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Flexible Imputation of Missing Data. Second Edition

Missing data pose challenges to real-life data analysis. Simple ad-hoc fixes, like deletion or mean imputation, only work under highly restrictive conditions, which are often not met in practice. Multiple imputation replaces each missing value by …

Combining multiple imputation and bootstrap in the analysis of cost-effectiveness trial data

In healthcare cost‐effectiveness analysis, probability distributions are typically skewed and missing data are frequent. Bootstrap and multiple imputation are well‐established resampling methods for handling skewed and missing data. However, it is …

Multiple imputation for multilevel data with continuous and binary variables

We present and compare multiple imputation methods for multilevel continuous and binary data where variables are systematically and sporadically missing. The methods are compared from a theoretical point of view and through an extensive simulation …

Multiple imputation of multilevel data

This chapter discusses critical issues associated with imputation of multilevel data. Section 10.2 introduces the notation used and outlines how two formulations of the same model are related. Section 10.3 dissects the multilevel missing data problem …