I am conducting cox proportional hazard regression modelling. Two predictors in the model, for example, the age and height, have significant interaction effects on the death outcome when both using as continuous variables (age * height). But when I categorized age into <30, 30-50, >50 groups, and added the interaction terms as age_group * height into the model, the interaction effect was not significant any more. I don't know how to interpret this result.
It is already hard to get sufficient statistical power for an interaction effect. By categorizing a continuous variable you are loosing statistical power, so it is not that surprising that interaction effects for the categorized continuous variable is no longer significant.