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Multiple imputation of missing data with ante-dependence covariance structure

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  • Paul Zhang

Abstract

A controlled clinical trial was conducted to investigate the efficacy effect of a chemical compound in the treatment of Premenstrual Dysphoric Disorder (PMDD). The data from the trial showed a non-monotone pattern of missing data and an ante-dependence covariance structure. A new analytical method for imputing the missing data with the ante-dependence covariance is proposed. The PMDD data are analysed by the non-imputation method and two imputation methods: the proposed method and the MCMC method.

Suggested Citation

  • Paul Zhang, 2005. "Multiple imputation of missing data with ante-dependence covariance structure," Journal of Applied Statistics, Taylor & Francis Journals, vol. 32(2), pages 141-155.
    • Handle: RePEc:taf:japsta:v:32:y:2005:i:2:p:141-155
    DOI: 10.1080/02664760500054178
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    References listed on IDEAS

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    1. Michael G. Kenward, 1987. "A Method for Comparing Profiles of Repeated Measurements," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 36(3), pages 296-308, November.
    2. Horton N. J. & Lipsitz S. R., 2001. "Multiple Imputation in Practice: Comparison of Software Packages for Regression Models With Missing Variables," The American Statistician, American Statistical Association, vol. 55, pages 244-254, August.
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    Cited by:

    1. Sarah Mustillo, 2012. "The Effects of Auxiliary Variables on Coefficient Bias and Efficiency in Multiple Imputation," Sociological Methods & Research, , vol. 41(2), pages 335-361, May.

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