We have seen survival plots before. Survival plots represent ‘time to event’ in survival analysis. For example, in the case of cancer diagnostics, survival analysis measures the time it takes from exposure to the event, which is most likely death.
These analyses are done following a group of candidates (or patients) between two time periods, i.e., the start and end of the study. Candidates are enrolled at different times during the period, and the ‘time to event’ is noted down. Censoring is a term in survival analysis that denotes when the researcher does not know the exact time-to-event for an included observation.
Right censoring
The term is used when you know the person is still surviving at the end of the study period. Let x be the time since enrollment, and then all we know is the time-to-event ti > x. Imagine a study that started in 2010 and ended in 2020, and a person who was enrolled in 2018 was still alive at the study’s culmination. So we know that xi > 2 years. The same category applies to patients who missed out on follow-ups.
Left censoring
This happens in observational studies, where the risk happens before entering the studies. Because of this, the researcher cannot observe the time when the event occurred. Obviously, this can’t happen if the event is death.
Interval censoring
It occurs when the time until an event of interest is not known precisely and, instead, only is known to fall between two time stamps.
