Univariate or multivariate regression for this project? I'm looking at the risk of seizure in patients with metastasizing brain cancer and so far have several variables that I want to check to my dependent variable of seizure yes/no.
These are variables such as age, sex, tumor size and much more.
Now if I understand it correctly, first I run a univariate regression and the result will tell me whether there is a statistical significant relationship between one variable and my dependent variable.
However, I see in similar studies that they also do multivariate regression, but do not specify how, exactly.
Can someone help me understand why I would do a multivariate regression in such a project?
Thank you.
 A: For the sake of simplicity, let's say your independent variables consist of age, sex and tumour size only.
When you fit a univariate binary logistic regression model relating your dependent variable (seizure, yes or no) to the independent variable age, the model is ultimately enabling you to answer this question: 
How does age affect the probability of seizure in the target patient population (i.e., for all patients in the population, regardless of their sex and tumour size)?
When you fit a univariate binary logistic regression model relating your dependent variable (seizure, yes or no) to the independent variable sex, the model is ultimately enabling you to answer this question: 
How does sex affect the probability of seizure in the target patient population (i.e., for all patients in the population, regardless of their age and tumour size)?
When you fit a univariate binary logistic regression model relating your dependent variable (seizure, yes or no) to the independent variable tumour size, the model is ultimately enabling you to answer this question: 
How does tumour size affect the probability of seizure in the target patient population (i.e., for all patients in the population, regardless of their age and sex)?
When you fit a multiple binary logistic regression model relating your dependent variable (seizure, yes or no) to the independent variables age, sex and tumour size, the model is ultimately enabling you to answer more pointed questions (assuming you only include main effects for these independent variables in your model):


*

*How does age affect the probability of seizure for patients in the target patient population having the same sex and the same tumour size?

*How does sex affect the probability of seizure for patients in the target population having the same age and the same tumour size?

*How does tumour size affect the probability of seizure for patients in the target population having the same age and the same sex?
Of course, if you include interactions between any of the independent variables in your multiple binary logistic regression model, that expands the list of questions you can ask. 
