Re: Missing value when modeling categorical data

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 [email protected] wrote: > 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