Re: Binary logistic regression - poor models

Posted by dfva on May 02, 2016; 10:40pm
URL: http://spssx-discussion.165.s1.nabble.com/Binary-logistic-regression-poor-models-tp5732023p5732082.html

Hi Rich Ulrich!

Sorry, I couldn't reply in the last week. Thank you for your help.

I understand, that R-square is not suitable quantity measure in this complex case.

The database cover 10 years follow-up of patients. The age of patients is very variable, from 30 up to 90+. I have calculated the OR for age, and it is 1.044. The OR for gender was also calculated. But in these cases, I did not take into account other factors. We see, that age and gender are surely the most important and basic predictors.

I have calculated the covariate matrix for the comorbidities as well, but this matrix does not show very high values. The highest value is 0.344.

In the last days, I have calculated a lot of basic computations. Now I see that I should probably divide the whole problem into smaller subproblems. We selected a drug, and we have made a selection for patients who received this medication. Showing the selected drug we see a dose dependence.  Distributions of ages for the different dose ranges are the same (differ not significantly). I have calculated age groups (for every 5 years) and I made a Kruskal-Wallis test, I hope this was the right choice. It was calculated for gender as well, and it is also OK.

In this subproblem are only a few frequent drug combinations. And here we faced with what you said: "How related are these strong, a-prior factors to other predictors?"  If the most frequent drug combinations are considered (all combinations contain the selected drug), the distribution of the selected drug dose differs in the different drug combinations. The distribution of age is OK. I can calculate for each combination the probability of the heart failure, but I think I should adjust the values to the dose range. So far I got so far. I read now about the confounding variables, and about standardization in SPSS. Is this the right way? I hope it can be made in this software.

Sorry for my basic failures, I learn this discipline now (I'm working in the area IT and data mining, but I never made so deep medical analysis until now. Unfortunately, my colleagues are not familiar with this area...)
I need to think about what you said: combining more predictors into a "propensity score" and use it as a covariate... I try it to interpret...

Thank you for your help!
Agnes