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Approximate subject-deletion influence diagnostics for Inverse Probability of Censoring Weighted (IPCW) method

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  • Hattori, Satoshi
  • Kato, Mai

Abstract

An approximate formula for subject-deletion influence diagnostics is proposed for the Inverse Probability of Censoring Weighted method [Robins, J.M., Rotnitzky, A., Zhao, P., 1995. Analysis of semiparametric regression models for repeated outcomes in the presence of missing data. J. Amer. Statist. Assoc. 90, pp. 106-121] when the independent working correlation is employed. By a numerical study with a dataset from a clinical trial, it is found that the formula provides good approximation to the exact method by fitting regression models repeatedly to datasets without each subject and saves the computational time remarkably in particular for large datasets.

Suggested Citation

  • Hattori, Satoshi & Kato, Mai, 2009. "Approximate subject-deletion influence diagnostics for Inverse Probability of Censoring Weighted (IPCW) method," Statistics & Probability Letters, Elsevier, vol. 79(17), pages 1833-1838, September.
    • Handle: RePEc:eee:stapro:v:79:y:2009:i:17:p:1833-1838
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    References listed on IDEAS

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    1. Kang‐Mo Jung, 2008. "Local Influence in Generalized Estimating Equations," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(2), pages 286-294, June.
    2. Hongtu Zhu, 2004. "A diagnostic procedure based on local influence," Biometrika, Biometrika Trust, vol. 91(3), pages 579-589, September.
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