MATLAB: Is it possible to get the group (that is the subject) number in sbiofit to do a lookup

covariateSimBiology

If I'm fitting using lsqnonlin, is there a matlab script function that returns the group number?
The idea is to allow use of covariates without having to add false dose columns in the dataset. I could use the group variable to look up parameters in a table, and use assignment rules to set parameters. So I could use covariates to set some parameters while fitting other. As in fitting a PK model with some data, and then using the typical parameter values plus covariate rules to set PK parameter when I do a PD fit using data from another trial/dataset.
One could (perhaps) use the following script to set parameter parA
Repeat assignment rule for parA is parA = getparA();
getparA.m
% [parA] = getparA(); % returns parA 
gp = getgroupvar(); % This is the function I'm asking about
if ~exist(parAdata)
    parAdata = dataset('parAdata.xlsx'); % columns are subject and parA values
end
[~,idx]=ismember(gp,parAdata(:,1));
idx(idx==0)=[];
parA = parAdata(idx,2);
This would allow one to set any number of parameter values without modifying the dataset. Alternately I could create a copy of the subject column (dummy_subj) and create a species subj_number in the model. Then dummy_subj could be used to put a dose in subj_number, and I could do the same lookup approach.
The point is, once one does (for example) a PK fit to get individual parameters (either with nlmefit or lsqnonlin), there are a lot of machinations needed to actually USE the parameter values in subsequent fits.
Also, nlmefit, one can add covariate effect when one is fitting a specific parameter. But (my understading is that if one unclicks the "estimate" tic box so that the parameter is not fitted, then this turns off the covariate effect as well.

Best Answer

Hi Jim,
the alternative you described, which involves a subject column used as a dose for a dummy species is the best way to get the group number into the ODE system. I have tried it in the past and it works just fine.
I recommend you to define parAdata as a persistent variable so that it's loaded only once per simulation.
As for nlmefit or nlmefitsa, you are right that parameters that are not estimated are fixed with the value specified in the model or variant of that task. Do you intend to perform NLME separately on PK and PD?
Best regards,
Jérémy
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