Re: Inflated random effects showed by VPC
Dear You,
pVPC sometimes inflates variability especially if some of the measurements are near BQL. Look at simple VPC, may be using concentrations normalized by dose (if the system is linear).
Sometimes is the very useful to clean the data. Look on the individual plots where you have DV, IPRED and PRED superimposed, and dose times marked. For subjects where you have large discrepancies between IPRED and PRED, look at whether DVs are consistent or you have some outliers. You can also look at observations with large abs(CWRES) to see whether they should be removed (not because they have high CWRES but due to some timing or dosing errors that could be obvious from looking on the plots)
Leonid
--------------------------------------
Leonid Gibiansky, Ph.D.
President, QuantPharm LLC
web: www.quantpharm.com
e-mail: LGibiansky at quantpharm.com
tel: (301) 767 5566
Quoted reply history
On 9/4/2014 12:15 PM, Jiang, Yu wrote:
> Dear all,
>
> I wonder what might cause a pharmacokinetic model to have inflated
> variability. For my model, the GOF plots look reasonably good--meaning
> that the fixed effects are OK. However the prediction corrected VPC
> implemented by PsN indicated severely overestimated variability
> regardless of whether I stratify them into different dose groups or
> analysis them altogether. I have tried all my candidate models, all of
> them have the observed 95th and 5th percentile way off from the
> simulated confidence bands, and for some of them, the observation points
> don't even go into the upper and lower confidence interval bands.
>
> I checked my eta plots, and I think although they don't look perfectly
> normal, they still looks reasonably symmetrical with a bell shape. Eta
> on V seems to be a little skewed to the right. I don't have much
> experience on PopPK so I might be wrong.
>
> I think there might three possibilities causing this problem.
>
> One is that, the true distribution of etas is not normally distributed
> but more like uniformly distributed (or skewed). The estimation step
> have no problem of identifying the right mean and variance for
> parameters even the true underlying distribution is not normal
> distribution. But when it comes to simulation, the simulated parameters
> are draw from the normal distribution with the estimated mean and
> variance. That discrepancy might cause inflated variability in simulated
> parameters and therefore inflated variability in simulated observations.
>
> The other is that there are a few subjects having very large eta
> compared with other subjects, therefore inflated the estimated omega.
>
> Also all my subjects are dosed based on their weight, height, gender and
> age to achieve a target drug concentration level. They might do a very
> good job making the concentrations to reach the target level so all of
> my observations lies in the middle of the prediction corrected VPC
> plots. I think this is the least likely possibility since I have already
> taken covariate effects into consideration in some of my models..
>
> I am not sure I am thinking it right. Please correct me if I am wrong.
> Does anyone have any thoughts into this? Has anyone encountered similar
> things before? I truly appreciate any comments or suggestions.
>
> Yu
>
> Graduate student in Clinical Pharmaceutical Science
>
> University of Iowa
>
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