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- W2912776378 abstract "Multiple imputation (MI) is a useful tool for conducting analyses under both missing at random (MAR) and missing not at random (MNAR) assumptions. This chapter discusses some general principles about how MIs are obtained and reviews several widely used imputation methods, focusing particularly on those available in SAS. It provides a discussion on how to choose different ancillary variables in the imputation model in either a prospective or a retrospective (exploratory) manner. After missing data have been multiply imputed and each of the imputed datasets analyzed using a method suitable for the complete-data analysis, the results from multiple datasets need to be combined for an overall inference. The chapter addresses the question of how many imputations are sufficient to produce a reliable result in a statistically efficient manner. It also provides a list of aspects, which should be fully pre-specified when using MI methodology." @default.
- W2912776378 created "2019-02-21" @default.
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- W2912776378 date "2014-02-21" @default.
- W2912776378 modified "2023-10-16" @default.
- W2912776378 title "Multiple Imputation" @default.
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- W2912776378 doi "https://doi.org/10.1002/9781118762516.ch6" @default.
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