NONMEM Users Network Archive

Hosted by Cognigen

Re: Missing value when modeling categorical data

From: Nick Holford <n.holford>
Date: Thu, 08 Oct 2009 07:56:14 -0400

Tianli,

Your data sounds like it could be described by a survival analysis. A
time to event model will give you the survivor function i.e. the prob of
not having had the event as a function of time. The event in your case
is defined by the 'certain PD score'. The censoring will of course be
taken care of by a typical survival analysis.

Nick

wangx826
> Dear NMUsers,
>
> I am modeling ordered-categorical PD data versus time, but since the
> clinical trial is ongoing, I don't currently have complete data set
> for each subject. In other words, for some subjects, I have 1 year's
> PD data, but for some others, I can only collect PD data for 1 month.
> For the 1 month's case, it is like missing data for the rest of time.
> But I need to consider time course of the proportion of subjects who
> got a certain PD score. In my case, if I use the general logistic
> regression model to fit the relationship between time and proportions
> of event, would there be any problem? If so, how can I avoid it? Or
> need I consider censoring like survival analysis?
>
> Any suggestion would be appreciated very much.
>
> Thanks in advance,
> Tianli
> *****************************************************************
> Tianli Wang
> University of Minnesota

--
Nick Holford, Professor Clinical Pharmacology
Dept Pharmacology & Clinical Pharmacology
University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New Zealand
n.holford
mobile: +64 21 46 23 53
http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford
Received on Thu Oct 08 2009 - 07:56:14 EDT

The NONMEM Users Network is maintained by ICON plc. Requests to subscribe to the network should be sent to: nmusers-request@iconplc.com.

Once subscribed, you may contribute to the discussion by emailing: nmusers@globomaxnm.com.