**Multiple
regressions**

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This module tests for associations
between risk factors and outcomes with different adjusting variables. It can quantify
the exposure effect with or without other covariates.

Given a set of **outcomes**, **risk factors**,
and **covariates**, this module runs
regressions for each outcome with each or all risk factors.

The risk factor and outcome could be
continuous or categorical variables. If the risk factors are categorical
variables only, you can direct Empower(S) to list Mean+SD (if outcome is
continuous) or N (%) (if outcome is binary) for the outcomes among each
subgroup of each risk factor.

Stratified variables could be added
to direct the module to do the regressions for each group as well as for the
whole study population.

Up to 3 different adjusting models,
and up to 2 levels of stratifications could be specified.

This module provides flexibility for
designing the output table format (rows and columns). You can specify the
columns by stratified subgroups, or outcomes, or risk factors or models; you
can order the rows by stratified subgroups, outcomes, risk factors or models.

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**

Up to 3 models with different set of
adjustment covariates could be specified, which helps to get more insight into
how the relationship of risk factor(s) and outcome will be affected by
different adjustments.

**Screen
shot of sample designs:**

**Sample
output tables:**