**Interaction
test**

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This module tests for the difference of risk
factor effect with or without the presence of an effect-

The risk factor could be continuous
variables or categorical. The effect modifier is limited to categorical
variables.

·
__If the risk
factor is a categorical variable__, Empower(S)
will first list Mean+SD (if outcome is a continuous variable) or N (%) (if
outcome is a binary variable) of the outcome among each subgroup of joint
distributions of risk factor and effect-modifier. It will run two models:
A and B. Model A combines risk factor and effect-modifier and has
parameters for each subgroup of the joint distributions of risk factor and
effect-modifier; Model B does not join risk factor and effect-modifier, has
separate parameters for each subgroup of risk factor and each subgroup of
effect-modifier. Then the module will apply log likelihood ratio test to
compare model A and B. If the p-value from log likelihood ratio test is
significant, it suggests that there is interaction between the risk factor and
effect modifier. Empower(S) reports the output from model A and the
p-value from log likelihood ratio test (p-interaction)

·
__If the risk
factor is a continuous variable__, Empower(S)
will first run two models A and B. Model A has separate risk factor
parameters for each subgroup of effect modifier. Model B just has one risk
factor parameter for all groups. The module will then apply log
likelihood ratio test to compare model A and B. If the p-value from log
likelihood ratio test is significant, it suggests that there is interaction
between risk factor and effect-modifier. Empower(S) will report the
output from model A and the p-value from log likelihood ratio test
(p-interaction).

The module automatically detects whether the outcome variable is binary or continuous and selects logistic or linear regression as appropriate. You also can specify the distribution and link function for each outcome manually, right click the variable and then select “Change functions”. In the popup window, select the distribution and link function.

**What
it’s used for**

This module could be used to identify effect modifier, and thus helps us to understand the underline potential cause-effect pathway.

**Screen
shot of sample designs:**

**Sample
output tables:**