Multiple imputation: a mature approach to dealing with missing data
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Missing values in clinical studies are almost unavoidable. When analyzing such data, the standard response is to exclude the patients with missing data. This is known as ‘complete case analysis’ (CCA) and has been shown to be the leading strategy in the epidemiology  and intensive care unit (ICU) literature . However, if the excluded patients are not a representative subsample from the whole sample, their exclusion can lead to bias and loss of precision in estimation, both of which can, for example, adversely affect the performance of predictive risk models in the ICU (Supplementary 1). To deal with this issue, numerous imputation methods have been developed. The simplest method is “simple imputation.” This involves replacing each missing value with a single value, such as the mean of the observed data . Thereafter, all patients present in the sample can be included in the analysis. The simplicity and ease of implementation of this method make it attractive. However, it tends...
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