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of conclusions from research: Missing completely at random, missing at random, and missing not at random. Missing data can be handled similarly as censored data.

Types

Understanding the reasons why data are missing is important for handling the remaining data correctly. If values are missing completely at random, the data sample is likely still representative of the population. But if the values are missing systematically, analysis may be biased. For example, in a study of the relation

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