RE: Visual predictive check!
All,
Would anyone be willing to comment on the applicability, or lack thereof, of
applying the various literature referenced techniques to PD/biomarker data,
including differences in assumptions and practical considerations?
Thank you,
Mark
Mark C. Peterson
Amgen Inc.
One Amgen Center Drive
MS 28-3-B
Thousand Oaks, CA 91320
Quoted reply history
________________________________
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of [EMAIL PROTECTED]
Sent: Saturday, May 24, 2008 4:58 PM
To: [EMAIL PROTECTED]
Cc: [email protected]
Subject: RE: [NMusers] Visual predictive check!
Ken and All,
The recent paper on JPP "Impact on censoring data below an arbitrary
quantification limit on structural model misspecification" 2008, 35:101-16, by
Byron, Fletcher and Brundage is still fully available on line and it speaks
volumes about bioanalytical motivated LLOQ and pharmacokinetics modeling. Just
for those who haven't read it, I vividly reccomend so.
Cheers
---------------------------------------------------------------
Luis M. Pereira, Ph.D.
Assistant Professor, Pharmacometrics
Massachusetts College of Pharmacy and Health Sciences
Childrens Hospital Boston / Harvard Medical School
179 Longwood Ave, Boston, MA 02115
Phone: (617) 732-2905
Fax: (617) 732-2228
________________________________
From: [EMAIL PROTECTED] on behalf of Ken Kowalski
Sent: Fri 5/23/2008 11:22 AM
To: 'Nick Holford'; [email protected]
Subject: RE: [NMusers] Visual predictive check!
Nick,
Yes, I'm making the assumption that a measured concentration cannot be
negative. Educate me about chemical assays. Can you get troughs rather
than peaks in a chromatogram such that the area below zero is integrated and
reported as a negative concentration? If so, what would happen if you
assayed a bunch of pre-dose samples (before drug is administered) where the
true mean concentration is zero? Would we get measured concentrations
symmetrically distributed about zero (with about 50% of the measured
concentrations reported as negative and 50% positive)? If so, then a normal
residual error model may indeed be appropriate.
Ken