- #1

FallenApple

- 566

- 61

so I have two models.

y=age+treatment_type . + . age*treatment_type

y=factor(age)+treatment_type . + . factor(age)*treatment_type

Basically what I got was that age is highly significant for the continuous model. For slight deviations in age, there is a huge effect on of treatment.

However, when age is factorized into groups, there is barely any interaction effect. (pvalue not significant)Why? What could be the reason for this. Is the reason why it's so significant for the continuous model is that patients simply differ from each other so much, that it seems like age has an effect, but it really doesn't.

Afterall, it has no effect when looking at groups. But somehow, within the groups, slight deviations give a large effect.