interaction_models: Compare Models With and Without Interaction Term

In gtregression: Tools for Creating Publication-Ready Regression Tables

Compare Models With and Without Interaction Term

Description

This function fits two models—one with and one without an interaction term between an exposure and a potential effect modifier— and compares them using either a likelihood ratio test (LRT) or Wald test. It is useful for assessing whether there is statistical evidence of interaction (effect modification).

Usage

interaction_models(
  data,
  outcome,
  exposure,
  covariates = NULL,
  effect_modifier,
  approach = "logit",
  test = c("LRT", "Wald"),
  verbose = TRUE
)

Arguments

data	A data frame containing all required variables.
outcome	The name of the outcome variable
exposure	The name of the main exposure variable.
covariates	character vector of additional covariates to adjust for
effect_modifier	The name of the variable to test for interaction
approach	The regression modeling approach to use. One of: "logit", "log-binomial", "poisson", "robpoisson", "negbin", or "linear".
test	Type of statistical test for model comparison. Either: "LRT" (likelihood ratio test, default) or "Wald".
verbose	Logical; if TRUE, prints a basic interpretation of whether interaction is likely present (default = FALSE).

Value

A list with the following elements:

• model_no_interaction: The model without the interaction term.

• model_with_interaction: The model with the interaction term.

• p_value: The p-value for interaction (based on selected test).

• interpretation: A brief text interpretation if verbose = TRUE.

Examples

data <- data_PimaIndiansDiabetes

gtregression documentation built on Aug. 18, 2025, 5:23 p.m.