 Solution

A standard sufficient non-informative censoring assumption is independent censoring: $T$ and $C$ are independent, or conditionally independent given the covariates used in the analysis. Their distributions need not be equal, have the same shape, or have proportional hazards. In a likelihood analysis the censoring distribution should also carry no information about the survival parameters being estimated, allowing its factor to be omitted. In the more general risk-set formulation, the censoring history must not alter the subsequent failure hazard conditional on the observed history used by the model. Informative censoring can arise if worsening unrecorded health causes withdrawal.