Re: [NMusers] time-dependent clearance model

[Toufigh Gordi]

Xiaofeng,

It always amazes me how many parameters we try to estimate based on the
limited amount of data available. This is specially the case when a rather
complex event is taking place, such as autoinduction in this case, and that
based on sparse samples. In your case, there are a total of 15 parameters to
be estimated, which, as Katya points out seems to be way too many.

I would suggest that you start with the simplest model and then make it more
complex once you have the structural (i.e., your PK) model in place. Since
you have data only at pre- and post-induction, I think it is a good idea to
estimate different CL values for the two occasions. This way, you can use
the built-in library, with explicit solutions, which is normally faster than
trying to solve the equations in the $DES. Why not start with this and 1-2
ETAs on the CLs on days 1 and 7? If this simple model works, you can always
move ahead and add ETAs.

Toufigh

On 3/4/12 8:51 PM, "Wang, Xiaofeng"  wrote:

> Hi Katya,
> 
> Thank you. I double checked the units. mcg for AMT, ng/mL for DV, and L for
> CL. I think there is no problem with the units. Since I am using
> log-transformed data, does that influence the units?
> 
> I will try kicking in CLSS after day 1.  For 6 observations per patients, what
> is the reasonable number of ETAs?
> 
> Best,
> Xiaofeng
> 
> From: owner-nmus...@globomaxnm.com [owner-nmus...@globomaxnm.com] on behalf of
> Ekaterina Gibiansky [egibian...@quantpharm.com]
> Sent: Sunday, March 04, 2012 8:53 PM
> To: 'nmusers@globomaxnm.com'
> Subject: Re: [NMusers] time-dependent clearance model
> 
> Xiaofeng,
> 
> Such a large difference suggests you may have a problem with units: check AMT,
> DV, and the parameter S2 for consistency.
> A couple of other points:
> - If the full induction is expected by day 7, and you only have data on day 1
> and after day 7, you may not be able to estimate KIN. You may be better off
> with the model where there is CLI on day 1 and CLSS on days >1 (And you will
> not need $DES for that model).
> - You have 6 observations per subject and you have 7 ETAs, it is too many,
> your model should be overparameterized (Check whether relative standard errors
> are large).
> 
> Regards,
> Katya
> Ekaterina Gibiansky, Ph.D.
> CEO&CSO, QuantPharm LLC
> Web: www.quantpharm.com 
> Email:   EGibiansky at quantpharm.com
>  
> 
> On 3/4/2012 5:33 PM, Wang, Xiaofeng wrote:
>> Dear nmusers,
>>  
>> I have a drug with clearance autoinduction. I have sparse data(three
>> observations on day 1, two between day 7 and 14, and one on day 28).  I am
>> trying to run a time-dependent clearance model. I tried FO and FOCEI, but I
>> always got unreasonable estimate for the initial clearance (CLI) which is
>> about 0.15 L/h (from knowledge of previous studies, the reasonable initial
>> clearance should be around 20 L/h and maximum induction occurs around day 7).
>> Could someone give me some advice about my model and my data? Thank you.
>>  
>> Xiaofeng 
>>  
>> The CTL file:
>> $PROB 
>> $INPUT C ID TIME AMT DV MDV EVID ADDL II CMT
>> $DATA C:/ IGNORE=C
>> $SUBROUTINES ADVAN6 TOL=6
>> $MODEL NCOMPARTMENTS=3
>>COMP=(DEPOT,DEFDOSE)
>>COMP=(CENTRAL,DEFOBS)
>>COMP=(PERIP)
>> $PK
>>CLI=THETA(1)*EXP(ETA(1))
>>CLSS=THETA(2)*EXP(ETA(2))
>>KIN=THETA(3)*EXP(ETA(3))
>>V2= THETA(4)*EXP(ETA(4))
>>Q= THETA(5)*EXP(ETA(5))
>>V3= THETA(6)*EXP(ETA(6))
>>KA= THETA(7)*EXP(ETA(7))
>>S2=V2
>>K23=Q/V2
>>K32=Q/V3
>>
>> $DES
>> CL=CLSS-(CLSS-CLI)*EXP(-KIN*T)
>> K20=CL/V2
>> DADT(1)=-KA*A(1)
>> DADT(2)=KA*A(1)+K32*A(3)-K23*A(2)-K20*A(2)
>> DADT(3)=K23*A(2)-K32*A(3)
>> $ERROR
>>IPRE=LOG(1)
>>IF(F.GT.0) IPRE=LOG(F)
>>Y = IPRE+EPS(1)
>> $EST METHOD=0 POSTHOC PRINT=10 MAX= SIG=2 NOABORT   MSFO=050.MSF
>> $THETA 
>>   (0, 20);[CLI]
>>   (0, 65);[CLSS]
>>   (0, 0.02) ;[KIN]
>>   (0, 45);[V2]
>>   (0, 5);[Q]
>>   (0, 58);[V3]
>>   (0, 0.2);[KA]
>>   
>> $OMEGA .25 .25 .25 .25 .25 .25 .25
>> $SIGMA .2
>> $COV PRINT=E
>> $TABLE ID TIME DV CLI CLSS KIN V2 Q V3 KA IPRE CWRES ONEHEADER NOPRINT
>> FILE=050.TAB
>> $TABLE ID TIME CLI CLSS KIN V2 Q V3 KA FIRSTONLY NOAPPEND NOPRINT
>> FILE=050.PAR
>> $TABLE ID ETA1 ETA2 ETA3 ETA4 ETA5 ETA6 ETA7 FIRSTONLY NOAPPEND NOPRINT
>> FILE=050.ETA
>> $TABLE ID TIME CLI CLSS KIN V2 Q V3 KA FIRSTONLY NOAPPEND NOPRINT
>> FILE=PATAB050
>>  
>>  
>>  
>>  
>>  
>>  
>>  
>>  
>>  
>>  
>>  
>>  
>>  
> 

[NMusers] Estimating multiple EPS with SAEM

[Brendan Johnson]

Hi all, I am having some issues when estimating two residual error parameters 
with the SAEM algorithm (NM7.1.2, PsN 3.2.4).



Below is my example, where each EPS is turned on/off by an indicator variable, 
not sure what the behavior is like for error models such as Y=IPRD*EXP(EPS(1)) 
+ EPS(2), but I suspect it works fine.



I have two subject populations, TYPE = 2 and TYPE = 3. Good reason to suspect 
TYPE = 3 is more variable, so I implemented the following in $ERROR of my PD 
model



Y=(IPRD+ERR(1))*(3-TYPE)+(IPRD+ERR(2))*(TYPE-2)



$SIGMA

  30 ; EPS type2

  30 ; EPS type3



This is the estimation sequence


$ESTIMATION METHOD=ITS

  PRINT=10 NOABORT NITER=100 NSIG=3 SIGL=6 CTYPE=2



$ESTIMATION METHOD=SAEM

 ISAMPLE=2 NBURN=500 NITER=2000

 PRINT=10 NSIG=3 SIGL=6 SEED=123345 CTYPE=3



$ESTIMATION METHOD=IMPMAP

  EONLY=1 NITER=10 ISAMPLE=1000

  PRINT=1 NSIG=3 SIGL=6 SEED=123345 CTYPE=3 CITER=10 CALPHA=0.05



Results from ITS are shown below, these estimates make sense.

ITS:
EPS1  EPS2
 EPS1
+2.17E+01
 EPS2
+0.00E+00  2.51E+01

After SAEM (which converges before end of burn in), the estimate of SIGMA2 has 
blown up (at least I think it has, the "E" is missing, which I think is a 
result of truncation since the exponent has 3 digits). All other THETA and 
OMEGA estimates are reasonable and consistent with prior models.

SAEM:


EPS1  EPS2



 EPS1

+2.23E+01



 EPS2

+0.00E+00  2.95+304


I do not understand why this, would have thought the study has a reasonable 
number of TYPE2:TYPE3 to estimate the two values (70:37), ITS think so I 
would like to confirm if it is a data issue or a NONMEM issue.


Consequently, the IMPMAP (EONLY=1) estimation goes off the rails with very 
large OBJ (which is usually in the range of 3000-4000), largely driven by 
several clusters of subjects with very high individual contributions to IMPMAP 
OBJ.


#METH: Objective Function Evaluation by Importance/MAP Sampling

 EM/BAYES SETUP

 THETAS THAT ARE MU MODELED:

   1   2   4   5   6   7   8   9  10  11  12  13  14

 THETAS THAT ARE SIGMA-LIKE:





 MONITORING OF SEARCH:



 iteration0 OBJ=178895.840910174

 iteration1 OBJ=178911.252369277

 iteration2 OBJ=178904.447527235

 iteration3 OBJ=178901.025144545

 iteration4 OBJ=178911.754485815

 iteration5 OBJ=178902.859882922

 iteration6 OBJ=178904.436625787

 iteration7 OBJ=178909.875233486

 iteration8 OBJ=178905.268884384

 iteration9 OBJ=178908.849070488

 Elapsed estimation time in seconds:  5830.79

 iteration   10 OBJ=178909.810873438



Any thoughts?

Cheers
Brendan

RE: [NMusers] time-dependent clearance model

[Wang, Xiaofeng]

Hi Toufigh,

Thank you for the suggestion. I tried the simplest model with 2 ETAs on the 
pre- and post-induction clearance. The result gave very small omega estimate 
for the pre-induction clearance (2.50e-005). But I don't believe that there is 
no between subject variability for the pre-induction clearance. Could you 
please give me some suggestion about this? Thank you.

Best,
Xiaofeng


From: Toufigh Gordi [tgo...@rosaandco.com]
Sent: Monday, March 05, 2012 2:30 AM
To: Wang, Xiaofeng; 'nmusers@globomaxnm.com'
Subject: Re: [NMusers] time-dependent clearance model

Xiaofeng,

It always amazes me how many parameters we try to estimate based on the limited 
amount of data available. This is specially the case when a rather complex 
event is taking place, such as autoinduction in this case, and that based on 
sparse samples. In your case, there are a total of 15 parameters to be 
estimated, which, as Katya points out seems to be way too many.

I would suggest that you start with the simplest model and then make it more 
complex once you have the structural (i.e., your PK) model in place. Since you 
have data only at pre- and post-induction, I think it is a good idea to 
estimate different CL values for the two occasions. This way, you can use the 
built-in library, with explicit solutions, which is normally faster than trying 
to solve the equations in the $DES. Why not start with this and 1-2 ETAs on the 
CLs on days 1 and 7? If this simple model works, you can always move ahead and 
add ETAs.

Toufigh

On 3/4/12 8:51 PM, "Wang, Xiaofeng" 
> wrote:

Hi Katya,

Thank you. I double checked the units. mcg for AMT, ng/mL for DV, and L for CL. 
I think there is no problem with the units. Since I am using log-transformed 
data, does that influence the units?

I will try kicking in CLSS after day 1.  For 6 observations per patients, what 
is the reasonable number of ETAs?

Best,
Xiaofeng

From: owner-nmus...@globomaxnm.com 
[owner-nmus...@globomaxnm.com] on behalf of Ekaterina 
Gibiansky [egibian...@quantpharm.com]
Sent: Sunday, March 04, 2012 8:53 PM
To: 'nmusers@globomaxnm.com'
Subject: Re: [NMusers] time-dependent clearance model

Xiaofeng,

Such a large difference suggests you may have a problem with units: check AMT, 
DV, and the parameter S2 for consistency.
A couple of other points:
- If the full induction is expected by day 7, and you only have data on day 1 
and after day 7, you may not be able to estimate KIN. You may be better off 
with the model where there is CLI on day 1 and CLSS on days >1 (And you will 
not need $DES for that model).
- You have 6 observations per subject and you have 7 ETAs, it is too many, your 
model should be overparameterized (Check whether relative standard errors are 
large).

Regards,
Katya
Ekaterina Gibiansky, Ph.D.
CEO&CSO, QuantPharm LLC
Web: www.quantpharm.com 
Email:   EGibiansky at quantpharm.com


On 3/4/2012 5:33 PM, Wang, Xiaofeng wrote:
Dear nmusers,

I have a drug with clearance autoinduction. I have sparse data(three 
observations on day 1, two between day 7 and 14, and one on day 28).  I am 
trying to run a time-dependent clearance model. I tried FO and FOCEI, but I 
always got unreasonable estimate for the initial clearance (CLI) which is about 
0.15 L/h (from knowledge of previous studies, the reasonable initial clearance 
should be around 20 L/h and maximum induction occurs around day 7). Could 
someone give me some advice about my model and my data? Thank you.

Xiaofeng

The CTL file:
$PROB
$INPUT C ID TIME AMT DV MDV EVID ADDL II CMT
$DATA C:/ IGNORE=C
$SUBROUTINES ADVAN6 TOL=6
$MODEL NCOMPARTMENTS=3
   COMP=(DEPOT,DEFDOSE)
   COMP=(CENTRAL,DEFOBS)
   COMP=(PERIP)
$PK
   CLI=THETA(1)*EXP(ETA(1))
   CLSS=THETA(2)*EXP(ETA(2))
   KIN=THETA(3)*EXP(ETA(3))
   V2= THETA(4)*EXP(ETA(4))
   Q= THETA(5)*EXP(ETA(5))
   V3= THETA(6)*EXP(ETA(6))
   KA= THETA(7)*EXP(ETA(7))
   S2=V2
   K23=Q/V2
   K32=Q/V3

$DES
CL=CLSS-(CLSS-CLI)*EXP(-KIN*T)
K20=CL/V2
DADT(1)=-KA*A(1)
DADT(2)=KA*A(1)+K32*A(3)-K23*A(2)-K20*A(2)
DADT(3)=K23*A(2)-K32*A(3)
$ERROR
   IPRE=LOG(1)
   IF(F.GT.0) IPRE=LOG(F)
   Y = IPRE+EPS(1)
$EST METHOD=0 POSTHOC PRINT=10 MAX= SIG=2 NOABORT   MSFO=050.MSF
$THETA
  (0, 20);[CLI]
  (0, 65);[CLSS]
  (0, 0.02) ;[KIN]
  (0, 45);[V2]
  (0, 5);[Q]
  (0, 58);[V3]
  (0, 0.2);[KA]

$OMEGA .25 .25 .25 .25 .25 .25 .25
$SIGMA .2
$COV PRINT=E
$TABLE ID TIME DV CLI CLSS KIN V2 Q V3 KA IPRE CWRES ONEHEADER NOPRINT 
FILE=050.TAB
$TABLE ID TIME CLI CLSS KIN V2 Q V3 KA FIRSTONLY NOAPPEND NOPRINT FILE=050.PAR
$TABLE ID ETA1 ETA2 ETA3 ETA4 ETA5 ETA6 ETA7 FIRSTONLY NOAPPEND NOPRINT 
FILE=050.ETA
$TABLE ID TIME CLI CLSS KIN V2 Q V3 KA FIRSTONLY NOAPPEND NOPRINT FILE=PATAB050

Re: [NMusers] time-dependent clearance model

[Toufigh Gordi]

Whenever I start a modeling process, my first objective is to get the
structural model, i.e., your PK model in this case, well defined and in
place. If you start from there and you are pretty confident that you have
the right model, finding what parameters differ between subjects should not
be difficult. In your case with the very low ETA on CL1, how confident are
you about the model estimates, i.e., V3 or Q? Are they stay the same from
days 1 to ss? I can¹t really give any specific advice since I haven¹t seen
the results and know very little about the model and the fits.

Toufigh

On 3/5/12 1:30 PM, "Wang, Xiaofeng"  wrote:

> Hi Toufigh,
> 
> Thank you for the suggestion. I tried the simplest model with 2 ETAs on the
> pre- and post-induction clearance. The result gave very small omega estimate
> for the pre-induction clearance (2.50e-005). But I don't believe that there is
> no between subject variability for the pre-induction clearance. Could you
> please give me some suggestion about this? Thank you.
> 
> Best,
> Xiaofeng
> 
> 
> From: Toufigh Gordi [tgo...@rosaandco.com]
> Sent: Monday, March 05, 2012 2:30 AM
> To: Wang, Xiaofeng; 'nmusers@globomaxnm.com'
> Subject: Re: [NMusers] time-dependent clearance model
> 
> Xiaofeng,
> 
> It always amazes me how many parameters we try to estimate based on the
> limited amount of data available. This is specially the case when a rather
> complex event is taking place, such as autoinduction in this case, and that
> based on sparse samples. In your case, there are a total of 15 parameters to
> be estimated, which, as Katya points out seems to be way too many.
> 
> I would suggest that you start with the simplest model and then make it more
> complex once you have the structural (i.e., your PK) model in place. Since you
> have data only at pre- and post-induction, I think it is a good idea to
> estimate different CL values for the two occasions. This way, you can use the
> built-in library, with explicit solutions, which is normally faster than
> trying to solve the equations in the $DES. Why not start with this and 1-2
> ETAs on the CLs on days 1 and 7? If this simple model works, you can always
> move ahead and add ETAs.
> 
> Toufigh
> 
> On 3/4/12 8:51 PM, "Wang, Xiaofeng"   > wrote:
> 
>> Hi Katya,
>> 
>> Thank you. I double checked the units. mcg for AMT, ng/mL for DV, and L for
>> CL. I think there is no problem with the units. Since I am using
>> log-transformed data, does that influence the units?
>> 
>> I will try kicking in CLSS after day 1.  For 6 observations per patients,
>> what is the reasonable number of ETAs?
>> 
>> Best,
>> Xiaofeng
>> 
>> From: owner-nmus...@globomaxnm.com 
>> [owner-nmus...@globomaxnm.com  ] on behalf of Ekaterina
>> Gibiansky [egibian...@quantpharm.com  ]
>> Sent: Sunday, March 04, 2012 8:53 PM
>> To: 'nmusers@globomaxnm.com  '
>> Subject: Re: [NMusers] time-dependent clearance model
>> 
>> Xiaofeng,
>> 
>> Such a large difference suggests you may have a problem with units: check
>> AMT, DV, and the parameter S2 for consistency.
>> A couple of other points:
>> - If the full induction is expected by day 7, and you only have data on day 1
>> and after day 7, you may not be able to estimate KIN. You may be better off
>> with the model where there is CLI on day 1 and CLSS on days >1 (And you will
>> not need $DES for that model).
>> - You have 6 observations per subject and you have 7 ETAs, it is too many,
>> your model should be overparameterized (Check whether relative standard
>> errors are large).
>> 
>> Regards,
>> Katya
>> Ekaterina Gibiansky, Ph.D.
>> CEO&CSO, QuantPharm LLC
>> Web: www.quantpharm.com 
>> Email:   EGibiansky at quantpharm.com
>>  
>> 
>> On 3/4/2012 5:33 PM, Wang, Xiaofeng wrote:
>>> Dear nmusers,
>>>  
>>> I have a drug with clearance autoinduction. I have sparse data(three
>>> observations on day 1, two between day 7 and 14, and one on day 28).  I am
>>> trying to run a time-dependent clearance model. I tried FO and FOCEI, but I
>>> always got unreasonable estimate for the initial clearance (CLI) which is
>>> about 0.15 L/h (from knowledge of previous studies, the reasonable initial
>>> clearance should be around 20 L/h and maximum induction occurs around day
>>> 7). Could someone give me some advice about my model and my data? Thank you.
>>>  
>>> Xiaofeng 
>>>  
>>> The CTL file:
>>> $PROB 
>>> $INPUT C ID TIME AMT DV MDV EVID ADDL II CMT
>>> $DATA C:/ IGNORE=C
>>> $SUBROUTINES ADVAN6 TOL=6
>>> $MODEL NCOMPARTMENTS=3
>>>COMP=(DEPOT,DEFDOSE)
>>>COMP=(CENTRAL,DEFOBS)
>>>COMP=(PERIP)
>>> $PK
>>>CLI=THETA(1)*EXP(ETA(1))
>>>CLSS=THETA(2)*EXP(ETA(2))
>>>KIN=THETA(3)*EXP(ETA(3))
>>>V2= THETA(4)*EXP(ETA(4))
>>>Q= THETA(5)*EXP(ETA(5))
>>>V3= THETA(6)*EXP(ETA(6))
>>>KA= THETA(7)*EXP(ETA(7))
>>>S2=V2
>>>K23=Q/V2
>>>K32=Q/V3
>>>
>>> $DES
>>> CL=CLSS-(CLSS-CLI)*EXP(-KIN*T)
>>> K20=CL/V2
>>> DADT(1)=-KA*A(1)
>>> DADT(2)=KA*A(1)+K3

RE: [NMusers] time-dependent clearance model

[Mats Karlsson]

Dear Xiaofeng,

 

Presumably the CL at the start and at SS are correlated. If you in the model
assumed a lack of correlation, that may well be the cause of a variance
driven to zero. 

I agree with Toufigh that starting simple is usually a good idea. Given your
data sparsity, I would start very simple. Why not apply a one-compartment
model without CL induction. You can then extend to a model that has 2
compartments and separately one that has a CL changing with time (or even
better one that changes with concentration as was discussed recently on
nmusers). If both are models better than your simple starting model, you can
try combining the two. 

 

Best regards,

Mats

 

Mats Karlsson, PhD

Professor of Pharmacometrics

Dept of Pharmaceutical Biosciences

Faculty of Pharmacy

Uppsala University

Box 591

75124 Uppsala

 

Phone: +46 18 4714105

Fax + 46 18 4714003

 

From: owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] On
Behalf Of Toufigh Gordi
Sent: 05 March 2012 22:45
To: Wang, Xiaofeng; 'nmusers@globomaxnm.com'
Subject: Re: [NMusers] time-dependent clearance model

 

Whenever I start a modeling process, my first objective is to get the
structural model, i.e., your PK model in this case, well defined and in
place. If you start from there and you are pretty confident that you have
the right model, finding what parameters differ between subjects should not
be difficult. In your case with the very low ETA on CL1, how confident are
you about the model estimates, i.e., V3 or Q? Are they stay the same from
days 1 to ss? I can't really give any specific advice since I haven't seen
the results and know very little about the model and the fits.

Toufigh

On 3/5/12 1:30 PM, "Wang, Xiaofeng"  wrote:

Hi Toufigh,

Thank you for the suggestion. I tried the simplest model with 2 ETAs on the
pre- and post-induction clearance. The result gave very small omega estimate
for the pre-induction clearance (2.50e-005). But I don't believe that there
is no between subject variability for the pre-induction clearance. Could you
please give me some suggestion about this? Thank you.

Best,
Xiaofeng

  _  

From: Toufigh Gordi [tgo...@rosaandco.com]
Sent: Monday, March 05, 2012 2:30 AM
To: Wang, Xiaofeng; 'nmusers@globomaxnm.com'
Subject: Re: [NMusers] time-dependent clearance model

Xiaofeng,

It always amazes me how many parameters we try to estimate based on the
limited amount of data available. This is specially the case when a rather
complex event is taking place, such as autoinduction in this case, and that
based on sparse samples. In your case, there are a total of 15 parameters to
be estimated, which, as Katya points out seems to be way too many.

I would suggest that you start with the simplest model and then make it more
complex once you have the structural (i.e., your PK) model in place. Since
you have data only at pre- and post-induction, I think it is a good idea to
estimate different CL values for the two occasions. This way, you can use
the built-in library, with explicit solutions, which is normally faster than
trying to solve the equations in the $DES. Why not start with this and 1-2
ETAs on the CLs on days 1 and 7? If this simple model works, you can always
move ahead and add ETAs.

Toufigh

On 3/4/12 8:51 PM, "Wang, Xiaofeng"  > wrote:

Hi Katya,

Thank you. I double checked the units. mcg for AMT, ng/mL for DV, and L for
CL. I think there is no problem with the units. Since I am using
log-transformed data, does that influence the units?

I will try kicking in CLSS after day 1.  For 6 observations per patients,
what is the reasonable number of ETAs?

Best,
Xiaofeng

  _  

From: owner-nmus...@globomaxnm.com 
[owner-nmus...@globomaxnm.com  ] on behalf of
Ekaterina Gibiansky [egibian...@quantpharm.com  ]
Sent: Sunday, March 04, 2012 8:53 PM
To: 'nmusers@globomaxnm.com  '
Subject: Re: [NMusers] time-dependent clearance model

Xiaofeng,

Such a large difference suggests you may have a problem with units: check
AMT, DV, and the parameter S2 for consistency.
A couple of other points:
- If the full induction is expected by day 7, and you only have data on day
1 and after day 7, you may not be able to estimate KIN. You may be better
off with the model where there is CLI on day 1 and CLSS on days >1 (And you
will not need $DES for that model). 
- You have 6 observations per subject and you have 7 ETAs, it is too many,
your model should be overparameterized (Check whether relative standard
errors are large).

Regards,
Katya
Ekaterina Gibiansky, Ph.D.
CEO&CSO, QuantPharm LLC
Web: www.quantpharm.com  
Email:   EGibiansky at quantpharm.com
 

On 3/4/2012 5:33 PM, Wang, Xiaofeng wrote: 

Dear nmusers,
 
I have a drug with clearance autoinduction. I have sparse data(three
observations on day 1, two between day 7 and 14, and one on day 28).  I am
trying to run a time-dependent clearance model. I tried FO and FOCEI, but I
always got unreasonable estimate for the initi