Dear All,
I am going through NONMEM manual 5 and I came across How to obtain initial
estimates for omega and sigma
In chapter 9 they mentioned the additive and ccv model but not about Log
Normal model.
For Additive
y =f +e
sigmay = sigma e= rt (r*t is the fraction of its typical value or mean*
typical value)
var(e) =se^2=(rt )^2 = (r*t) ^2
For CCV
y =f +f e
sy =f *se=r*t
var(e)=se^2= (rt)^ 2 /f^2
but if we identify t with the value of f (whatever it may be), we have
then
Var(e) = r^2 i.e the fraction of its typical value
How we have to estimate for Log normal model.
Thank you very much in advance for your feed back.
Regards,
Shankar Lanke Ph.D.
University at Buffalo
Office # 716-645-4853
Fax # 716-645-2886
Cell # 678-232-3567
Estimates for Random effects
Dear Shankar,
Please find the variance of a lognormal distribution at the following link:
http://en.wikipedia.org/wiki/Log-normal_distribution. In the variance formula,
the mu is set to zero.
Jean
Quoted reply history
From: [email protected] [mailto:[email protected]] On
Behalf Of Shankar Lanke
Sent: Friday, February 18, 2011 9:31 AM
To: [email protected]
Subject: [NMusers] Estimates for Random effects
Dear All,
I am going through NONMEM manual 5 and I came across How to obtain initial
estimates for omega and sigma
In chapter 9 they mentioned the additive and ccv model but not about Log Normal
model.
For Additive
y =f +e
sigmay = sigma e= rt (r*t is the fraction of its typical value or mean*
typical value)
var(e) =se^2=(rt )^2 = (r*t) ^2
For CCV
y =f +f e
sy =f *se=r*t
var(e)=se^2= (rt)^ 2 /f^2
but if we identify t with the value of f (whatever it may be), we have
________________________________
then
Var(e) = r^2 i.e the fraction of its typical value
How we have to estimate for Log normal model.
Thank you very much in advance for your feed back.
Regards,
Shankar Lanke Ph.D.
University at Buffalo
Office # 716-645-4853
Fax # 716-645-2886
Cell # 678-232-3567
Shankar,
Because NONMEM uses a first-order Taylor series approximation when fitting data,
Y=F*(1+EPS(1)) <CCV> is equivalent to Y=F*EXP(EPS(1)) .
Therefore to fit a log-normal error model, use log-transformed concentrations as DV and then fit an additive error model, Y=F + EPS(1), where F represents now represents the log of concentration. To obtain initial estimates, use the procedure for the additive model with log of concentration.
Luann Phillips
Director, PK/PD
Cognigen Corporation
Shankar Lanke wrote:
> Dear All,
>
> I am going through NONMEM manual 5 and I came across How to obtain initial estimates for omega and sigma
>
> In chapter 9 they mentioned the additive and ccv model but not about Log Normal model.
>
> For Additive y =f +e sigmay = sigma e= rt (r*t is the fraction of its typical value or mean* typical value)
>
> var(e) =se^2=(rt )^2 = (r*t) ^2
>
> For CCV
>
> y =f +f e
> sy =f *se=r*t
> var(e)=se^2= (rt)^ 2 /f^2
>
> but if we identify t with the value of f (whatever it may be), we have then Var(e) = r^2 i.e the fraction of its typical value
>
> How we have to estimate for Log normal model.
>
> Thank you very much in advance for your feed back.
>
> Regards, Shankar Lanke Ph.D. University at Buffalo
>
> Office # 716-645-4853
> Fax # 716-645-2886
>
> Cell # 678-232-3567