Combining Multiple Imputation and Inverse-Probability Weighting

@inproceedings{Seaman2012CombiningMI,
  title={Combining Multiple Imputation and Inverse-Probability
Weighting},
  author={Shaun R. Seaman and Ian R. White and Andrew J Copas and Leah Li},
  booktitle={Biometrics},
  year={2012}
}
Two approaches commonly used to deal with missing data are multiple imputation (MI) and inverse-probability weighting (IPW). IPW is also used to adjust for unequal sampling fractions. MI is generally more efficient than IPW but more complex. Whereas IPW requires only a model for the probability that an individual has complete data (a univariate outcome), MI needs a model for the joint distribution of the missing data (a multivariate outcome) given the observed data. Inadequacies in either model… CONTINUE READING
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