Hello,
I'm trying to predict preterm birth (yes/no) from weight gain during pregnancy, controlling for race/ethnicity, bmi at entry to prenatal care, and parity (number of previous births). I've run a logistic regression with the following model statement:
preterm=wtgaincat ethnicity bmicat parity;
wtgaincat has 4 levels (ethnicity has 3, bmicat has 4)
In my output, the p-value for the Wald Chi-Square in Type 3 Analysis of Effects table is .0292 - so less than .05, so significant.
In the Maximum Likelihood parameter estimates table, the p-value for one of the levels of wtgaincat is .0130, so significant.
The OR is for that level of wtgaincat 0.332.
BUT the CI is 0.082 -1.345 - so not significant?
Are there some situations where the p-value for the ML estimate is better, and other situations where you'd favor the 95% CI around the OR????
I'm not sure what to think or to report. Any help would be much appreciated!
Thanks,
Patricia
It would be helpful if you showed your code so we can avoid making suggestions already present in your model and the output as something else may be needed to answer the question.
What does the rest of your code look like within proc logistic or genmod?
Sorry, here is the proc, is that what you need? If more, I can paste that as well.
Thank you!
Patricia
proc logistic descending data=jain_ob7 ;
title1 'Assoc between wtgaincat and preterm';
title2 'Controlling for bmicat, ethnicity, parity';
class wtgaincat bmicat ethnicity;
model preterm=wtgaincat ethnicity bmicat parity;
format wtgaincat wtgaincatrevf.;
where bmicat>=3 and gaentry28=1;
run;
Does that give you the odds ratio and CI by default?
Yep.
Don't know if this will come through, but there is the relevant output.
"wtgaincat 0 above recommended" is the level that I'm talking about, that has conflicting p-value and OR CI.
Patricia
Type 3 Analysis of Effects
Wald
Effect DF Chi-Square Pr > ChiSq
wtgaincat 3 9.0083 0.0292
Ethnicity 2 12.8604 0.0016
bmicat 3 2.3003 0.5125
Parity 1 1.7427 0.1868
Analysis of Maximum Likelihood Estimates
Standard Wald
Parameter DF Estimate Error Chi-Square Pr > ChiSq
Intercept 1 -1.4628 0.3557 16.9141 <.0001
wtgaincat 0 above recommended 1 -0.9070 0.3652 6.1689 0.0130
wtgaincat 1 2 recommended 1 0.6497 0.3272 3.9432 0.0471
wtgaincat 2 1 below recommended 1 0.0617 0.3884 0.0253 0.8737
Ethnicity AA 1 0.9401 0.3552 7.0041 0.0081
Ethnicity H 1 -0.9584 0.2920 10.7740 0.0010
bmicat 30-35 1 -0.0366 0.3246 0.0127 0.9102
bmicat 35-40 1 -0.4800 0.3837 1.5646 0.2110
bmicat 40-45 1 0.4624 0.3772 1.5025 0.2203
Parity 1 -0.2109 0.1598 1.7427 0.1868
Odds Ratio Estimates
Point 95% Wald
Effect Estimate Confidence Limits
wtgaincat 0 above recommended vs 3 0 wt loss 0.332 0.082 1.345
wtgaincat 1 2 recommended vs 3 0 wt loss 1.575 0.417 5.942
wtgaincat 2 1 below recommended vs 3 0 wt loss 0.875 0.208 3.685
Ethnicity AA vs White 2.514 0.703 8.988
Ethnicity H vs White 0.377 0.129 1.101
bmicat 30-35 vs 45+ 0.913 0.266 3.140
bmicat 35-40 vs 45+ 0.586 0.153 2.251
bmicat 40-45 vs 45+ 1.504 0.396 5.717
Parity 0.810 0.592 1.108
patriciav wrote:
Sorry, here is the proc, is that what you need? If more, I can paste that as well.
Thank you!
Patricia
proc logistic descending data=jain_ob7 ;
title1 'Assoc between wtgaincat and preterm';
title2 'Controlling for bmicat, ethnicity, parity';
class wtgaincat bmicat ethnicity/param=ref;
model preterm=wtgaincat ethnicity bmicat parity;
format wtgaincat wtgaincatrevf.;
where bmicat>=3 and gaentry28=1;
run;
try adding the param=ref option (see above) as specified in this note:
That is perfect, thank you so much!!!!
Reeza, I can't figure out how to mark your response as correct, if this is important pls let me know and I'll do it.
Patricia
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