Review of inverse probability weighting for dealing with missing data
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- Shaun R Seaman
- MRC Biostatistics Unit, Institute of Public Health, Forvie Site, Robinson Way, Cambridge, UK
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- Ian R White
- MRC Biostatistics Unit, Institute of Public Health, Forvie Site, Robinson Way, Cambridge, UK
書誌事項
- 公開日
- 2011-01-10
- 権利情報
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- https://journals.sagepub.com/page/policies/text-and-data-mining-license
- DOI
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- 10.1177/0962280210395740
- 公開者
- SAGE Publications
この論文をさがす
説明
<jats:p>The simplest approach to dealing with missing data is to restrict the analysis to complete cases, i.e. individuals with no missing values. This can induce bias, however. Inverse probability weighting (IPW) is a commonly used method to correct this bias. It is also used to adjust for unequal sampling fractions in sample surveys. This article is a review of the use of IPW in epidemiological research. We describe how the bias in the complete-case analysis arises and how IPW can remove it. IPW is compared with multiple imputation (MI) and we explain why, despite MI generally being more efficient, IPW may sometimes be preferred. We discuss the choice of missingness model and methods such as weight truncation, weight stabilisation and augmented IPW. The use of IPW is illustrated on data from the 1958 British Birth Cohort.</jats:p>
収録刊行物
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- Statistical Methods in Medical Research
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Statistical Methods in Medical Research 22 (3), 278-295, 2011-01-10
SAGE Publications