Dear Edgar,
What you describe is high stochastic noise in the EM algorithms. The
sampling integration needs to be accurate enough in order for these
algorithms to work properly. The first parameter to tune is ISAMPLE of
SAEM, I would start with 10 if you have less than 20 subjects. Accuracy
increases with the square root of ISAMPLE, so it sounds you probably are
going to need to tune more. I would look into Sobol sampling
(RANMETHOD=2S2). Are the stopping criteria reached in SAEM? If not,
consider more iterations (NBURN,NITER). Else, you may fare better with
more stringent stopping criteria (CINTERVAL to 20,50 or 100). Once SAEM
stabilizes with consistent parameters you can than go on and tune IMP
accordingly.
You may find Bob Bauer's tutorial at PAGE
(http://www.page-meeting.org/pdf_assets/9805-Bauer_EM_Methods_tutorial.pdf)
helpful; certainly the last 5 pages or so contain practical advices.
As a completely alternative approach you may want to consider to use
FOCE as a first step and than use IMP sampling only to get a covariance
matrix. Especially if you consider the FOCE parameter estimates to be
appropriate.
Hope this helps,
Jeroen
http://pd-value.com
jer...@pd-value.com
@PD_value
+31 6 23118438
-- More value out of your data!
Op 26-09-16 om 22:33 schreef edgar_sch...@eisai.com:
Hi all,
I am new to the EM methods, but thought I would give it a try since I
had no success in generating confidence intervals with the other more
traditional methods. I am modeling a rich data set for a compound that
has shown to have a second peak between 12 - 24 hours postdose. I have
been able to get good runs with ITS followed by SAEM and IMP. The
problem I am having is that if I repeat a run, with exactly the same
settings and initial estimates, I get different results each time. Not
only the estimates change, but the OFV does too, sometimes up to 10
points. I have tried multiple alternatives, but I continuously have
the same issue. Parameter estimates are generally fine, diagnostic
plots are fine, residual variability is low, but I cannot to move into
covariate analysis without understanding why is this happening. I
remember seeing this before the first time I tried the EM methods, so
my guess is that I am doing something wrong. The model below is the
last one I ran, I would appreciate your help.
thanks in advance,
Edgar
$INPUT C NMID=ID DOSE AMT MDV EVID TIME DV LNDV=DROP CMT
$DATA XXX.CSV IGNORE=C
$SUBROUTINES ADVAN13 TOL=9
$MODEL
COMP=(1_DOS, DEFDOSE) ; DOSE, 1
COMP=(2_DOS) ; DOSE, 2
COMP=(3_CENT, DEFOBS) ; CENTRAL
COMP=(4_PERI) ; PERIPHERAL
COMP=(5_ABS_2) ; TRANSIT 1
COMP=(6_ABS_3) ; TRANSIT 2
COMP=(7_ABS_3) ; TRANSIT 3
$PK
MU_1=LOG(THETA(1))
CL=EXP(MU_1+ETA(1))
MU_2=LOG(THETA(2))
V3=EXP(MU_2+ETA(2))
MU_3=LOG(THETA(3))
Q=EXP(MU_3+ETA(3))
MU_4=LOG(THETA(4))
V4=EXP(MU_4+ETA(4))
MU_5=LOG(THETA(5))
KTR=EXP(MU_5+ETA(5))
MU_6=LOG(THETA(6))
KA1=EXP(MU_6+ETA(6)) ; First order rate of absorption for first
absorption path
MU_7=LOG(THETA(7))
KA2=EXP(MU_7+ETA(7)) ; First order rate of absorption for second
absorption path
MU_8=LOG(THETA(8))
FR1=EXP(MU_8+ETA(8))
FR2=1-FR1
S3=V3/1000
K23 = KA2
K30 = CL/V3
K34 = Q/V3
K43 = Q/V4
$DES
DADT(1) = -KA1*A(1)*FR1
DADT(2) = -KTR*A(2)*FR2
DADT(3) = KA1*FR1*A(1)+K43*A(4)-K34*A(3)-K30*A(3)+KA2*A(7)
DADT(4) = K34*A(3)-K43*A(4)
DADT(5) = KTR*A(2)*FR2 - KTR*A(5)
DADT(6) = KTR*A(5) - KTR*A(6)
DADT(7) = KTR*A(6) - KA2*A(7)
$ERROR
IPRE=F
Y=F*(1+ERR(1))
$EST METHOD=ITS INTERACTION NITER=50 NOABORT
$EST METHOD=SAEM INTERACTION NBURN=1000 ISAMPLE=2 NITER=3000 CTYPE=3
PRINT=200 SEED=1556678 NOABORT
$EST METHOD=IMP INTERACTION EONLY=1 ISAMPLE=1000 NITER=20 MAPITER=20
PRINT=1 MSFO=XXX.MSF
$COVARIANCE UNCONDITIONAL SIGL=8 TOL=10
$THETA
(0,13,) ;[CL]
(0,20) ;[V3]
(0,60,) ;[Q]
(0,200,) ;[V4]
(0,1,) ;[KTR]
(0,1,) ;[KA1]
(0,3,) ;[KA2]
(0,0.2,1) ;[FR1]
$OMEGA
0.3 ;[P] CL
0.3 ;[P] V3
0.3 ;[P] Q
0.3 ;[P] V4
0.3 ;[P] KTR
0.3 ;[P] KA1
0.3 ;[P] KA2
0.3 ;[P] FR1
$SIGMA
0.1 ;[P] sigma(1,1)
$TABLE ID TIME IPRE CWRES ONEHEADER NOPRINT FILE=XXX.tab
Here is a sample of the dataset:
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