Hello, I ran a logistic regression model assessing the interaction between distance (a continuous variable) and SES (a categorical variable with two levels). I then wanted to calculate the predicted probabilities of the outcome for each level of the interaction. However, I understand that this cannot be done with a continuous variable in the interaction term. Thus, I used the lsmeans command to evaluate the predicted probability of each level of the interaction term at specific values of distance. My question is regarding the interpretation of the output. My interaction term was significant in my original logistic regression model such that the interaction between distance and the second level of SES were significantly associated with the outcome when the interaction between distance and the first level was the reference. However, once I calculated the predicted probabilities, these values were not significantly different from one another. Does this mean that overall, as distance changes, there is an association with the outcome among those in the second SES group, but that at these specific values of distance, there is no difference between the two groups? Thank you. proc logistic data="H:\desktop\DataAnalysis" descending; class SES (ref='1') /param=ref; model stage = SES | distance ; where sample = 1; run; proc logistic data = "H:\desktop\DataAnalysis" descending; class SES (ref='1') /param=glm; model stage = SES | distance ; lsmeans SES/ at distance=2.67 ilink or cl diff; lsmeans SES/ at distance=7.60 ilink or cl diff; lsmeans SES/ at distance=24.51 ilink or cl diff; where sample = 1; run;
... View more
