--- On Thu, 6/19/08, Ryan <Ryan.Andrew.Black@GMAIL.COM> wrote:

> From: Ryan <Ryan.Andrew.Black@GMAIL.COM> > Subject: Re: GLIMMIX Question - Dependent Observations > To: SAS-L@LISTSERV.UGA.EDU > Date: Thursday, June 19, 2008, 7:29 PM > Thank you, Dale! You've helped me with so many questions > already. I > hope it's okay if I ask you two more... > > 1. The dichotomous variable in my model was collected at the subjects > level (not city level), and the categories are not mutually exclusive-- > there were people who fit into both categories. I'm not sure how to > handle this issue--one option I thought was to raise it to the city > level, and code the city as a particular category based on the higher > rate (by the way, DV (rate) and the continuous IV are functions of > data at the city level). So if the rate is higher in category one, > then that city is assigned category one. Would that work? Would you > recommend an alternative approach that can maintain the variable at > the city level? > > 2. As mentioned above, the DV (rate) and the continuous IV in my model > are functions of aggregated data. After you mentioned that a city with > less observations would be weighted less, I realized that all cases > would actually have equal weights at the city level. Is there a way to > deal with unequal Ns per case while maintaining city as the unit of > analysis for all variables? > > Anyway, I realize I've asked much of you. I completely understand if > you're too busy to respond. I appreciate your help. It's been a true > learning experience! > > Ryan >

Ryan,

I'm confused now. I don't know how your dependent variable (collected at the individual level) can take on two values and those two values are not mutually exclusive. It sounds to me as though there are two boxes that the respondent can check off, and that there are no constraints that if they check box 1 then they cannot check box 2 (and vice versa).

To me, that would represent two (almost certainly correlated) binary responses. I would be looking at modeling the binary responses at the individual level with the person-specific IV as a predictor. At the same time, you can allow for variation across cities in the proportion who respond positively. In addition to allowing for the person-specific IV to relate directly to the person-specific response, this analysis preserves information about differences in number of subjects in the different cities. A city with only 10 respondents will have a city random effect estimate which has a much larger standard error than a city with 1000 respondents.

If I am correct that there are two check boxes and hence two binary responses, then an appropriate model for check box 1 would be something like:

proc glimmix data=muydata; model box1 = x / s dist=binary; random intercept / subject=city type=sp(pow)(lat long) group=region; run;

A similar model could be fit for check box 2 as a response. One could model check box 1 and check box 2 responses together as correlated within individuals. There may be quite a few ways that such an analysis could be constructed. It is not clear given the spatial covariance structure assumed for the city random effects along with correlated responses within individuals just what the appropriate code would be for such a model.

Statisticians have the habit of adding confusion to seemingly simple problems, don't we? Are you more or less confused than at the start of this dialogue?

Dale

--------------------------------------- Dale McLerran Fred Hutchinson Cancer Research Center mailto: dmclerra@NO_SPAMfhcrc.org Ph: (206) 667-2926 Fax: (206) 667-5977 ---------------------------------------