the overall interaction

[fengxiuming]

As the tunsmart commented [on 21 Nov 2021](https://github.com/epi zen/interactionR/issues/2#issuecomment-974696015)
"For categorical exposures, It is possible to proceed in this manner for each possible comparison of two levels of each of the two exposures. For example, if there were two categorical variables, A and B, and A had three levels (A1, A2, and A3) and B had four levels (B1, B2, B3, and B4), then one could assess additive interaction comparing A = A1 and A = A2 and B = B1 and B = B4 by ignoring the observations with A = A3 and also ignoring those with B = B2 or B = B3 and then using the code for binary exposures above.

I still have a question about the addictive interaction about the categorical variables (>2). How could I compute a P value for the overall addictive interaction? I have read some paper which show the overall addictive interaction, such as "Genetic Factors, Adherence to Healthy Lifestyle Behavior, and Risk of Invasive Breast Cancer Among Women in the UK Biobank", and "Association of Lifestyle and Genetic Risk With Incidence of Dementia".
As menthod in manuscript,
The additive interaction between the modified HLI and the PRS in association with invasive breast cancer risk was assessed by testing whether the estimated joint effect (ie, relative risk) of the two exposures was greater than the sum of the individual effect estimates for the modified HLI and the PRS.
The additive interaction was assessed as to whether the estimated joint effects of the HLS and PRS were greater than the sum of the individual effect estimates for these 2 variables.
I do not quit understand how to compute a P value for the overall addictive interaction.

Hi, you don't need to compute the p-value by hand. You can specify to output p-values for all the effect estimates by setting p.value = TRUE in the following call:
interactionR_table(obj, p.value = FALSE, file_path = NA)

[fengxiuming]

Thank you very much for your help, If the exposure was a dichotomous variable, I could set p.value = TRUE in the following call:
interactionR_table(obj, p.value = FALSE, file_path = NA). However, if I used the grade variables of classification as the exposure, the call is not used.

[fengxiuming]

And I want to know how to calculate the p.value for the overall interaction when the levels of exposure>=3

[beiwo07]

And I want to know how to calculate the p.value for the overall interaction when the levels of exposure>=3

Hi! I was in a similar situation. My stratifying factor is sex (2-level). My 3-level exposure variable is BMI (normal, overweight, and obese). I used interactionR_table to create separate tables contrast overweight vs. normal and obeser vs. normal stratifying on sex.