Hi Eduard,
If you want to have a minimalistic model, with respect to both fixed effects
and random effects, but still incorporate all 4 categories as well as the
Markov element, you can try the minimal Continuous-Time Markov Model (mCTMM)
described in "A Minimal Continuous-Time Markov Pharmacom
Dear Eduard,
Did you check the distribution of the number of transitions per individual?
If this number is low, IIV is difficult to estimate and high shrinkage is
expected, as well as a non-normal distribution of the post-hoc estimates.
With respect to your second question, you can obtain a rough
Dear Sumeet,
Actually please ignore my last remark, and thanks to Jonathan French for
pointing out to me that, the ratio of two log-Normals is indeed log-Normal. I
should have deferred to statistical theory rather than fuzzy memory of
Normal/Normal being Cauchy so must somehow extend to log-No
Thank you so much!!
These are all wonderful insights and replies and I am definitely going to
ponder over it today.
Regards,
Sumeet Singla
On Feb 5, 2019, at 7:52 AM, Saeheum Song
mailto:ss.pkpdmo...@gmail.com>> wrote:
The input data are composed of amount as dosing and concentration in plasm
Thanks Bill
- Original Message -
From: "Bill Denney"
To: "Sebastien Bihorel" ,
nmusers@globomaxnm.com
Sent: Thursday, January 31, 2019 11:16:08 AM
Subject: RE: [NMusers] Mailing list about pharmacometrics
Hi Sebastien,
The best I know for general PMx questions is the ISoP message board
The input data are composed of amount as dosing and concentration in
plasma. Concentration is expressed as amount divided by volume of
distribution. The rate constant is movement of either amount or
concentration pending on modelers intention. Whence, in the modeling
fitting, dosing amount needs t
Dear Eduard,
Have you tried SAEM or IMP methods to estimate the variability ?
Also did you check how good are your population predictions when compared to
individual observations? I'm curious to see whether your model with the
Markovian and time components would not be capturing all the variabili
Dear Sumeet,
If you are assuming a distribution for your parameters (e.g. log-Normal p =
theta * exp(eta)) then it might matter if you use rate constants versus
clearances and volumes. In general, if you want to make the log-Normal
assumption you should use clearances and volumes as there is r
Dear all,
I am currently trying to model the transitions between four adverse event
grades (0-3) using a continuous-time Markov modeling approach. I have
included a dose effect as well as a time effect on the transition constants.
Overall, the parameters are well estimated and the VPC looks als
