Missing Covariate Data

  • Joseph G. Ibrahim
  • Ming-Hui Chen
  • Debajyoti Sinha
Part of the Springer Series in Statistics book series (SSS)


Missing covariate data often arise in various settings, especially in clinical trials, epidemiological studies, and environmental studies. In the frequentist setting, it is well known (see Little and Rubin, 1987) that analyses based on complete cases can and will often result in inaccurate estimates of coefficients and their standard deviations. In the Bayesian context, complete case analyses will often lead to posterior distributions with properties that are quite different than those based on the observed data posterior, i.e., the posterior distribution using all of the cases. Thus, it becomes increasingly important in these situations to develop methods which incorporate the missing data into the analysis. The missing data problem has received much attention under the frequentist paradigm for a wide variety of models because of its common occurrence in many studies. Little (1992) gives an excellent review of developments for a variety of missing data problems. A recent book by Schafer (1997) examines frequentist and Bayesian approaches for missing data problems involving normal and categorical data, but it does not discuss survival models.


Posterior Distribution Model Check Covariate Distribution Posterior Inference Miss Data Mechanism 
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Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • Joseph G. Ibrahim
    • 1
  • Ming-Hui Chen
    • 2
  • Debajyoti Sinha
    • 3
  1. 1.Department of BiostatisticsHarvard School of Public Health and Dana-Farber Cancer InstituteBostonUSA
  2. 2.Department of Mathematical SciencesWorcester Polytechnic InstituteWorcesterUSA
  3. 3.Department of Biometry and EpidemiologyMedical Universtiy of South CarolinaCharlestonUSA

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