Re: Missing value when modeling categorical data

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 [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 -- Nick Holford, Professor Clinical Pharmacology Dept Pharmacology & Clinical Pharmacology University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New Zealand [email protected] tel:+64(9)923-6730 fax:+64(9)373-7090 mobile: +64 21 46 23 53 http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford