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Power calculation in multiply imputed data

Author

Listed:
  • Ruochen Zha

    (The University of Connecticut)

  • Ofer Harel

    (The University of Connecticut)

Abstract

Multiple imputation (MI) has been proven an effective procedure to deal with incomplete datasets. Compared with complete case analysis (CCA), MI is more efficient since it uses the information provided by incomplete cases which are simply discarded in CCA. A few simulation studies have shown that statistical power can be improved when MI is used. However, there is a lack of knowledge about how much power can be gained. In this article, we build a general formula to calculate the statistical power when MI is used. Specific formulas are given for several different conditions. We demonstrate our finding through simulation studies and a data example.

Suggested Citation

  • Ruochen Zha & Ofer Harel, 2021. "Power calculation in multiply imputed data," Statistical Papers, Springer, vol. 62(1), pages 533-559, February.
    • Handle: RePEc:spr:stpapr:v:62:y:2021:i:1:d:10.1007_s00362-019-01098-8
    DOI: 10.1007/s00362-019-01098-8
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    References listed on IDEAS

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    1. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    2. Jerome P. Reiter, 2008. "Multiple imputation when records used for imputation are not used or disseminated for analysis," Biometrika, Biometrika Trust, vol. 95(4), pages 933-946.
    3. David A. Wagstaff & Ofer Harel, 2011. "A closer examination of three small-sample approximations to the multiple-imputation degrees of freedom," Stata Journal, StataCorp LP, vol. 11(3), pages 403-419, September.
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