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Lao_feng
Obsidian | Level 7

I am doing a logistic regression analysis with random intercept in the model to account for within cluster correlation.There are 3 levels in my data:

Level 1: individual subject

Level 2: Family (some subjects from the same family), the variable is fam_num

Level 3: Village, the variable clu_num

 

The model included country, age, gender, education and marrital status.

 

My codes are:

 

proc glimmix data=work0 NOCLPRINT;
class  country (ref='Pakistan') clu_num  fam_num age4g(ref='40~49') gender edu2g mar2g ;
model comb2g(ref='0')= country age4g gender edu2g mar2g /solution Link=logit dist=binary
random int /sub=clu_num;
random int /sub=fam_num (clu_num) ;
COVTEST GLM;
run;

 

I used 'COVTEST GLM' to check if the outcome is independent or not within clusters. The results I got are as follow:

The P value is 0.4115, suggesting the clustering effects are not statistically significant. In such case, can I remove the random effect from model, and use standard logistic regression? Thanks

 

Covariance Parameter Estimates

Cov Parm

Subject

Estimate

Standard
Error

Intercept

clu_num

0.01724

0.02710

Intercept

fam_n(clu_nu)

0.01920

0.1512

 

Tests of Covariance Parameters
Based on the Residual Pseudo-Likelihood

Label

DF

-2 Res Log P-Like

ChiSq

Pr > ChiSq

Note

Independence

2

10507

0.58

0.4115

MI

2 REPLIES 2
sld
Rhodochrosite | Level 12 sld
Rhodochrosite | Level 12

In general, I believe that the statistical model should match the design. In your study, individuals are not independent: they are clustered within families which are clustered within villages. Your model does not appear to be having any trouble with estimation. So I would keep the mixed model.

 

Other people may have other opinions.

 

Lao_feng
Obsidian | Level 7

Thank you for your suggestion !

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