From: Leonid Gibiansky <*LGibiansky*>

Date: Thu, 08 Oct 2009 17:51:37 -0400

Tianli,

Let me check whether I understood your data correctly:

You have a trial where subjects enroll over time. You collected data up

to some calendar date. Since subjects were enrolled over time, you have

unequal amount of data for each subject. However, period of observation

does not depend on the observed PD scores.

If this is a correct description, then your data are missing completely

at random (probability of an observation being missing does not depend

on observed or unobserved measurements). In this case, you may analyze

them ignoring missingness: just build your model with all the available

data.

ACoP conference that just has ended had a good section on missing data

(http://www.go-acop.org/acop2009/program). You may look at the

presentations there, or just on some literature.

Thanks

Leonid

--------------------------------------

Leonid Gibiansky, Ph.D.

President, QuantPharm LLC

web: www.quantpharm.com

e-mail: LGibiansky at quantpharm.com

tel: (301) 767 5566

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
*

*> *

Received on Thu Oct 08 2009 - 17:51:37 EDT

Date: Thu, 08 Oct 2009 17:51:37 -0400

Tianli,

Let me check whether I understood your data correctly:

You have a trial where subjects enroll over time. You collected data up

to some calendar date. Since subjects were enrolled over time, you have

unequal amount of data for each subject. However, period of observation

does not depend on the observed PD scores.

If this is a correct description, then your data are missing completely

at random (probability of an observation being missing does not depend

on observed or unobserved measurements). In this case, you may analyze

them ignoring missingness: just build your model with all the available

data.

ACoP conference that just has ended had a good section on missing data

(http://www.go-acop.org/acop2009/program). You may look at the

presentations there, or just on some literature.

Thanks

Leonid

--------------------------------------

Leonid Gibiansky, Ph.D.

President, QuantPharm LLC

web: www.quantpharm.com

e-mail: LGibiansky at quantpharm.com

tel: (301) 767 5566

wangx826

Received on Thu Oct 08 2009 - 17:51:37 EDT