Hi Amy,
you can do this in several ways. Will list some of them in the order of my personal preference:
Option 1: Similar to Option 2 but with a fine grid. Example: simtime = seq(0,240,0.1). Then averaging over CONC readouts in the last 24 hours and multiplying by 24. Not as numerically correct as Option 1 but for practical purposes with fine enough grid OK.
Option 2: I use a defined simulation time vector. Example: simtime = seq(0,240,24) (let's assume a 24 hour dosing interval). In the model I have d/dt(AUC) = Conc. From the simulation results I take the difference of the AUC variable. The last value is the AUC over the last 24 hours.
Option 3: As in option 1 you can have a state d/dt(AUC) = CONC. Using the even construct you can reset AUC to 0 at defined times or for defined conditions. For equidistant dosing (e.g. 24 hours) you could define an event as follows:
myeventname = eq(mod(time,24),0),AUC,0
Then at the end of each 24 hour interval AUC would contain the value of the AUC in this interval.
A very simple example model for Option 3 is below this email. No dosing here ... just some trajectory and calculating AUC in 24 hour increments.
There are more possibilities to do this ... but Option 1 is the one I typically use.
Cheers,
Henning
********** MODEL NAME
Provide Model Name
********** MODEL NOTES
Provide Model Information
********** MODEL STATES
d/dt(CONC) = -0.001*CONC
d/dt(AUC) = CONC
CONC(0) = 100
********** MODEL PARAMETERS
********** MODEL VARIABLES
********** MODEL REACTIONS
********** MODEL FUNCTIONS
********** MODEL EVENTS
myeventname = eq(mod(time,24),0),AUC,0
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