title: On weighting approaches for missing data.
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.
authors.affiliation: Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA. lingling_li@post.harvard.edu
authors.forename: Lingling
authors.initials: L
authors.lastname: Li
authors.forename: Changyu
authors.initials: C
authors.lastname: Shen
authors.forename: Xiaochun
authors.initials: X
authors.lastname: Li
authors.forename: James M
authors.initials: JM
authors.lastname: Robins
mesh_terms: D003627:Data Interpretation, Statistical; D015233:Models, Statistical; D011336:Probability