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Date:         Fri, 17 Jul 2009 09:33:35 -0400
Reply-To:     Kevin Viel <citam.sasl@GMAIL.COM>
Sender:       "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From:         Kevin Viel <citam.sasl@GMAIL.COM>
Subject:      Basics of pharma studies (EC50)


I have a question about the basics of time-to-response studies for various doses within an agent or for various agents. I will reference the following paper:

Dose-time-response cumulative multinomial generalized linear model. Chen DG. J Biopharm Stat. 2007;17(1):173-85.

For what I gather, a basic shape of a response curve is assumed, conventionally, it may be the logistic (sigmoid) curve. To frame my thoughts, consider that I what to test whether the EC50 is statistically different between two curves (doses or agents).

Initially, I thought that I might just add parameters to a model that would result in different estimates of beta (consider the simple case). I am not seeing that in the brief literature review I performed. Unfortunately, the number of journals to which I have full text articles is more limited than I desired and I have yet to establish access to a large library.

How does this thought strike those in the field? It might be akin to a random coefficient model, for instance.

My question concerning the Chen paper is why would one use probabilities for what is essentially survival (continuous time-to-event) data? In fact, Chen mentions a potential pitfall him- or herself: namely, rates that change over time. For instance, for days 0-3 the rate might be x, for days 4-6 the rate might be y, and for days 7+ the rate might be z.

I would appreciate any comments or references.

Kind regards,


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