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RE: modeling binary observations

From: Matt Hutmacher <matt.hutmacher>
Date: Fri, 12 Sep 2008 14:02:53 -0400

Hello Dr. Zhang,

 

The first reference below discusses some issues and interpretations using
CAUC to model binary (0/1) events. In general, CAUC, being non-mechanistic,
can have trouble identifying sources of delay, which can have impact on
decision-making. Also, CAUC would have difficulty predicting the responses
of subjects should they be withdrawn from medication (unless some function
were used to un-accumulate the AUC). In the case that we studied, it did
provide reasonable predictions of the responses. So one might infer from
this that covariate analysis used for certain predictions might not be
problematic.

 

The second reference provides a method for constructing semi-mechanistic (?)
models. The method is based on the idea of an unobserved (or unobservable)
continuous variable. The semi-mechanistic models are built with respect to
this 'latent' variable. This method, given sufficient data, should be able
to parttion delay between PK (keo) or PD (kin,kout) sources, which could
have an impact on decision making and dosing strategies.



1.) Hutmacher, Matthew M., Nestorov, Ivan, Ludden, Tom, Zitnik, Ralph,
Banfield, Christopher
Modeling the Exposure-Response Relationship of Etanercept in the Treatment
of Patients With Chronic Moderate to Severe Plaque Psoriasis
J Clin Pharmacol 2007 47: 238-248

2.) Exposure-response modeling using latent variables for the efficacy of a
JAK3 inhibitor administered to rheumatoid arthritis patients

Matthew M. Hutmacher, Sriram Krishnaswami and Kenneth G. Kowalski Volume 35,
Number 2 / April, 2008

Hope these help.

Kind regards,

 

Matt

 

From: owner-nmusers
Behalf Of Zhang, Yi [CNTUS]
Sent: Friday, September 12, 2008 1:37 PM
To: nmusers
Subject: [NMusers] modeling binary observations

 

Dear All :
  
I'd like to get your opinion on this: when modeling an event (0/1) that is
caused by an cumulative effect, 1. would cumulative AUC be a better
predictor than concentration, since CAUC can better capture the accumulative
process? 2. If CAUC is used, is there good ways to implement
mechanistic/semi-mechanistic models rather than empirical approaches for
binary data?

3. Can the model results from CAUC be extrapolated to other trial designs,
why or why not?
Your input is appreciated. Thanks.

Yi Zhang
Centocor

 


Received on Fri Sep 12 2008 - 14:02:53 EDT

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