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Date:         Fri, 20 Jun 2008 11:54:28 -0700
Reply-To:     Ryan <Ryan.Andrew.Black@GMAIL.COM>
Sender:       "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From:         Ryan <Ryan.Andrew.Black@GMAIL.COM>
Subject:      Re: GLIMMIX Question - Dependent Observations
Comments: To:
Content-Type: text/plain; charset=ISO-8859-1

As usual, thank you.

On Jun 20, 1:27 pm, (Dale McLerran) wrote: > --- On Thu, 6/19/08, Ryan <Ryan.Andrew.Bl...@GMAIL.COM> wrote: > > > > > > > From: Ryan <Ryan.Andrew.Bl...@GMAIL.COM> > > Subject: Re:GLIMMIXQuestion- Dependent Observations > > To: SA...@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).

Yes! The DV is a rate of obtaining a category in the dichotomous IV (when the dichotomous IV is raised to the city level).

Concretely, Rate = # of people who contracted disease A or B / total number of people at risk of the respective disease within a city.

The dichotomous IV, which was collected at the subjects level, reflects two diseases, and people can have one or both--most only have one. I want to compare the relative risk of contracting disease A to contracting disease B (Poisson type regression). As a result, I thought of raising the dichotomous variable, disease type, to the city level, and if more people have disease A than disease B that city would be categorized as disease A--bad idea, I know.

If the dichotomous variable were in fact mutually exclusive, this analysis would be fairly straightforward (after your help with spatial analysis!) . The primary goal is to run a statistical test comparing the risk of contracting disease A to the risk of contracting disease B, after controlling for a continuous variable. The challenege is that participant A could have contracted both diseases, and when you raise it to the city level (which you have to do to obtain the rate), certainly no city has a diagnosis of only one disease.

I know I keep saying this, but just the fact that you've talked through some of this stuff with me has been invaluable.

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

I'm not sure if this would answer my question regarding relative risk of contracting one disease versus another.

> > 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.

Yes. I think this is where I need to be headed.

> > 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?

This model is particularly confusing. Although I haven't finalized the model, you have certainly moved me along tremendously!

> > Dale > > --------------------------------------- > Dale McLerran > Fred Hutchinson Cancer Research Center > mailto: > Ph: (206) 667-2926 > Fax: (206) 667-5977 > ---------------------------------------- Hide quoted text - > > - Show quoted text -

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