Imputation and Missing Data
The presence of missing data is a big challenge for statisticians, especially if the distribution of the missing values is not completely random. Analysis performed on datasets with missing data can lead to erroneous conclusions and significant bias in the results.
Missing data is a common occurrence in pathology and laboratory medicine as most analyzers have multiple points of failure which can lead to errors or measurement failures. Thus dealing with missing data is a necessary skill for a pathologist.
There are different solutions for dealing with missing data. They can be as simple as dropping the observation with missing data to more complex solutions such as ‘imputing’ the missing data. In this chapter, we will explain some of these solutions.
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