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?
Best Answer
Survival methods are about modeling some time to event data. There is no need for there to be censoring! the methods will work and be more effective without censoring. Time to event data will probably not be well fitted by normal distribution models, so usual linear regression is not indicated. I say you should go with survival methods.