RE: Inflated random effects showed by VPC
Dear Yu,
Did you explore block Omega structures to investigate potential correlations
among the random effects? If you assumed a diagonal Omega structure where
the random effects are assumed to be independent when they are indeed
correlated this can inflate the between-subject variability in your
simulations of the concentrations. For example, if the IIV random effects
for CL and V are highly correlated but you simulate assuming these random
effects are independent then you will likely simulate some extreme
combinations of subject-specific values of CL and V that may not be
represented in your data.
Ken
Kenneth G. Kowalski
President & CEO
A2PG - Ann Arbor Pharmacometrics Group, Inc.
110 Miller Ave., Garden Suite
Ann Arbor, MI 48104
Work: 734-274-8255
Cell: 248-207-5082
Fax: 734-913-0230
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Quoted reply history
From: [email protected] [mailto:[email protected]] On
Behalf Of Jiang, Yu
Sent: Thursday, September 04, 2014 12:15 PM
To: [email protected]
Subject: [NMusers] Inflated random effects showed by VPC
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