Mark,
IMPMAP procedure produces run.cnv file. There you can find mean and SD
of OF (over the last few iterations that were considered for convergence
stop). I use these numbers for covariate assessment as
iteration-to-iteration numbers oscillate and cannot be reliably compared.
Concerning the last iteration OF drop, cannot tell for sure but I've
seen OF drops in some cases when the main manager do not wait for the
slaves to return OF of their portion of the data. prn file has
parameters TIMEOUTI and TIMEOUT, and I would try to increase them and
see whether this fixes the problem
Thanks
Leonid
On 8/23/2018 1:54 PM, Mark Sale wrote:
I have a model that seems to be behaving strangely, looking for interpretation
help
in model building, the OBJ is usually ~20900. Until this model, where, on the
covariance step (IMPMAP method) the OBJ drops 9000 points (20798 to 11837),
monitoring from output file below
iteration 70 OBJ= 20798.6782833867 eff.= 5530. Smpl.= 10000.
Fit.= 0.99524
Convergence achieved
iteration 70 OBJ= 11837.9045704476 eff.= 5475. Smpl.= 10000.
Fit.= 0.99522
Parameters don't change much (edited .ext file below).
50 1.35E+01 9.96E-01 4.42E-02 9.41E-01 3.05E+01 1.29E-01 20799.68932
60 1.35E+01 9.67E-01 4.45E-02 9.43E-01 3.05E+01 1.29E-01 20792.90665
70 1.35E+01 9.73E-01 4.44E-02 9.44E-01 3.05E+01 1.29E-01 20798.67828
70 1.35E+01 9.73E-01 4.44E-02 9.44E-01 3.05E+01 1.29E-01 11837.90457
Plots don't look particularly different than other model (and look pretty
good), p values for ETAs are very reasonable, it converges, condition # is
good. Only two issues:
RSE for 2 OMEGAs is a little large (0.5)
an interoccasion variability term (on V) is very large (~4, exponential). This
is, I think, related to many subjects with data only at steady state.
Further, when I advance this model, add another covariate, or another IOV on
CL, to address the issue with SS data, cannot identify Volume uniquely (using
the final parameter from this model as the initial in the next model), I cannot
reproduce these results - the OBJ goes back to ~20,800, with essentially the
same parameter estimates. So I end up rejected all additional covariates in
this model (at least by LRT).
other details, running on Windows, 64 bit, Intel compiler, NONMEM version 7.3.
Can I believe this OBJ value? Should I base an additional hypotheses on the
SEE, rather than the LRT?
But, basically, why is this happening?
thanks
Mark Sale M.D.
Senior Vice President, Pharmacometrics
Nuventra Pharma Sciences, Inc.
2525 Meridian Parkway, Suite 200
Durham, NC 27713
Phone (919)-973-0383
ms...@nuventra.com<ms...@kinetigen.com>
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