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Diagnostic checking of multiple imputation models

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  • Yang Zhao

    (University of Regina)

Abstract

Model checking in multiple imputation (MI, Rubin in Multiple imputation for nonresponse in surveys, Wiley, New York, 1987) becomes increasingly important with the recent developments in MI and its widespread use in statistical analysis with missing data (e.g. van Buuren et al. in J Stat Comput Simul 76(12):1049–1064, 2006; van Buuren and Groothuis-Oudshoorn in J Stat Soft 45(3):1–67, 2011; Chen et al. in Biometrics 67:799–809, 2011; Nguyen et al. in Emerg Themes Epidemiol 14(8):1–12, 2017). The currently recommended posterior predictive checking method (He and Zaslavsky in Stat Med 31:1–18, 2012; Nguyen et al. in Biom J 4:676–694, 2015) is less effective when the proportion of missing values increases and its produced posterior predictive p value is not supported by a null distribution as a standard p value (Meng in Annu Stat 22:1142–1160, 1994). This research develops a new diagnostic method for checking MI models and proposes a test statistic with a standard p value. The new diagnostic checking method is effective and flexible. It does not depend on the proportion of missing values and can deal with data sets with arbitrary nonmonotone missing data patterns. We examine the performance of the proposed method in a simulation study and illustrate the method in a study of coronary disease and associated factors.

Suggested Citation

  • Yang Zhao, 2022. "Diagnostic checking of multiple imputation models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(2), pages 271-286, June.
  • Handle: RePEc:spr:alstar:v:106:y:2022:i:2:d:10.1007_s10182-021-00429-1
    DOI: 10.1007/s10182-021-00429-1
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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. Hua Yun Chen & Hui Xie & Yi Qian, 2011. "Multiple Imputation for Missing Values through Conditional Semiparametric Odds Ratio Models," Biometrics, The International Biometric Society, vol. 67(3), pages 799-809, September.
    3. Yang Zhao & Meng Liu, 2021. "Unified approach for regression models with nonmonotone missing at random data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(1), pages 87-101, March.
    4. BaoLuo Sun & Eric J. Tchetgen Tchetgen, 2018. "On Inverse Probability Weighting for Nonmonotone Missing at Random Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(521), pages 369-379, January.
    5. Yi‐Hau Chen & Hung Chen, 2000. "A unified approach to regression analysis under double‐sampling designs," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(3), pages 449-460.
    6. Kobi Abayomi & Andrew Gelman & Marc Levy, 2008. "Diagnostics for multivariate imputations," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 57(3), pages 273-291, June.
    7. Andrew Gelman & Iven Van Mechelen & Geert Verbeke & Daniel F. Heitjan & Michel Meulders, 2005. "Multiple Imputation for Model Checking: Completed-Data Plots with Missing and Latent Data," Biometrics, The International Biometric Society, vol. 61(1), pages 74-85, March.
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