Hello all, I use additive\ multiplicative\add&mul error models to evaluate my base model. All error models can fit OK, but when I check the VPC results, the 5%/95% bounds under/overestimate the observed data for additive and add&mul error models.
So my question is whether my VPC setting for these two error models is right.
Can I do something to improve it.
What can lead to the negative values in this study (but not other studies)?
The file only shows one study, and I fit the same model to the whole studies. Actually the VPC problem only happened for this one. And overall the add&mul error model can reduce AIC significantly (~100 compared with multiplicative error model, and even better than additive) overall and give reasonable VPC to all studies except this one, which makes it a dilema, how can I select the final error model, as mentioning a bad VPC for one study is not a strong reason to select multiplicative over the mixed error model. This study represents 20% of the data points, 10% of the subjects, and the only food effect study.
First think I did was plot your data and would also want to double check that you have the correct units for the DV column you used as a lot (96/300) of your data is below your BQL of 0.000868 with peaks only 10x that. Is that correct ?
why do you think that food is affecting Ka, I’m not sure I see it in these data, maybe a tlag or F would be better.
I think there is a lot of variability in your data, and therefore the VPC is telling you to be cautious as you can’t be that confident of the parameter estimates, what sort of CV% were you getting in your fitting of a 2com model? are you sure 1 com would not be sufficient, maybe if you have data from other studies it would be more informative to fit them all in one model?
Thank you for the reply. Yes indeed, the whole data made me chose the current model that it may not be a good model for this data set. The BQL is right, I have changed the dataset a little bit to ignore some of the BQL (see attached). I think this has dragged the attention for my question a little bit.
What I don’t understand is the VPC difference between multiplicative/additive/mixed error model. I understand your point that the variability of the parameter estimation makes the VPC like this. But why the multiplicative error model gives a less exaggerate prediction than additive and mixed error model. For example, the mul erro model dose not give very negative VPC value.
Joy, I think it is because with multiplicative the error is proportional to the conc, whereas additive is assumin uniform. take a look at this post and perhaps that clarifies it for you
Thanks for the link, I read through and learn posts with similar topics. I still have two questions to make sure I am on the right track to understand them.
In this post (CMultStdev - Phoenix WNL basics - Certara Forums) why “you cannot use more than one epsilon in each observe statement” for a Add+Mul error model? Just like Y=F +EPS(1) + F*EPS(2), to report eps1 for additive and eps2 for multiplicative varible and their corresponding sigma1 and sigma2.
So based on the answers, are Add+Mul error model and Mix Ratio error model basically the same error model? For Add+Mul model, it reports CmultStdev as sigma2 and sigma() as sigma1 for additive error, whereas in Mix Ratio, it only reports CMixRatio, which equals to Sigma2/Sigma1. So you can only know the ratio, which is less interpretable than Add+Mul model?.
And since they represent sigma, so both CmultStdev and CMixRatio have to be positive?
