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. 2013 Feb;22(1):14-30.
doi: 10.1177/0962280211403597. Epub 2011 Jun 24.

On weighting approaches for missing data

Lingling Li  1 , Changyu Shen, Xiaochun Li, James M Robins
Affiliations

On weighting approaches for missing data

Lingling Li et al. Stat Methods Med Res. 2013 Feb.

Abstract

We review the class of inverse probability weighting (IPW) approaches for the analysis of missing data under various missing data patterns and mechanisms. The IPW methods rely on the intuitive idea of creating a pseudo-population of weighted copies of the complete cases to remove selection bias introduced by the missing data. However, different weighting approaches are required depending on the missing data pattern and mechanism. We begin with a uniform missing data pattern (i.e. a scalar missing indicator indicating whether or not the full data is observed) to motivate the approach. We then generalise to more complex settings. Our goal is to provide a conceptual overview of existing IPW approaches and illustrate the connections and differences among these approaches.

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Figures

Figure 1
Figure 1
Missing data process in a RMM process
See this image and copyright information in PMC

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