Getting the following error repeatedly for different error setting other than exponential error in population PK PD modelling in Simbiology.
1 view (last 30 days)
I am trying to develop a population pharmacokinetic model using Simbiology. My model is two compartmental. And I am trying for non-linear mixed effect model. For the data which i am fitting - other than exponential error for all the other error settings (like combined, proportional and constant) - i am getting the follow error.
After the initial refinement of the fixed effects with the Levenberg-Marquardt algorithm the Jacobian at BETA0 is ill-conditioned. Some fixed effects may not be identifiable resulting in a poor estimation. Check for possible aliased parameters of your model, or try setting 'RefineBeta0' to FALSE.
If I am setting for exponential error, then I am not getting this error and is getting a good Loglikelihood value. I have also inputed and seen the inital values based on the results of non-mixed effect modelling strategies.
What is this error and is it required to be overcomed ?
Arthur Goldsipe on 24 Jun 2019
As a general rule, you will need to address anything that causes an error, while a warning indicates something that may or may not be an actual problem.
In this case, there was a problem during the initial optimization that occurs when estimating parameters of a non-linear mixed effects model and when the option RefineBeta0 is true (which is the default value). Specifically, the error message indicates a problem with parameter identifiability. This means parameters could not be uniquely identified. This can occur for a number of reasons. Unless you can provide your model, it's difficult to say the exact cause or how to it. But if you read up on parameter identifiability, you might get some ideas on how to understand the problem.
As indicated in the error message, you could try setting the option RefineBeta0 to false to see if that avoids the error. This can work around some but not all of the problems that can lead to this error message.