

Abstract

This paper outlines a strategy to validate multiple imputation methods. Rubin's criteria for proper multiple imputation are the point of departure. We describe a simulation method that yields insight into various aspects of bias and efficiency of the imputation process. We propose a new method for creating incomplete data under a general Missing At Random (MAR) mechanism. Software implementing the validation strategy is available as a SAS/IML module. The method is applied to investigate the behavior of polytomous regression imputation for categorical data.

Suggested Citation

  • Jaap P.L. Brand & Stef van Buuren & Karin Groothuis‐Oudshoorn & Edzard S. Gelsema, 2003. "A toolkit in SAS for the evaluation of multiple imputation methods," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 57(1), pages 36-45, February.
    • Handle: RePEc:bla:stanee:v:57:y:2003:i:1:p:36-45
    DOI: 10.1111/1467-9574.00219
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    Cited by:

    1. Eneko Echaniz & Chinh Ho & Andres Rodriguez & Luigi dell’Olio, 2020. "Modelling user satisfaction in public transport systems considering missing information," Transportation, Springer, vol. 47(6), pages 2903-2921, December.
    2. Janet MacNeil Vroomen & Iris Eekhout & Marcel G. Dijkgraaf & Hein van Hout & Sophia E. de Rooij & Martijn W. Heymans & Judith E. Bosmans, 2016. "Multiple imputation strategies for zero-inflated cost data in economic evaluations: which method works best?," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 17(8), pages 939-950, November.

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