# RE: [NMusers] Modeling accelerated phase of malignancy

From: Mark Sale <msale_at_nuventra.com>
Date: Sat, 30 May 2015 02:44:48 +0000

Thanks to Mannie, this is the correct way to solve this (and seems to work =
as well).
Thanks
Mark

From: echigutsa_at_yahoo.com [mailto:echigutsa_at_yahoo.com]
Sent: Friday, May 29, 2015 7:34 PM
To: Mark Sale
Subject: Re: [NMusers] Modeling accelerated phase of malignancy

Hi Mark

Have you tried using MTIME and MPAST for the switch? In my experience it ap=
pears to behave better for time-dependent changes.

Best wishes,

Mannie

Sent from Yahoo Mail on Android<https://overview.mail.yahoo.com/mobile/?.sr=
c=Android>

________________________________
From: Mark Sale <msale_at_nuventra.com<mailto:msale_at_nuventra.com>>;
To: nmusers_at_globomaxnm.com<mailto:nmusers_at_globomaxnm.com> <nmusers_at_globomax=
nm.com<mailto:nmusers_at_globomaxnm.com>>;
Subject: [NMusers] Modeling accelerated phase of malignancy
Sent: Fri, May 29, 2015 7:52:36 PM

Dear Colleagues,
I'm working on a model of a malignancy that, at some point in the course =
of the disease enters into an accelerated phase. I'm using a sort of stand=
ard serial compartment model, with a zero order input rate, then first orde=
r transit to the next compartment. I think the "correct" model for natural=
history is a slow increase in the input rate over time, then, at some poin=
t change to an exponential growth. I'm having trouble getting NONMEM to do=
this. The relevant code I have in \$DES is:

IF(T.LT.NTLAG) THEN
LGIND = 0
ELSE
LGIND = 1
END IF
NATHL = LGIND*NATH
.
.
.
Where NTLAG is an estimated parameter for the lag time between entry into t=
he study and the onset of the accelerated phase, NATH is the natural histor=
y term, NATHL is the lagged natural history term, INPUT is the zero order i=
nput rate and K is the first order transit constant. FOCE actually works =
pretty well for this for the THETA term for NTLAG, gives reasonable values.=
Probably is with the ETA for NTLAG (which is essential since it varies fr=
om person to person. With FOCE I get zero gradient for it. BAYES, SAEM, I=
MP MAP and ITS give reasonable values for OMEGA, but conditional values for=
ETA are all zero.
What I think is going on is that, unlike an ALAG, there is not event at tha=
t point in time, so small changes in ETA (smaller than the integration step=
size) don't change the predicted value, so no gradient and all ETAs = 0 =
with EM methods.
I've tried to figure out a way to do this with an additional compartment fo=
r the natural history and haven't been able to yet. That, I think would so=
lve the problem, since an event would be inserted at the end of ALAG.
Any ideas on a solution, is there a way to insert an event at an unknown ti=
me?

Thanks
Mark

Received on Fri May 29 2015 - 22:44:48 EDT

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