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Apologies as this has been posted before in similar topics but still I am trying to stretch my brain to understand it.

I have panel data, I am looking at both binary and count variables as outcomes and I am using random effect logistic and negative binomial regressions.

My two predictors are both discrete, one is smoking status (3 categories smoker non smoker and ex smoker) and the other is gender (binary) and I want to look at whether smoking cessation has an impact on reducing the effect of gender on selected outcomes.

First case: I have the two main effects and the interaction that are significant. Do have I to present in the table main effects and interaction or directly the RERI (in case of negative binomial regression)?

Second case: The two main effects are significant but not the interaction. Can I still say that the main effect of one is ..., the main effect of the other is... but smoking doesn't modify the effect of gender? Or I am not allowed to infer this in the specific case?

Third case: how to handle when the main effect (i.e. gender) and the interaction is significant but not the main effect of the other predictor (i.e. smoking)?

Apologies as this has been posted before in similar topics but still I am trying to stretch my brain to understand it.

I have panel data, I am looking at both binary and count variables as outcomes and I am using random effect logistic and negative binomial regressions.

My two predictors are both discrete, one is smoking status (3 categories smoker non smoker and ex smoker) and the other is gender (binary) and I want to look at whether smoking cessation has an impact on reducing the effect of gender on selected outcomes.

First case: I have the two main effects and the interaction that are significant. Do have I to present in the table main effects and interaction or directly the RERI (in case of negative binomial regression)?

Second case: The two main effects are significant but not the interaction. Can I still say that the main effect of one is ..., the main effect of the other is... but smoking doesn't modify the effect of gender? Or I am not allowed to infer this in the specific case?

Third case: how to handle when the main effect (i.e. gender) and the interaction is significant but not the main effect of the other predictor (i.e. smoking)?

Apologies as this has been posted before in similar topics but still I am trying to stretch my brain to understand it.

I have panel data, I am looking at both binary and count variables as outcomes and I am using random effect logistic and negative binomial regressions.

My two predictors are both discrete, one is smoking status (3 categories smoker non smoker and ex smoker) and the other is gender (binary) and I want to look at whether smoking cessation has an impact on reducing the effect of gender on selected outcomes.

First case: I have the two main effects and the interaction that are significant. Do have I to present in the table main effects and interaction or directly the RERI (in case of negative binomial regression)?

Second case: The two main effects are significant but not the interaction. Can I still say that the main effect of one is ..., the main effect of the other is... but smoking doesn't modify the effect of gender? Or I am not allowed to infer this in the specific case?

Third case: how to handle when the main effect (i.e. gender) and the interaction is significant but not the main effect of the other predictor (i.e. smoking)?

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# How to interpret my coeff/ORs when the main effect of my two predictors is significant but not the interaction between the two?

Apologies as this has been posted before in similar topics but still I am trying to stretch my brain to understand it.

I have panel data, I am looking at both binary and count variables as outcomes and I am using random effect logistic and negative binomial regressions.

My two predictors are both discrete, one is smoking status (3 categories smoker non smoker and ex smoker) and the other is gender (binary) and I want to look at whether smoking cessation has an impact on reducing the effect of gender on selected outcomes.

First case: I have the two main effects and the interaction that are significant. Do have I to present in the table main effects and interaction or directly the RERI (in case of negative binomial regression)?

Second case: The two main effects are significant but not the interaction. Can I still say that the main effect of one is ..., the main effect of the other is... but smoking doesn't modify the effect of gender? Or I am not allowed to infer this in the specific case?

Third case: how to handle when the main effect (i.e. gender) and the interaction is significant but not the main effect of the other predictor (i.e. smoking)?