Methods of handling and working with missing data (part 1)

Description In biology, the presence of missing values is a common occurrence for example in proteomics and metabolomics study. This represents a real challenge if one intends to perform an objective statistical analysis avoiding misleading conclusions. The leading causes of incompletely observed data are truncation and censoring which are often wrongly used interchangeably. You can... Continue Reading →

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