4 replies to this topic

[csheme]

Hello,

 

I have a 2-cmpt pop PK model, and have generated the post-hoc output table by individual subject, which includes each of the thetas by ID. I also do have a corresponding eta table by ID.

 

My question is how do I setup my model to simulate exposures for each of the subjects in the study based on the individual post-hoc estimates for each subject. I would like to setup a fixed dose (126 mg given q3w for 6 cycles with PK simulations every 2 hours) for each of the subjects based on their (actual theta and eta) post-hoc estimates.

 

Previously I believe I was doing clinical trial simulations by accepting all fixed and random effects with my model, and adding a simulation table to include 50 random individuals, I added the dosing sheet, and put in seq (0,2260,2) with CObs as the output, I would then do descriptive stats on CObs and plot the median and also the shaded area band from the 5th to 95th percentiles (90% confidence). Feel free to see the image below.

 

 

This time around I am interested in the post-hoc analysis, but am not familiar with the approach for this. I would like to see the entire profiles, and generate PK parameters such as AUCtau for each actual subject ID based on the actual post-hoc theta and eta estimates (and not a random population of 50 theoretical subjects with the fixed population PK estimates as I had done before in the image above). You can see I also added the actual observations into the plot.

 

I imagine I will get a table (DV vs CObs) for each of the actual subject IDs, which I could then do a simple NCA with descriptive stats based on the post-hoc results.

 

Thanks a lot for your input, please see my model below. If you could please describe the process and also help me set this up in my model I would be much obliged.

 

https://www.dropbox....is.phxproj?dl=0

Edited by csheme, 01 April 2018 - 04:40 PM.

[soujanya234]

Hello,

 

I have a 2-cmpt pop PK model, and have generated the post-hoc output table by individual subject, which includes each of the thetas by ID. I also do have a corresponding eta table by ID.

 

My question is how do I setup my model to simulate exposures for each of the subjects in the study based on the individual post-hoc estimates for each subject. I would like to setup a fixed dose (126 mg given q3w for 6 cycles with PK simulations every 2 hours) for each of the subjects based on their (actual theta and eta) post-hoc estimates.

 

Previously I believe I was doing clinical trial simulations by accepting all fixed and random effects with my model, and adding a simulation table to include 50 random individuals, I added the dosing sheet, and put in seq (0,2260,2) with CObs as the output, I would then do descriptive stats on CObs and plot the median and also the shaded area band from the 5th to 95th percentiles (90% confidence). Feel free to see the image below.

 

 

This time around I am interested in the post-hoc analysis, but am not familiar with the approach for this. I would like to see the entire profiles, and generate PK parameters such as AUCtau for each actual subject ID based on the actual post-hoc theta and eta estimates (and not a random population of 50 theoretical subjects with the fixed population PK estimates as I had done before in the image above). You can see I also added the actual observations into the plot.

 

I imagine I will get a table (DV vs CObs) for each of the actual subject IDs, which I could then do a simple NCA with descriptive stats based on the post-hoc results.

 

Thanks a lot for your input, please see my model below. If you could please describe the process and also help me set this up in my model I would be much obliged.

 

https://www.dropbox....is.phxproj?dl=0

Hi,

Try using individual eta estimates as the covariates instead of population eta, that way you would get Cobs adjusted to their eta for each individual subject- see the sample code:

test(){
 cfMicro(A1, Cl / V, Cl2 / V, Cl2 / V2, first = (Aa = Ka))
 dosepoint(Aa)
 C = A1 / V
 error(CEps = 0.268573)
 observe(CObs = C * (1 + CEps))
 stparm(Ka = tvKa * exp(nKa))
 stparm(V = tvV * exp(nV))
 stparm(V2 = tvV2 * exp(nV2))
 stparm(Cl = tvCl * exp(nCl))
 stparm(Cl2 = tvCl2 * exp(nCl2))
 fcovariate(nV)
 fcovariate(nCl)
 fcovariate(nKa)
 fcovariate(nV2)
 fcovariate(nCl2)
 fixef(tvKa = c(, 0.00871573, ))
 fixef(tvV = c(, 6.42957, ))
 fixef(tvV2 = c(, 900.051, ))
 fixef(tvCl = c(, 0.0973144, ))
 fixef(tvCl2 = c(, 0.0582446, ))
 #ranef(diag(nV, nCl, nKa, nV2, nCl2) = c(0.46607611, 0.4057042, 0.044551053, 0.016906062, 0.46273008))
 ranef(diag(ndumm) =c(0.0001))
 
}

 

hope this helps!

Thanks,

Soujanya

[csheme]

Thanks Soujanya for the quick reply. 

 

What about the theta for each individual? The way the model is written above is using the fixed population estimates.

 

I would like to simulate based off the post-hoc estimates for each individual, theta + eta respectively (i.e. volumes and clearances for each subject are unique). 

 

Thanks greatly.

[soujanya234]

Thanks Soujanya for the quick reply. 

 

What about the theta for each individual? The way the model is written above is using the fixed population estimates.

 

I would like to simulate based off the post-hoc estimates for each individual, theta + eta respectively (i.e. volumes and clearances for each subject are unique). 

 

Thanks greatly.

Ok.. I tried following code as test case and it worked- 

I have used the structural parameters from the post hoc estimates table from your model and removed the pop. estimates.

 not sure if it is accurate or not though.

test(){

cfMicro(A1, Cl / V, Cl2 / V, Cl2 / V2, first = (Aa = Ka))

dosepoint(Aa)

C = A1 / V

error(CEps = 0.268573)

observe(CObs = C * (1 + CEps))

stparm(Ka = Ka)

stparm(V = V)

stparm(V2 = V2)

stparm(Cl = Cl)

stparm(Cl2 = Cl2)

#fixef(tvKa = c(, 0.00871573, ))

#fixef(tvV = c(, 6.42957, ))

#fixef(tvV2 = c(, 900.051, ))

#fixef(tvCl = c(, 0.0973144, ))

#fixef(tvCl2 = c(, 0.0582446, ))

#ranef(diag(nV, nCl, nKa, nV2, nCl2) = c(0.46607611, 0.4057042, 0.044551053, 0.016906062, 0.46273008))

ranef(diag(ndumm) =c(0.0001))

}

[csheme]

Thanks Soujanya for the the quick follow-up.

 

Could anyone confirm if this is the correct way to do post-hoc analysis of population pharmacokinetic data using NLME?

 

I am looking to simulate distinct individuals with the population PK model based on the individual post hoc PK parameters.

 

Thanks all, much obliged.