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
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