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- Modeling Longitidunal Proportional Data

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08-16-2008 08:38 AM

I am dealing with a problem to model longitudinal proportional data using both GEE (M1) and GLMM (M2). I am not sure if my syntax is correct especially for GEE since most SAS books only cover longitudinal "binary" data. I am even not sure if PROC GENMOD can handle GEE modeling for "repeated proportions". Both proportional and binary data belong to binomial distribution but the former one is in group level while the latter one individual level. Hope any one can share experience or opinion with me.

Thanks

Jacob

===================================

M1: GEE

proc genmod data=dsn;

class ID group;

model num/den = time group / link=logit dist=binomial type3;

repeated subject=ID/type=ar(1);

run;

M2: GLMM

proc glimmix data=dsn;

class ID group;

model num/den = time group / link=logit dist=binomial solution;

random int time/subject=ID type=un;

run;

ID time num den rate group

1 1 4 5 0.8 A

1 2 3 4 0.75 A

1 3 3 3 1 A

2 1 6 10 0.6 B

2 2 5 5 1 B

2 3 8 10 0.8 B

3 1 1 2 0.5 A

3 2 2 5 0.4 A

3 3 3 5 0.6 A

Note that rate = num / den

Thanks

Jacob

===================================

M1: GEE

proc genmod data=dsn;

class ID group;

model num/den = time group / link=logit dist=binomial type3;

repeated subject=ID/type=ar(1);

run;

M2: GLMM

proc glimmix data=dsn;

class ID group;

model num/den = time group / link=logit dist=binomial solution;

random int time/subject=ID type=un;

run;

ID time num den rate group

1 1 4 5 0.8 A

1 2 3 4 0.75 A

1 3 3 3 1 A

2 1 6 10 0.6 B

2 2 5 5 1 B

2 3 8 10 0.8 B

3 1 1 2 0.5 A

3 2 2 5 0.4 A

3 3 3 5 0.6 A

Note that rate = num / den

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08-16-2008 10:47 PM

I do not know where is the binary data, it is not 0/1 coded, it multinormal data I think

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08-20-2008 10:56 AM

I don't think this is multinomial (you wrote multinormal - I don't know what that is), but he has clearly specified the model correctly. For each class he has the number of trials and the number of observed events, and he is assuming that the number of events is a binomial distribution with probability p_{class}.