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 standard
serial compartment model, with a zero order input rate, then first order
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 point
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
DADT(3) = (INPUT-A(3)*K)+ A(3)*NATHL
DADT(4) = A(3)*K-A(4)*K
.
.
.
Where NTLAG is an estimated parameter for the lag time between entry into the
study and the onset of the accelerated phase, NATH is the natural history term,
NATHL is the lagged natural history term, INPUT is the zero order input 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 from person to
person. With FOCE I get zero gradient for it. BAYES, SAEM, IMP 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 that
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 for
the natural history and haven't been able to yet. That, I think would solve
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 time?
Thanks
Mark
Modeling accelerated phase of malignancy
Dear Mark,
I think one problem might be the distributioal assumption on NTLAG in that if you have outlying individuals with early/late lag you may need to think of a sensible transformation for the ETA or there might be no obvious one. Do all the subjects go into the exponential phase or might the typical value of NTLAG be greater than your observed time? That aside, something to try would be to have a continuous function driving NTLAG, e.g.:
NATHL = NATH*T**50/(T**50+NTLAG**50)
Play around with the shape parameter value, or even consider estimating it thus allowing a more gradual transition?
Hope this helps,
Joe
Joseph F Standing
MRC Fellow, UCL Institute of Child Health
Antimicrobial Pharmacist, Great Ormond Street Hospital
Tel: +44(0)207 905 2370
Mobile: +44(0)7970 572435
Quoted reply history
________________________________________
From: owner-nmusers_at_globomaxnm.com [owner-nmusers_at_globomaxnm.com] On Behalf Of Mark Sale [msale_at_nuventra.com]
Sent: 29 May 2015 20:52
To: nmusers_at_globomaxnm.com
Subject: [NMusers] Modeling accelerated phase of malignancy
Dear Colleagues,
Im working on a model of a malignancy that, at some point in the course of the disease enters into an accelerated phase. Im using a sort of standard serial compartment model, with a zero order input rate, then first order 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 point change to an exponential growth. Im 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
DADT(3) = (INPUT-A(3)*K)+ A(3)*NATHL
DADT(4) = A(3)*K-A(4)*K
.
.
.
Where NTLAG is an estimated parameter for the lag time between entry into the study and the onset of the accelerated phase, NATH is the natural history term, NATHL is the lagged natural history term, INPUT is the zero order input 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 from person to person. With FOCE I get zero gradient for it. BAYES, SAEM, IMP 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 that point in time, so small changes in ETA (smaller than the integration step size) dont change the predicted value, so no gradient and all ETAs = 0 with EM methods.
Ive tried to figure out a way to do this with an additional compartment for the natural history and havent been able to yet. That, I think would solve 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 time?
Thanks
Mark
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Dear Mark,
I think one problem might be the distributioal assumption on NTLAG in that if
you have outlying individuals with early/late lag you may need to think of a
sensible transformation for the ETA or there might be no obvious one. Do all
the subjects go into the exponential phase or might the typical value of NTLAG
be greater than your observed time? That aside, something to try would be to
have a continuous function driving NTLAG, e.g.:
NATHL = NATH*T**50/(T**50+NTLAG**50)
Play around with the shape parameter value, or even consider estimating it thus
allowing a more gradual transition?
Hope this helps,
Joe
Joseph F Standing
MRC Fellow, UCL Institute of Child Health
Antimicrobial Pharmacist, Great Ormond Street Hospital
Tel: +44(0)207 905 2370
Mobile: +44(0)7970 572435
Quoted reply history
________________________________________
From: [email protected] [[email protected]] On Behalf Of
Mark Sale [[email protected]]
Sent: 29 May 2015 20:52
To: [email protected]
Subject: [NMusers] Modeling accelerated phase of malignancy
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 standard
serial compartment model, with a zero order input rate, then first order
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 point
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
DADT(3) = (INPUT-A(3)*K)+ A(3)*NATHL
DADT(4) = A(3)*K-A(4)*K
.
.
.
Where NTLAG is an estimated parameter for the lag time between entry into the
study and the onset of the accelerated phase, NATH is the natural history term,
NATHL is the lagged natural history term, INPUT is the zero order input 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 from person to
person. With FOCE I get zero gradient for it. BAYES, SAEM, IMP 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 that
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 for
the natural history and haven’t been able to yet. That, I think would solve
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 time?
Thanks
Mark
********************************************************************************************************************
This message may contain confidential information. If you are not the intended
recipient please inform the
sender that you have received the message in error before deleting it.
Please do not disclose, copy or distribute information in this e-mail or take
any action in reliance on its contents:
to do so is strictly prohibited and may be unlawful.
Thank you for your co-operation.
NHSmail is the secure email and directory service available for all NHS staff
in England and Scotland
NHSmail is approved for exchanging patient data and other sensitive information
with NHSmail and GSi recipients
NHSmail provides an email address for your career in the NHS and can be
accessed anywhere
********************************************************************************************************************
Thanks to Mannie, this is the correct way to solve this (and seems to work as
well).
Thanks
Mark
Quoted reply history
From: [email protected] [mailto:[email protected]]
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
appears to behave better for time-dependent changes.
Best wishes,
Mannie
Sent from Yahoo Mail on
https://overview.mail.yahoo.com/mobile/?.src=Android
________________________________
From: Mark Sale <[email protected]<mailto:[email protected]>>;
To: [email protected]<mailto:[email protected]>
<[email protected]<mailto:[email protected]>>;
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 standard
serial compartment model, with a zero order input rate, then first order
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 point
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
DADT(3) = (INPUT-A(3)*K)+ A(3)*NATHL
DADT(4) = A(3)*K-A(4)*K
.
.
.
Where NTLAG is an estimated parameter for the lag time between entry into the
study and the onset of the accelerated phase, NATH is the natural history term,
NATHL is the lagged natural history term, INPUT is the zero order input 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 from person to
person. With FOCE I get zero gradient for it. BAYES, SAEM, IMP 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 that
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 for
the natural history and haven't been able to yet. That, I think would solve
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 time?
Thanks
Mark
Hi Mark,
A different but related issue is that when the malignant cells start
proliferating fast, which is the definition of entering an accelerated phase,
you would at least 2 different clones or subpopulation within your malignant
cells: one initial clone less proliferating, and a new clone highly
proliferating. These 2 clones have different biology, and different
sensitivity to different drugs. The different drug sensitivity come snot only
from different molecular lesions that made the cells highly proliferating, but
also because many drugs have different activities on proliferating vs non
proliferating cells. Can you model 2 cell subpopulations, one less
proliferating and another more proliferating, where at the beginning there is
only the non-proliferating clone, then the proliferating clone appears taking
over the % of cells over time, until only the proliferating clone is present
among the malignant cells? This would be much closer to the real process.
Best
Joan
Quoted reply history
De: [email protected] [mailto:[email protected]] En
nombre de Mark Sale
Enviado el: Saturday, May 30, 2015 4:45 AM
Para: [email protected]
CC: [email protected]
Asunto: RE: [NMusers] Modeling accelerated phase of malignancy
Thanks to Mannie, this is the correct way to solve this (and seems to work as
well).
Thanks
Mark
From: [email protected]<mailto:[email protected]>
[mailto:[email protected]]
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
appears to behave better for time-dependent changes.
Best wishes,
Mannie
Sent from Yahoo Mail on
https://overview.mail.yahoo.com/mobile/?.src=Android
________________________________
From: Mark Sale <[email protected]<mailto:[email protected]>>;
To: [email protected]<mailto:[email protected]>
<[email protected]<mailto:[email protected]>>;
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 standard
serial compartment model, with a zero order input rate, then first order
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 point
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
DADT(3) = (INPUT-A(3)*K)+ A(3)*NATHL
DADT(4) = A(3)*K-A(4)*K
.
.
.
Where NTLAG is an estimated parameter for the lag time between entry into the
study and the onset of the accelerated phase, NATH is the natural history term,
NATHL is the lagged natural history term, INPUT is the zero order input 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 from person to
person. With FOCE I get zero gradient for it. BAYES, SAEM, IMP 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 that
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 for
the natural history and haven't been able to yet. That, I think would solve
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 time?
Thanks
Mark