I'm Rafael Nepomuceno, I'm PhD student in Brazil and I'm writing a paper about polymorphism and periodontal disease.
I was trying to do a more-than-multiplicative gene-gene or gene-environment interaction analysis using logistic regression analysis (i.e. (Case/Control * Covariates + Smoking + SNP1 + Smoking×SNP2).
I was trying to do a interaction analyzes separating according to the subgroups (i.e. genotype 1.1. non smoking, genotype 1.2 or 2.2 non smoking, genotype 1.1 smoking...) showing the OR and p-value for each one of the 4 possibilities (2 genotypes * 2 smoking status).
I am using STATA to do that, and I used this code:
logistic group SNP1 smoking ib(0).SNP1#ib(0).smoking age sex
However for all my analysis I saw that:
note: 1.snp11#0.smoking_m omitted because of collinearity
note: 1.snp11#1.smoking_m omitted because of collinearity
Logistic regression Number of obs = 682
LR chi2(7) = 65.23
Prob > chi2 = 0.0000
Log likelihood = -440.10673 Pseudo R2 = 0.0690
---------------------------------------------------------------------------------
group | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]
----------------+----------------------------------------------------------------
snp11 | 1.434213 .26643 1.94 0.052 .9965244 2.06414
smoking_m | 1.542746 1.07332 0.62 0.533 .3945443 6.032439
|
snp11#smoking_m |
0 1 | 1.204203 .882984 0.25 0.800 .2861243 5.068095
1 0 | 1 (omitted)
1 1 | 1 (omitted)
|
age | 1.049977 .0085603 5.98 0.000 1.033332 1.06689
sex | .682767 .1162793 -2.24 0.025 .4889989 .9533167
_cons | .0960629 .0377742 -5.96 0.000 .0444471 .2076197
---------------------------------------------------------------------------------
Do you know why I can not get all the OR and p-value for all 4 possibilites?
When I tried to do the same analysis with just the interaction term (SNP # smoking) without each variable separately (i.e. ogistic group ib(0).SNP1#ib(0).smoking age sex), I could get the OR and p-value for each of the 4 subgroups (ie genotype 1.1 non smoking, genotype 1.2 or 2.2 non smoking, genotype 1.1 smoking ...), but i think that it is not correct acording to interaction analysis.
I was trying to do a more-than-multiplicative gene-gene or gene-environment interaction analysis using logistic regression analysis (i.e. (Case/Control * Covariates + Smoking + SNP1 + Smoking×SNP2).
I was trying to do a interaction analyzes separating according to the subgroups (i.e. genotype 1.1. non smoking, genotype 1.2 or 2.2 non smoking, genotype 1.1 smoking...) showing the OR and p-value for each one of the 4 possibilities (2 genotypes * 2 smoking status).
I am using STATA to do that, and I used this code:
logistic group SNP1 smoking ib(0).SNP1#ib(0).smoking age sex
However for all my analysis I saw that:
note: 1.snp11#0.smoking_m omitted because of collinearity
note: 1.snp11#1.smoking_m omitted because of collinearity
Logistic regression Number of obs = 682
LR chi2(7) = 65.23
Prob > chi2 = 0.0000
Log likelihood = -440.10673 Pseudo R2 = 0.0690
---------------------------------------------------------------------------------
group | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]
----------------+----------------------------------------------------------------
snp11 | 1.434213 .26643 1.94 0.052 .9965244 2.06414
smoking_m | 1.542746 1.07332 0.62 0.533 .3945443 6.032439
|
snp11#smoking_m |
0 1 | 1.204203 .882984 0.25 0.800 .2861243 5.068095
1 0 | 1 (omitted)
1 1 | 1 (omitted)
|
age | 1.049977 .0085603 5.98 0.000 1.033332 1.06689
sex | .682767 .1162793 -2.24 0.025 .4889989 .9533167
_cons | .0960629 .0377742 -5.96 0.000 .0444471 .2076197
---------------------------------------------------------------------------------
Do you know why I can not get all the OR and p-value for all 4 possibilites?
When I tried to do the same analysis with just the interaction term (SNP # smoking) without each variable separately (i.e. ogistic group ib(0).SNP1#ib(0).smoking age sex), I could get the OR and p-value for each of the 4 subgroups (ie genotype 1.1 non smoking, genotype 1.2 or 2.2 non smoking, genotype 1.1 smoking ...), but i think that it is not correct acording to interaction analysis.
Comment