1: 10 rule in logistic regression - EPV
1) Whether is there any other consideration for determining the number of predictors in logistic regression model?


The right number of predictors depends on your data and on your theories about data, and on that only. All these rules of thumb seem completely arbitrary to me, and they lack any scientific ground.




  2) How do people calculate min sample size required based on this?


What do you mean with "min sample size"?

EDIT: Of course, the number of explanatory variables cannot be larger that the number of observations. Based on this answer, a rulo of thumb is to use at least 10-20 observations for each variable. I wish also to stress one point: they must be useful variables, i.e. variables with actual explanatory power. If a variable is a linear combination of others, it won't improve the model by any means, and statistical softwares such as R would delete one automatically.

However, my suggestion is to try to always employ all the data available, don't just stick to a minimum threshold.