= Independent censoring
= Uninformative censoring
{synonym}
A simple sufficient <independent censoring> assumption is that the potential observation-ending time $C$ and <survival time> $T$ are <independent random variables>. More generally, appropriate <conditional independence> given the <covariates> used in the analysis can suffice for analysis within those conditions. The requirement concerns the potential times, rather than <independence> of the observed duration $Y=\min(T,C)$ and event indicator $\delta$: those recorded quantities ordinarily depend on each other.
For an ordinary pooled <Kaplan–Meier estimator>, the observed <risk set> must represent the future event experience of everyone still event-free at each time. <Conditional independence> given a <covariate> does not alone justify ignoring that <covariate> in a pooled calculation. Also, there must be a positive <probability> of remaining observed through the horizons being estimated. If follow-up ends at a common closing time, it supplies no observations to identify the later <survival function>. Independent subjects with a common <survival distribution> and <independent censoring> form the basic model in https://web.stanford.edu/~lutian/coursepdf/STAT331unit3.pdf[Lu Tian's STAT331 notes, Unit 3, pages 1–4].
For example, subjects who stop attending because their condition is deteriorating can produce <informative censoring>. A predetermined common closing date can instead give <administrative censoring>, provided calendar entry and other relevant conditions do not select future <survival times>. The reason for stopping observation must be justified from study design and context; the recorded event indicators alone cannot verify <independent censoring>.
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