Re: How to model delayed count data due to time needed to form an event
Dear Yaming,
My 2 cents ...
You may need a joint model for time to first AE with left and right
censoring (see tutorial on TTE from Holford and Lavieille last year at
PAGE) and a model for your count data.
This paper may help, although it does not fully cover your case:
Ito K, Hutmacher M, Liu J, Qiu R, Frame B, Miller R. Exposure-response
analysis for spontaneously reported dizziness in pregabalin-treated
patient with generalized anxiety disorder. Clin Pharmacol Ther. 2008
Jul;84(1):127-35.
Kind regards / Mit freundlichen Grüßen / Bien cordialement
Pascal Girard, PhD
[email protected]
Head of Modeling & Simulation - Oncology
Global Exploratory Medicine
Merck Serono S.A. · Geneva
Tel: +41.22.414.3549
Cell: +41.79.508.7898
Quoted reply history
From: Yaming Hang <[email protected]>
To: "[email protected]" <[email protected]>
Date: 18/06/2012 17:42
Subject: [NMusers] How to model delayed count data due to time
needed to form an event
Sent by: [email protected]
Dear NONMEM Users,
I’m dealing with some longitudinal count data (number of AEs observed in
fixed time interval over certain time period). One feature with the data
is the delay in PK/PD relationship because it takes time to develop the AE
(i.e. there is lag time between the start of the AE formation process and
the time you can observe the AE). The onset of the process of developing
an AE is clearly PK dependent, but the exact time (or PK level) it onsets
can never be observed. In addition, the exact time that the AE first
appears is also not available, we only know the interval during which it
first appears. What is a good approach to handle this kind of delay? Is
there any published paper for similar data type?
Many thanks in advance for you suggestions!
Yaming Hang, Ph.D.
Pharmacometrics
Biogen Idec
14 Cambridge Center
Cambridge, MA 02142
Office: 781-464-1741
Fax: 617-679-2804
Email: [email protected]
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