Now, I have three new markers (binary) that I'd like to study. Normally I would just do Cox regression univariately for each variable and then do multivariable Cox regression analysis and adjust the risk ratios for those factors that had significance of P<0.100 and as well as for few other factors which are usually always included, like body-mass-index and heart rate, previous myocardial infarction (even if they're not univariately predictive).

I've done this and get pretty reasonable risk ratios: e.g. univariate/multivariate: 5.8/6.8 (for some reason the multivariate RR is higher than that of the univariate).

Then I read somewhere, that I couldn't use Cox regression in nested case-control study. Instead I should do conditional logistic regression. I did this in SPSS by "tricking" the Cox regression as instructed at

https://www-304.ibm.com/support/docv...id=swg21477360

The thing is that I got risk ratios that don't seem right. Univariate/multivariate: 18.4/34.6. I've never seen risk ratios this high ever before.

So, my question is:

1) Can I use Cox regression in this nested case-control study to account for the clinical factors not used in the matching?

2) If not, is logistic regression the way to go or is there some better way to account for the group differences?

All help is welcome. Thanks,

Thomas

PS. all variables entered into the model are binary and there aren't any missing values...