On weighting approaches for missing data

Lingling Li, Changyu Shen, Xiaochun Li, James M. Robins

Research output: Contribution to journalArticlepeer-review

39 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)14-30
Number of pages17
JournalStatistical Methods in Medical Research
Issue number1
StatePublished - Feb 2013


  • inverse probability weighting
  • missing at random
  • missing data
  • missing not at random
  • monotone missing
  • non-monotone missing

ASJC Scopus subject areas

  • Epidemiology
  • Health Information Management
  • Statistics and Probability

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