RE: Modeling biomarker data below the LOQ
Dear Leonid,
The extra error term for the BQL observations was estimated. This extra
error term was only added for Model A. In the other examples there was
already an additive part in the residual error that sufficiently relaxed the
assumption of the imputation.
I am not a big fan of imputations of BQL samples and think that they should
be avoided in favor of the M3 or M4 method as often as possible. However if
practical issues (probably disappearing as estimation methods and
computational power are improving) hinder you from using the likelihood
based methods I suggest to do a small sensitivity analysis regarding what
imputation to chose. There are no possibility to make general
recommendations on what imputations (LOQ/2 etc.) that will result in
unbiased estimates.
The first thing you should do is to evaluate the simulation properties of
the obtained model parameters. In the article that you refer to we have
described how we think that VPC are best done for datasets with censored
observations such as BQL. If a certain imputation is chosen and the final
model demonstrate good simulation properties both for the contentious
observations (above LOQ) and for the fraction of BQL samples this is a good
indication that the obtained parameter estimates are likely to be fairly
unbiased.
I have also thought of more elaborate approaches with simulation and
re-estimation with different imputations of BQL observations to evaluate the
possible biased introduced by substitution the BQL samples with an assumed
value (similar principle as the Back step method described by Kjellsson MC
et.al. (1)). However this is nothing that I have tested or think is an
attractive solution for many cases.
(1) Kjellsson MC, Jönsson S, Karlsson MO. The back-step method--method for
obtaining unbiased population parameter estimates for ordered categorical
data. AAPS J. 2004 Aug 11;6(3):e19.
By the way I don't agree with your conclusion that LOQ/2 substitution
provides reasonable results in the indirect response model example (C). It
induces a non negligible amount of bias in both the concentration effect
parameter (SLOP) and Kout (5-15%). In my opinion substitution with LOQ/2 are
just as bad as omitting the BQL data (just bad in another way) for that very
example.
Kind regards,
Martin Bergstrand, MSc, PhD student
-----------------------------------------------
Pharmacometrics Research Group,
Department of Pharmaceutical Biosciences,
Uppsala University
-----------------------------------------------
P.O. Box 591
SE-751 24 Uppsala
Sweden
-----------------------------------------------
[email protected]
-----------------------------------------------
Work: +46 18 471 4639
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Fax: +46 18 471 4003
Quoted reply history
-----Original Message-----
From: Leonid Gibiansky [mailto:[email protected]]
Sent: den 20 november 2009 04:01
To: Mats Karlsson
Cc: 'Doshi, Sameer'; 'nmusers'; Martin Bergstrand
Subject: Re: [NMusers] Modeling biomarker data below the LOQ
Mats, Martin,
In the paper, you mentioned that "an extra additive error
model term was added for samples substituted with LOQ/2". Was it fixed
or estimated? If fixed, how? Have you tried to vary this level?
In many of your examples, LOQ/2 imputations and exclusion of BQL samples
seen to lead to bias in opposite directions; if so, it could be an
optimal value (relative to LOQ) of the fixed extra error term that
provides the least biased parameters.
Laplacian method is often not feasible for receptor (target) models
since they are strongly nonlinear (thus requiring differential
equations) and stiff. Based on the the indirect-response model
simulations considered in your paper, LOQ/2 substitution seems to
provide reasonable results if Laplacian (and thus M2-M3-M4) cannot be used.
Thanks
Leonid
--------------------------------------
Leonid Gibiansky, Ph.D.
President, QuantPharm LLC
web: www.quantpharm.com
e-mail: LGibiansky at quantpharm.com
tel: (301) 767 5566
Mats Karlsson wrote:
> Dear Sameer,
>
>
>
> Weve had this problem with biomarker data and published experiences in
> terms of a methodological paper (below). Maybe it can give you some ideas.
>
>
>
> Handling data below the limit of quantification in mixed effect models.
>
> Bergstrand M, Karlsson MO.
>
> AAPS J. 2009 Jun;11(2):371-80. Epub 2009 May 19.
>
>
>
> Best regards,
>
> Mats
>
>
>
> Mats Karlsson, PhD
>
> Professor of Pharmacometrics
>
> Dept of Pharmaceutical Biosciences
>
> Uppsala University
>
> Box 591
>
> 751 24 Uppsala Sweden
>
> phone: +46 18 4714105
>
> fax: +46 18 471 4003
>
>
>
> *From:* [email protected]
> [mailto:[email protected]] *On Behalf Of *Doshi, Sameer
> *Sent:* Wednesday, November 18, 2009 6:53 PM
> *To:* nmusers
> *Subject:* [NMusers] Modeling biomarker data below the LOQ
>
>
>
> Hello,
>
> We are attempting to model suppression of a biomarker, where a number of
> samples (40-60%) are below the quantification limit of the assay and
> where 2 different assays (with different quantification limits) were
> used. We are trying to model these BQL data using the M3 and M4 methods
> proposed by Ahn et al (2008).
>
>
>
> I would like to know if anyone has any comments or experience
> implementing the M3 or M4 methods for biomarker data, where levels are
> observed at baseline, are supressed below the LOQ for a given duration,
> and then return to baseline.
>
>
>
> Also please advise if there are other methods to try and incorporate
> these BQL data into the model.
>
>
>
> I have included the relevant pieces of the control file (for both M3 and
> M4) and data from a single subject.
>
>
>
> Thanks for your review/suggestions.
>
>
>
> Sameer
>
>
>
> DATA:
>
> #ID TIME AMT DV CMT EVID TYPE ASSY
>
> 1 0 0 65.71 0 0 0 1
>
> 1 0 120 0 3 1 0 1
>
> 1 168 0 10 0 0 1 1
>
> 1 336 0 10 0 0 1 1
>
> 1 336 120 0 3 1 0 1
>
> 1 504 0 12.21 0 0 0 1
>
> 1 672 120 0 3 1 0 1
>
> 1 1008 0 10 0 0 1 1
>
> 1 1008 120 0 3 1 0 1
>
> 1 1344 0 10 0 0 1 1
>
> 1 1344 120 0 3 1 0 1
>
> 1 1680 0 10 0 0 1 1
>
> 1 1680 120 0 3 1 0 1
>
> 1 2016 0 10 0 0 0 1
>
> 1 2352 0 25.64 0 0 0 1
>
> 1 2688 0 59.48 0 0 0 1
>
>
>
> MODEL M3:
>
> $DATA data.csv IGNORE=#
>
> $SUB ADVAN8 TRANS1 TOL=6
>
> $MODEL
>
> COMP(central)
>
> COMP(peri)
>
> COMP(depot,DEFDOSE)
>
> COMP(effect)
>
>
>
> $DES
>
> DADT(1) = KA*A(3) - K10*A(1) - K12*A(1) + K21*A(2)
>
> DADT(2) = K12*A(1) - K21*A(2)
>
> DADT(3) = -KA*A(3)
>
> CONC = A(1)/V1
>
> DADT(4) = KEO*(CONC-A(4))
>
>
>
> $ERROR
>
> CALLFL = 0
>
>
>
> LOQ1=10
>
> LOQ2=20
>
>
>
> EFF = BL* (1 - IMAX*A(4)**HILL/ (IC50**HILL+A(4)**HILL))
>
> IPRED=EFF
>
> SIGA=THETA(7)
>
> STD=SIGA
>
> IF(TYPE.EQ.0) THEN ; GREATER THAN LOQ
>
> F_FLAG=0
>
> Y=IPRED+SIGA*EPS(1)
>
> IRES =DV-IPRED
>
> IWRES=IRES/STD
>
> ENDIF
>
> IF(TYPE.EQ.1.AND.ASSY.EQ.1) THEN ; BELOW LOQ1
>
> DUM1=(LOQ1-IPRED)/STD
>
> CUM1=PHI(DUM1)
>
> F_FLAG=1
>
> Y=CUM1
>
> IRES = 0
>
> IWRES=0
>
> ENDIF
>
> IF(TYPE.EQ.1.AND.ASSY.EQ.2) THEN ; BELOW LOQ2
>
> DUM2=(LOQ2-IPRED)/STD
>
> CUM2=PHI(DUM2)
>
> F_FLAG=1
>
> Y=CUM2
>
> IRES = 0
>
> IWRES=0
>
> ENDIF
>
>
>
> $SIGMA 1 FIX
>
>
>
> $ESTIMATION MAXEVAL=9990 NOABORT SIGDIG=3 METHOD=1 INTER LAPLACIAN
>
> POSTHOC PRINT=2 SLOW NUMERICAL
>
> $COVARIANCE PRINT=E SLOW
>
>
>
> MODEL M4:
>
> $DATA data.csv IGNORE=#
>
> $SUB ADVAN8 TRANS1 TOL=6
>
> $MODEL
>
> COMP(central)
>
> COMP(peri)
>
> COMP(depot,DEFDOSE)
>
> COMP(effect)
>
>
>
> $DES
>
> DADT(1) = KA*A(3) - K10*A(1) - K12*A(1) + K21*A(2)
>
> DADT(2) = K12*A(1) - K21*A(2)
>
> DADT(3) = -KA*A(3)
>
> CONC = A(1)/V1DADT(4) = KEO*(CONC-A(4))
>
>
>
> $ERROR
>
> CALLFL = 0
>
>
>
> LOQ1=10
>
> LOQ2=20
>
>
>
> EFF = BL* (1 - IMX*A(4)**HILL/ (IC50**HILL+A(4)**HILL))
>
> IPRED=EFF
>
> SIGA=THETA(7)
>
> STD=SIGA
>
> IF(TYPE.EQ.0) THEN ; GREATER THAN LOQ
>
> F_FLAG=0
>
> YLO=0
>
> Y=IPRED+SIGA*EPS(1)
>
> IRES =DV-IPRED
>
> IWRES=IRES/STD
>
> ENDIF
>
> IF(TYPE.EQ.1.AND.ASSY.EQ.1) THEN
>
> DUM1=(LOQ1-IPRED)/STD
>
> CUM1=PHI(DUM1)
>
> DUM0=-IPRED/STD
>
> CUMD0=PHI(DUM0)
>
> CCUMD1=(CUM1-CUMD0)/(1-CUMD0)
>
> F_FLAG=1
>
> Y=CCUMD1
>
> IRES = 0
>
> IWRES=0
>
> ENDIF
>
> IF(TYPE.EQ.1.AND.ASSY.EQ.2) THEN
>
> DUM2=(LOQ2-IPRED)/STD
>
> CUM2=PHI(DUM2)
>
> DUM0=-IPRED/STD
>
> CUMD0=PHI(DUM0)
>
> CCUMD2=(CUM2-CUMD0)/(1-CUMD0)
>
> F_FLAG=1
>
> Y=CCUMD2
>
> IRES = 0
>
> IWRES=0
>
> ENDIF
>
>
>
> $SIGMA 1 FIX
>
>
>
> $ESTIMATION MAXEVAL=9990 NOABORT SIGDIG=3 METHOD=1 INTER LAPLACIAN
>
> POSTHOC PRINT=2 SLOW NUMERICAL
>
> $COVARIANCE PRINT=E SLOW
>
>
>
>
>
>
>
>
>
> Sameer Doshi
>
> Pharmacokinetics and Drug Metabolism, Amgen Inc.
>
> (805) 447-6941
>
>
>
>
>
>
>
>
>