A key characteristic that distinguishes survival analysis from other areas in statistics is that survival data are usually censored. Censoring occurs when incomplete information is available about the survival time of some individuals. We define censoring through some practical examples extracted from the literature in various fields of public health. With few exceptions, the censoring mechanisms in most observational studies are unknown and hence it is necessary to make assumptions about censoring when the common statistical methods are used to analyze censored data. In addition, we present situations in which censoring mechanisms can be ignored. The effects of the censoring assumptions are demonstrated through actual studies.


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  • Article Type: Review Article
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