When using the generalized regression platform to analyze left censored data, where detection is not possible due to a lower detection limit, how JMP handles the censored data?
Please confirm that i understand correctly. Is the left censored data treated as if it lies between zero and the detection limit through missing imputation (simulation) assuming entire dataset is non-normal distribution (such as log-normal)?
Or, instead of using simulation for imputation, are specific statistical rules or methods applied to impute values for left censored data?
The reason I'm asking this question is because yesterday, during a session introducing generalized regression for JMP Pro, a customer mentioned that they are currently analyzing censored data in JMP Basic by simulating missing values between zero and the detection limit. They asked if genreg platform could be a better approach.
I can say GENREG could be appropriate to suggest that assuming a specific distribution for simulation might be more suitable in our case. right?
