I am working with proc logistic on a model that has "level of physical activity during pandemic impacted VS level of physical activity during pandemic not impacted" as the dependant variable (hebehch_sq002) and my independent variables are a binary variable called chronic_dummy (having at least a chronic disease VS having none) and sex. The interaction term is also added (chronic_dummy*sex). The variable chronic_dummy seems to be significant in the "Type 3 analysis of effects" with a p-value of 0.015 (<0.05) but, it it isn't significant in the "Analysis of Maximum Likelihood Estimates" with a p-value of 0.0651. From my understanding when you add the interaction term in the model, the p-value associated with the chronic_dummy variable in the model is measuring the significance of the variable at the reference level of the other variable (Male), but I tried to run the model with "Female" as a reference category, and it isn't significant for this category either. The confidence interval for the odds ratio of chronic_dummy also contain 1 for both Male and Female. If it isn't significant neither for male nor female, why is it significant in the "Type 3 analysis of effects" ? Can someone explain what is causing that ? Here is my code : proc logistic data=chronic; class chronic_dummy (ref = "No chronic disease") sex(ref="Male") / param=glm; model hebehch_sq001 (event="impacted") = chronic_dummy sex chronic_dummy*sex; run; First 2 tables are the results with "Male" as a reference category, and the last 2 tables are for "Female" :
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