RE: 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: [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
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