**Covariates
check**

This module checks each covariate to determine if it should be included or excluded in model as an adjusting variable. This module is used to identify important covariates that should be adjusted for in multivariate regression models.

Determining which covariates should
be put into a model for adjusting is a common question. Different criteria have
been used to include or exclude covariates in a model. The Empower(S)
“Covariates check" module provides a function of diagnosing the effect of
including or excluding a covariate in a model.

For a covariate (C), the module will
first model the outcome with the covariate (C) called univariate
model, the p-value for the covariate (C) will be taken into account; Then, the
module will compare the model that has risk factor(X) and the covariate(C)
versus the model that has risk factor(X) only, the change in the regression
coefficient for the risk factor(X) will be taken into account; lastly the
module will compare the model that has risk factor(X) and all covariates versus
the model that has risk factor(X) and all other covariates except the
covariate(C), the change of regression coefficient for risk factor(X) will be
taken into account. Finally, the module picks up the covariates that meet your
specified criteria.

For example, we want to run
regression for SBP with BMI, we will adjust for SEX
and AGE. We want to determine if we should adjust for SMK2, ALH2, EDU2
and OCCU2. Use SMK2 as an example, we will run the following models:

(1) SBP = SMK2+ SEX
+ AGE

(2) SBP = BMI + SEX
+ AGE

(3) SBP = BMI + SEX
+ AGE + SMK2

(4) SBP = BMI + SEX
+ AGE + SMK2 + ALH2 + EDU2 + OCCU2

(5) SBP = BMI + SEX
+ AGE + ALH2 + EDU2 + OCCU2

Model (1) will give us the p value
for SMK2 show the significance of the association between SMK2 with SBP.
Model 2 is basic model. Model 3 adds SMK2 to basic model. By comparing
the regression coefficient of BMI from model 2 to model 3, we calculate the
change of the BMI’s effect by adding SMK2 to the basic model. Model 4 is
called the full model. Model 5 removes the SMK2 from full model. By
comparing the regression coefficient of BMI from model 4 to model 5 we
calculate the change of BMI’s effect by removing the SMK2 from the full model.

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.

**A
screen shot of sample design:**

**A
sample output table:**