Dear NMUSERS,#
I would like to ask for some opinions regarding the handling of missing lab
values in a NONMEM Dataset;
Our normal procedure:
Parameter values will be carried backward to the first visit if the first visit
value is missing, it will be carry forward to the last visit if no value is
available at the last visit and will be set at the median value of two adjacent
visits in other cases.
Now we have a phase III study (multiple doses), one safety lab at day -1 and
one safety lab at final examination only, no lab in between (>6 month)
Two main strategies are possible
1.) Different from our standard procedure:
Carry the lab value at final examination backward to day -1.
2.) According to our standard: Use the median (or perhaps a regression
between the first and final examination)
:
My assumptions:
The first strategy might be useful to reflect the influence of the drug on lab
values and will reflect the steady state situation.
The second strategy might be better to characterize the influence of the lab
values on the PK of the drug, e.g if a disease worsens during the study.
As our main focus will be the last one, I would use the standard approach.
I know that this is quite basic, however as this was discussed during a meeting
I would appreciate to have your opinion.
Many thanks in advance
Dirk
Dirk Garmann, PhD
Clinical Scientific Expert /Pharmacokineticist
Merz Pharmaceuticals
Eckenheimer Landstrasse 100
60318 Frankfurt
Phone +49 (69) 1503 720
Merz Pharmaceuticals GmbH, Frankfurt am Main
Amtsgericht Frankfurt am Main, HRB 53808
Geschäftsführung: Dr. Martin Zügel (Vors.), Dr. Alexander Gebauer,
Dr. Karsten Schlemm, Dr. Eugen Wilbert
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lab values
Dirk,
I think the approach is influenced by what this lab value represents. If it is
a biomarker/endpoint that is influenced by drug treatment then the best
approach is to include this in your PK-PD model as a dependent variable. If you
treat this as a traditional covariate it should not be influenced by treatment.
Assuming your drug improves disease symptom or progression (as measured by this
biomarker) it would not be ideal to use either LOCF or LOCB. The baseline for
this biomarker (DAY -1 in your case) can be used as a covariate in your PK
model, as it is not influenced by drug treatment.
If you can not spend the time to build a proper PK-PD model but still believe
this covariate is important for your PK model then maybe you can do something
simple, like assuming a linear slope in this biomarker between the two
measurements and use the two observed values for interpolation?
Best regards
Jakob
Quoted reply history
________________________________
From: [email protected] [mailto:[email protected]] On
Behalf Of Garmann, Dirk
Sent: 13 January 2010 12:41
To: [email protected]
Subject: [NMusers] lab values
Dear NMUSERS,#
I would like to ask for some opinions regarding the handling of missing lab
values in a NONMEM Dataset;
Our normal procedure:
Parameter values will be carried backward to the first visit if the first visit
value is missing, it will be carry forward to the last visit if no value is
available at the last visit and will be set at the median value of two adjacent
visits in other cases.
Now we have a phase III study (multiple doses), one safety lab at day -1 and
one safety lab at final examination only, no lab in between (>6 month)
Two main strategies are possible
1.) Different from our standard procedure:
Carry the lab value at final examination backward to day -1.
2.) According to our standard: Use the median (or perhaps a regression
between the first and final examination)
:
My assumptions:
The first strategy might be useful to reflect the influence of the drug on lab
values and will reflect the steady state situation.
The second strategy might be better to characterize the influence of the lab
values on the PK of the drug, e.g if a disease worsens during the study.
As our main focus will be the last one, I would use the standard approach.
I know that this is quite basic, however as this was discussed during a meeting
I would appreciate to have your opinion.
Many thanks in advance
Dirk
Dirk Garmann, PhD
Clinical Scientific Expert /Pharmacokineticist
Merz Pharmaceuticals
Eckenheimer Landstrasse 100
60318 Frankfurt
Phone +49 (69) 1503 720
________________________________
Merz Pharmaceuticals GmbH, Frankfurt am Main
Amtsgericht Frankfurt am Main, HRB 53808
Geschäftsführung: Dr. Martin Zügel (Vors.), Dr. Alexander Gebauer,
Dr. Karsten Schlemm, Dr. Eugen Wilbert
________________________________
Die vorgenannten Angaben der E-Mail haben grundsätzlich nur informativen
Charakter. Dies ist kein Anerkenntnis, dass es sich beim Inhalt dieser E-Mail
um eine rechtsverbindliche Erklärung der entsprechenden Gesellschaft der Merz
Gruppe handelt, es sei denn dies ist ausdrücklich als solches formuliert.
Erklärungen, die eine Gesellschaft der Merz Gruppe verpflichten sollen,
bedürfen jeweils der Unterschrift durch zwei zeichnungsberechtigte Personen
dieser Gesellschaft.
Diese E-Mail enthält vertrauliche und/oder rechtlich geschützte Informationen.
Wenn Sie nicht der richtige Adressat sind oder diese E-Mail irrtümlich erhalten
haben, informieren Sie bitte sofort den Absender und vernichten Sie diese
E-Mail. Das unerlaubte Kopieren sowie die unbefugte Weitergabe dieser E-Mail
ist nicht gestattet.
Dear Dirk and Jakob,
I agree with all of Jakobs suggestions but would like to come with an
additional suggestion. If you are going to use any type of imputation method
(LOCF, LOCB, linear interpolation, random imputation, baseline imputation,
substitution with population/individual median etc.) then create different
data-set using several imputation methods (that at least make some sense).
Choose the data-set with your primary choice of imputation and perform your
model building with this data-set. With you final model it can be wise to
make sure that the conclusions and parameter estimates based on this model
is not heavily dependent on the imputations made in your data-set. To do
this you can re-estimate parameter estimates of your final model using
data-sets with one or more alternative imputation methods. Similarly you can
reassess important likelihood ratio tests with the alternative data-sets.
Last I would like to point out that the imputation methods all rely on the
fact that the missingness of the observation is completely random and not
dependent on your primary dependent variable of interest. If this is
suspected only simultaneous modeling of the two variables are likely to give
unbiased results.
Best regards,
Martin Bergstrand, MSc, PhD student
-----------------------------------------------
Pharmacometrics Research Group,
Department of Pharmaceutical Biosciences,
Uppsala University
-----------------------------------------------
<mailto:[email protected]> [email protected]
-----------------------------------------------
Quoted reply history
From: [email protected] [mailto:[email protected]] On
Behalf Of Ribbing, Jakob
Sent: den 13 januari 2010 14:34
To: Garmann, Dirk; [email protected]
Subject: RE: [NMusers] lab values
Dirk,
I think the approach is influenced by what this lab value represents. If it
is a biomarker/endpoint that is influenced by drug treatment then the best
approach is to include this in your PK-PD model as a dependent variable. If
you treat this as a traditional covariate it should not be influenced by
treatment. Assuming your drug improves disease symptom or progression (as
measured by this biomarker) it would not be ideal to use either LOCF or
LOCB. The baseline for this biomarker (DAY -1 in your case) can be used as a
covariate in your PK model, as it is not influenced by drug treatment.
If you can not spend the time to build a proper PK-PD model but still
believe this covariate is important for your PK model then maybe you can do
something simple, like assuming a linear slope in this biomarker between the
two measurements and use the two observed values for interpolation?
Best regards
Jakob
_____
From: [email protected] [mailto:[email protected]] On
Behalf Of Garmann, Dirk
Sent: 13 January 2010 12:41
To: [email protected]
Subject: [NMusers] lab values
Dear NMUSERS,#
I would like to ask for some opinions regarding the handling of missing lab
values in a NONMEM Dataset;
Our normal procedure:
Parameter values will be carried backward to the first visit if the first
visit value is missing, it will be carry forward to the last visit if no
value is available at the last visit and will be set at the median value of
two adjacent visits in other cases.
Now we have a phase III study (multiple doses), one safety lab at day -1 and
one safety lab at final examination only, no lab in between (>6 month)
Two main strategies are possible
1.) Different from our standard procedure:
Carry the lab value at final examination backward to day -1.
2.) According to our standard: Use the median (or perhaps a regression
between the first and final examination)
:
My assumptions:
The first strategy might be useful to reflect the influence of the drug on
lab values and will reflect the steady state situation.
The second strategy might be better to characterize the influence of the lab
values on the PK of the drug, e.g if a disease worsens during the study.
As our main focus will be the last one, I would use the standard approach.
I know that this is quite basic, however as this was discussed during a
meeting I would appreciate to have your opinion.
Many thanks in advance
Dirk
Dirk Garmann, PhD
Clinical Scientific Expert /Pharmacokineticist
Merz Pharmaceuticals
Eckenheimer Landstrasse 100
60318 Frankfurt
Phone +49 (69) 1503 720
_____
Merz Pharmaceuticals GmbH, Frankfurt am Main
Amtsgericht Frankfurt am Main, HRB 53808
Geschäftsführung: Dr. Martin Zügel (Vors.), Dr. Alexander Gebauer,
Dr. Karsten Schlemm, Dr. Eugen Wilbert
_____
Die vorgenannten Angaben der E-Mail haben grundsätzlich nur informativen
Charakter. Dies ist kein Anerkenntnis, dass es sich beim Inhalt dieser
E-Mail um eine rechtsverbindliche Erklärung der entsprechenden Gesellschaft
der Merz Gruppe handelt, es sei denn dies ist ausdrücklich als solches
formuliert. Erklärungen, die eine Gesellschaft der Merz Gruppe verpflichten
sollen, bedürfen jeweils der Unterschrift durch zwei zeichnungsberechtigte
Personen dieser Gesellschaft.
Diese E-Mail enthält vertrauliche und/oder rechtlich geschützte
Informationen. Wenn Sie nicht der richtige Adressat sind oder diese E-Mail
irrtümlich erhalten haben, informieren Sie bitte sofort den Absender und
vernichten Sie diese E-Mail. Das unerlaubte Kopieren sowie die unbefugte
Weitergabe dieser E-Mail ist nicht gestattet.