Selection models and pattern-mixture models for incomplete data with covariates |
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Authors: | Michiels B Molenberghs G Lipsitz S R |
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Institution: | Biostatistics, Limburgs Universitair Centrum, Diepenbeek, Belgium. |
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Abstract: | Most models for incomplete data are formulated within the selection model framework. This paper studies similarities and differences of modeling incomplete data within both selection and pattern-mixture settings. The focus is on missing at random mechanisms and on categorical data. Point and interval estimation is discussed. A comparison of both approaches is done on side effects in a psychiatric study. |
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Keywords: | Categorical data Maximum likelihood estimation Missing data Multiple imputation Sensitivity analysis |
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