= 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.
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