Hi,
I would like to study association between few outcomes (binary) and types of feed- bolus versus continuous in a group of patients.
I used multivariate logistic regression to do the analysis and we found significant results at 0.05 level.
However, some are making comments that our continuous fed group is sicker and very different from bolus group.
However , i have all the confounders adjusted for in my logistic regression.
I am wondering how to addresss that concern.
Thanks
Typically in a study of this type, you randomly assign patients to a treatment (either bolus or continuous). This reduces (but does not eliminate) the possible differences between the groups affecting the result.
But if you have covariates (I think this is what you mean by a confounders) and you include them in the analysis, the analysis should (if you do it properly) come to conclusions about the treatment after adjusting for the covariates.
So its not clear, on a statistical basis, why there is a concern over one group being sicker than the other group, if the health/sickness of the group is a covariate. You didn't specifically say what the covariates are, and if they measure the sickness of a group.
@Kyra wrote:
Birth weight was significantly different between the bolus and continuous groups. I added that as a covariate in my multivariate model.
Does birthweight measure sickness? Why do the commenters bring up sickness anyway? Do you have a variable that measures sickness directly?
Commenters are saying that even variable is not significantly different , it makes the two groups different. i had decided not to add those extra variables to multivariable analysis since our sample size is very small.
Yes, of course, the two groups are different, they will never be EXACTLY the same, that's impossible. But that's why we do statistics and things like randomization and include the effect of covariates. But with a small sample size, you are limited in what you can do. There's only so much you can learn from the data and you can't after the fact make the study perfect and you can't account for ALL possible sources of difference.
So there's really no definitive answer here, if the opinion of some people are that this is a meaningful difference even if the statistics don't show it, well that's just the way these things go.
This sounds like a problem that can be addressed by causal analysis. See the Overview section in the documentation of PROC CAUSALTRT.
Are you ready for the spotlight? We're accepting content ideas for SAS Innovate 2025 to be held May 6-9 in Orlando, FL. The call is open until September 25. Read more here about why you should contribute and what is in it for you!
ANOVA, or Analysis Of Variance, is used to compare the averages or means of two or more populations to better understand how they differ. Watch this tutorial for more.
Find more tutorials on the SAS Users YouTube channel.