This is probably a straight-forward question but I can't find a straight-forward answer. The topic is new to me.
I am performing parametric survival analysis, e.g. estimating a survival function from survival data and some covariates.
It seems like it would be intuitive to check the residuals of the model in the same was as linear regression, ie per subject, subtract the mean survival time estimated from the model from the actual survival time (and square and sum the results, for example).
But I do not see this anywhere as a recommended procedure. I can see that with censoring you would not be able to do this too well (ie, there is no "actual" survival time for these individuals), but if there is no censoring? Or to compare competing parametric survival models on the same data?
Why is this not done? Or is it done and I am just not finding it?
Edit: I am editing the question because it seems it is unclear (the "duplicate" question is not the same as mine). My question is essentially, why does one not use, the "sum of the absolute difference in predicted survival time vs actual survival time" from parametric models of survival data (where the predicted survival time is the median/mean survival time given the covariates) to compare models? It seems the model with the smaller sum of residuals would be better.