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Hi, I am currently working with SimBiology and have implemented a compartmental model to follow the Lutatera therapy. I am using the 2021 version of Matlab. I have modified sbiofit to implement the object function in order to perform a fit with Bayes values. Comparing it with another software that already uses Bayes, I could see that the fitted parameters have values that differ little. The problem is that with matlab, the standard errors that sbiofit calculates are too large. At first I thought it was just because of the degrees of freedom, but unfortunately, even when correcting them, I still get too high errors on the fitted parameters. Upon redoing the code, I noticed that the Jacobian does not take into account my Bayes values, nor the standard Bayes error. numjac is the function that calculates the Jacobian. My question is whether it is possible, and how, to incorporate Bayes standard errors into the standard errors of my parameters. In theory, from the fit, the errors of the fitted parameters should be no larger than the Bayes errors. Has anyone ever solved this problem in other functions used for the fit? Or are there functions that already use Bayes that can help me solve my problem? Regards

Hello, I have a question regarding sbiofitmixed. I have fitted a model using log parameter transformation. In the NLMEresults object I can see the transformed values of the fixed effects in conjunction with their standard errors. While I can extract the original non-transformed values of the fixed effect by either using exp to transform them back or extracting them from the NLMEResults.PopulationParameterEstimates field, I can not do the same with the standard errors. I could in theory use the Delta method but I have a small sample size so that is not a proper idea here. My question is if there is a way to extract the back-tranformed Standard Errors of the model fit using sbiofitmixed or the NLMEResults object? Thank you for your answers in advance. Best, Ben