# Binary logistic regression model shows unrealistic OR and 95% CI

I've just done a multivariate regression analysis, using a p-value from bivariate regression analyses of <0.20 as a cut-off to determine which variables will be included in the multivariate model.

I have a total sample of 628 patients, and the crosstabs shows just right. The bivariate regression analyses also seems normal. However, when I try to converge all the eligible variables, the output seems to unrealistic

Here are some additional information on the model:

While it seems that most of the cases were excluded in the analysis, I think this is not the case... The other analyses I performed also showed that a considerable number of observations were observed, but the models still converged well and aligns with the cross-tabulations.

^These are the summary of the model.

I also tried changing the method from ENTER to FORWARD: LR, but the model failed to converge.

Does anyone know what problems may cause the above phenomenon? I have previously tried performing multivariate regression analyses with less sample sizes and more variables, but they all went well. This is the first time I've encountered such a problem.

Thank you very much in advance.

• What is your sample size? You should not use stepwise methods ... see Algorithms for automatic model selection. You probably have separation in the data see stats.stackexchange.com/questions/11109/… and search this site Sep 15 at 2:37
• Selecting variables on the basis of p-values is invalid, and especially ruins confidence intervals of remaining variables. Sep 15 at 2:37
• @kjetilbhalvorsen The total sample size is 628.. However, I presume that if case-wise (deletes a case if at least one variable is missing) deletion instead of list-wise deletion (omits the missing variable instead of omitting the case), only 104 observations were left for analysis Sep 15 at 13:45