# Interactions between categorical and continuous independent variables

Im going to investigate if a disease have a negative impact on the development of children. The disease is the independent variable with additionally 10 confounders. Do I have to check for interactions between the confounders? And interactions between the disease and the confounders? I have both categorical and continuous independent variables, how do I check them in that case before the logistic regression?

• May I ask why you have to do a logistic regression? It'd be interesting to assess the joint probability distribution of disease, development, and the ten confounders. From it you could investigate how the confounders affect the probability, and also have the most complete answer about the impact of the disease on development. Aug 24 '19 at 22:01

Finally, you should probably use a state-of-the-art method that flexibly models the relationships among the disease, the confounders, and development. These methods do the work for you of figuring out whether you need to account for interactions or not. They don't tell you the answer to that question, but they will give you a (likely) valid estimate of the treatment effect without relying on you to specify the correct model. I outline a few such methods here with references in the linked post. I tend to recommend Bayesian Additive Regression Trees (BART) because the bartCause R package makes it so easy to use them, and they tend to have excellent performance in many arbitrary simulations (including in the presence of interactions).
Whatever you do, do not interpret the coefficient on the disease variable in a logistic regression as a treatment effect! Not only are odds ratios essentially uninterpretable scientifically, but effect estimates from logistic regression models also have a variety of interpretational difficulties statistically. If you're using R or Stata, use the marginal effects procedure to estimate a risk difference (using the margins function in either software), and if you're using SAS, use PROC CAUSALTRT. Seek the help of a biostatistician or epidemiologist trained in causal inference if these concepts are unfamiliar to you.