RE: Truncated Emax
Hi Martin,
Thank you for pointing this out. I actually do agree with you! I certainly did not imply that deleting censored data is a guarantee for unbiased results.
But please keep in mind that especially with pain censoring is not arbitrary. It is actually a meaningful border, for example unbearable pain or perhaps safety of currents in this case. And as I referred to, I have compared models for pain with deletion or with M3 but could not find any difference in results even with a high amount of censoring. My finding surprised me at first, but when I discussed this with our residential senior statistician he told me that this, unbiased results after deleting of censored data, was common experience.
I would be curious about experience from others on this list! Please do share in if you have seen results one way or the other.
Best regards,
Jeroen
Quoted reply history
________________________________
From: Martin Bergstrand [mailto:martin.bergstrand
Sent: Tuesday, December 20, 2011 08:27
To: 'Francois Gaudreault'; nmusers
Cc: 'Waqas Sadiq'; Elassaiss - Schaap, J. (Jeroen)
Subject: RE: [NMusers] Truncated Emax
Dear Franois,
I do not agree with Jeroen that less than ~1/3 of total data censored is a guarantee for that these observations can be ignored without substantial bias. I think this is highly dependent on the nature of the model (system), the limit of quantification in relationship to Emax etc. To make a statement on what percentage of censored data (out of the total) that will result in negligible bias is never a good idea since it might be that only a small portion of the total data speaks to a specific parameter. If a substantial amount of that small portion of data is censored it can have important implications while it is still just a minor percentage that is missing out of the total. But importantly you do not need to take anyone's word for this since you can test it you self with simulation based diagnostics and/or simulation and re-estimation with the applied censoring.
The way that I would go about this issue is that I would take into account also the censored observations. The below code is just a slight alteration of the M3 method suggested by S. Beal for the handling of observations below the limit o detection (BQL)[1]. More detail on how this is best implemented in NONMEM is given in a paper by Anh et.al [2]. Me and others have also several times discussed how to best diagnose models in the presence of censored observations (see NMusers archive).
;;; ---------------------------------------------------------
$ERROR
W = THETA(.) ; Residual error model (in this example simple additive)
ULOQ = 10 ; Upper limit of detection (10mA)
IPRED = PT ; Individual prediction of perception threshold according to your desired model
DUM = (IPRED-ULOQ)/SIG
CUMD = PHI(DUM)
; Flag variable CENS in dataset. CENS=1 => observation >ULOQ
IF(CENS.EQ.0) THEN ; <ULOQ
F_FLAG = 0
Y = IPRED+SIG*ERR(1)
ENDIF
IF(ALQ.EQ.1) THEN ; >ULOQ
F_FLAG = 1
Y = CUMD
ENDIF
;;; ---------------------------------------------------------
Obs. When applying this code the SIGMA variance is fixed to 1 ($SIGMA 1 FIX) and the Lapalcian estimation option needs to be utilized (or possibly SAEM etc.) [2].
This type of coding have previously been successfully applied by my colleague Waqas Sadiq. A manuscript on this project is currently in preparation and might be referenced once published (look out).
[1] Beal SL. Ways to fit a PK model with some data below the quantification limit. J Pharmacokinet Pharmacodyn. 2001 Oct;28(5):481-504.
[2] Ahn JE, Karlsson MO, Dunne A, Ludden TM. Likelihood based approaches to handling data below the quantification limit using NONMEM VI. J Pharmacokinet Pharmacodyn. 2008 Aug 7.
Kind regards,
Martin Bergstrand, PhD
Pharmacometrics Research Group
Dept of Pharmaceutical Biosciences
Uppsala University, Sweden
martin.bergstrand
Visiting scientist:
Mahidol-Oxford Tropical Medicine Research Unit,
Bangkok, Thailand
Phone: +66 8 9796 7611
From: owner-nmusers
Behalf Of Elassaiss - Schaap, J. (Jeroen)
Sent: Tuesday, December 20, 2011 4:12 AM
To: Francois Gaudreault; nmusers
Subject: RE: [NMusers] Truncated Emax
Hi Francois,
For pain measurements it is not uncommon to analyze data with a upper limit of quantitation. You can follow the literature on BQL, only reversing from a lower limit to an upper limIt. In my experience just deleting censored data works fine, certainly as a first attempt, as long as censoring stays below ~1/3 of total data.
Best regards,
Jeroen
J. Elassaiss-Schaap
Scientist PK/PD
MSD
PO Box 20, 5340 BH Oss, Netherlands
Phone: + 31 412 66 9320
Fax: + 31 412 66 2506
e-mail: jeroen.elassaiss
________________________________
From: owner-nmusers
ilto:owner-nmusers
Sent: Monday, December 19, 2011 21:40
To: nmusers
Subject: [NMusers] Truncated Emax
Dear NM users
I am currently developing a PK PD model for local anesthetics using a sequential approach with ADVAN6. The PD model is a sigmoid Emax with an effect compartment (Ce).
The intensity and duration of nerve blockade are monitored throughout the perioperative period in patients using a quantitative pharmacodynamic endpoint, i.e, the current perception threshold (CPT) REF: Can. J. Anesth, 57 (S1) 2010). Briefly, CPT is evaluated before and after the administration of the local anesthectic. Data are normalized by baseline using the following equation :
(observed-baseline) / (max-baseline) *100 (%)
Here is the problem. The device only goes to a maximum of 10 mA. In some patients, the real Emax is much higher. Any ideas on how handle a truncated Emax ?
Thanks in advance
--
Franois Gaudreault, Ph.D. Candidate
Pharmacomtrie / Pharmacometrics
Charger de cours / Lecturer
Facult de pharmacie / Faculty of Pharmacy
Universit de Montral
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