Dear Experts,
I have trouble in finding the confidence interval value for the attached (CSV) data. Please have a look at the attachment.
The data contains 2 variables Grade (1 / 0) - dependent variable, WeightHeightRatio value(Independent variable).
While evaluating an odd-ratio for the data I get a result as kind of infinity. ---->999.999 ????????
Effect | Unit | Estimate | 95% Confidence Limits | |
WHtR | 1 | 440.666 | 72.929 | >999.999 |
proc logistic data = reg2 plots=roc;
model Grade1(event='1') = WHtR;
roc 'WHR' WHtR;
run;
/*I tried a alternate way to ommit the infinity value*/
proc logistic data = reg2 plots=roc;
model Grade1(event='1') = WHtR / FIRTH clodds = pl;
roc 'WHR' WHtR;
run;
/*But get the same result*/
I attach my dataset as well here. Please let me know where I went wrong.
Thanks in advance!
You have not necessarily done anything wrong. The estiamte for odss ration is EXP(b1), where b1=6.1056 is the estimate for the WHtR variable. The lower/upper limits are
exp(b1 +/- 1.96*StdErr), which is
72.784571 |
2761.9604 |
For a discussion and example, see the end of the article "Formats for p-values and odds ratios in SAS."
Dear @Rick_SAS
Thank you so much for your suggestion.
But it looks like weird to get a very big odds ratio in this logistic regression.
Do you think, Is it fine to considerable to present in the report?
Even though I followed a few procedures to get exact results.
448.348 | 72.779 | >999.999 |
1 | -1.5159 | 0.5301 | 8.1777 | 0.0042 |
1 | 6.1056 | 0.9276 | 43.3206 | <.0001 |
As per your statement :
The estimate for odds ratio is EXP(b1), where b1=6.1056 is the estimate for the WHtR variable. The lower/upper limits are
exp(b1 +/- 1.96*StdErr), which is
72.784571 |
2761.9604 |
> Do you think, Is it fine to considerable to present in the report?
You should discuss this issue with your colleagues or supervisor.
.
> Can I present an Odds Ratio as 448.348 (95%CI 72.78 - 2761.96)
Yes, you could do that. If you use four significant digits, it would be (72.78, 2762).
SAS Innovate 2025 is scheduled for May 6-9 in Orlando, FL. Sign up to be first to learn about the agenda and registration!
Learn the difference between classical and Bayesian statistical approaches and see a few PROC examples to perform Bayesian analysis in this video.
Find more tutorials on the SAS Users YouTube channel.
Ready to level-up your skills? Choose your own adventure.