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RE: [NMusers] model of error, linearisation, additive error, and VPC

From: Huub Jan Kleijn <hkleijn_at_wequantify.com>
Date: Sun, 31 Jan 2016 18:02:08 +0000

Hi Pavel,

I guess I would opt for using logtransformed concentrations as dv in your m=
odel. That would deal with the negative model predictions.

Best,
Huub
________________________________________
From: owner-nmusers_at_globomaxnm.com [owner-nmusers_at_globomaxnm.com] on behalf=
 of Pavel Belo [nonmem_at_optonline.net]
Sent: Friday, January 29, 2016 10:47 PM
Cc: 'nmusers_at_globomaxnm.com'
Subject: [NMusers] model of error, linearisation, additive error, and VPC

Hello NONMEM Users,

When I tried to print log-scaled VPC, there was an error message about nega=
tive values. It can be caused by an additive error and/or linearization of=
 the error model when NONMEM transforms Y = F*DEXP(ERR(1)*SD1) into Y =
 = F+ F*ERR(1)*SD1. The error is inflated at small concentrations and re=
moving the additive term is not an option in my case.

Is there an easy way to solve it? It can be something like Y = F**GAMMA=
*DEXP(ERR(1)*SD1) or
Y = F+ F*ERR(1)*SD1 + F**GAMMA*ERR(2)*SD2, where GAMMA<1.

Thanks,
Pavel
Received on Sun Jan 31 2016 - 13:02:08 EST

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