NONMEM Users Network Archive

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Re: Missing value when modeling categorical data

From: Nick Holford <n.holford>
Date: Thu, 08 Oct 2009 21:28:34 -0400

Tianli,

Please look carefully at this:

http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford/teaching/pharmacometrics/_docs/modelling_likelihoods_using_NONMEM_VI.pdf

It shows you how to do time to event models which also include PKPD
variables which change as a function of dose and time.

Nick

wangx826
> Dear Nick,
>
> I think I didn't make it clear in my first email that my model is a
> PK-PD linked model, which means, the probability of a certain event is
> a function of drug exposure which can describe as a function of time
> and some other parameters like CL, V, etc. But it seems to me that the
> hazard function only takes time into account. I am not familiar with
> survival analysis so this might be a very basic question: How can I
> combine the PK/PD model with the time-to-event model?
>
> Thanks in advance,
> Tianli
>
> On Oct 8 2009, Nick Holford wrote:
>
>> 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 - 21:28:34 EDT

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