# Time to event with no censoring - use survival or normal regression?

I have some time to event data, but the population is only those who had the event (specifically, my cohort is all kidney tx recipients who were readmitted within one year of discharge for a specific event).

Since there is no censoring, what would be the pros and cons of using survival methods (both KM and Cox) vs. median regression (the time to event is highly skewed)? Also for the survival model - I know that this is the complement of the empirical distribution function, but does my interpretation of results change at all in regards to specific language when there is no censoring?

• @kjetilbhalvorsen Thank you for clarifying that censored data is not necessary for survival models. However I wonder, what "time" should be entered for the cases, where the event did not occur and no censorship was present? (I use R, so Inf seems to indicate censoring, NA seems to indicate a missing value (which is not correct) and 0 seems to indicate the occurance of an event (which also doesn't apply)) Can you clarify? Jun 8, 2020 at 13:54