Calculate All Additive Interaction Measures from an Interaction Model — addintlist • svyTable1

Automatically identifies interaction terms in a fitted model involving two categorical factors (e.g., `~ A * B`) and calculates measures of additive interaction (RERI, AP, S) for each combination of non-reference levels. This function requires the `addint()` function to be available.

Usage

addintlist(
  model,
  factor1_name,
  factor2_name,
  measures = "all",
  conf.level = 0.95,
  digits = 3
)

Arguments

model: A fitted model object (e.g., `svycoxph`, `svyglm`) of type `"interaction"` (containing a `factor1 * factor2` term).
factor1_name: Character string: The name of the first factor variable in the interaction (e.g., `"Race1"`).
factor2_name: Character string: The name of the second factor variable (e.g., `"ObeseStatus"`).
measures: A character vector specifying which measures to calculate via `addint()`. Options: `"RERI"`, `"AP"`, `"S"`, or `"all"`. Default is `"all"`.
conf.level: Confidence level for the interval (default 0.95).
digits: Integer. Number of decimal places for rounding estimates and CIs in the final table. Default is 3. (Set to NULL for no rounding).

Value

A `tibble` (data frame) summarizing the additive interaction results. Columns include:

`Factor1`: Name of the first factor.
`Level1`: Non-reference level of the first factor for the comparison.
`Factor2`: Name of the second factor.
`Level2`: Non-reference level of the second factor for the comparison.
`Measure`: The additive interaction measure calculated ("RERI", "AP", or "S").
`Estimate`: Point estimate of the measure.
`SE`: Standard Error (Note: For S, this is SE of log(S)).
`CI_low`: Lower confidence interval bound.
`CI_upp`: Upper confidence interval bound.

Returns an empty tibble if errors occur.

Details

This function serves as a wrapper around `addint()`. It determines the levels of the two specified factors from the model object, constructs the expected coefficient names for main effects and interaction terms (checking both `A:B` and `B:A` formats), and then calls `addint()` with `type = "interaction"` for each valid combination found in the model. The results are compiled into a single summary table.

See also

Examples

# \donttest{
# --- Load required libraries for the example ---
# Ensure the 'addint' function is defined or loaded from the package
# source("R/addint.R") # If running interactively before package build

if (requireNamespace("survey", quietly = TRUE) &&
    requireNamespace("NHANES", quietly = TRUE) &&
    requireNamespace("dplyr", quietly = TRUE) &&
    requireNamespace("tidyr", quietly = TRUE) &&
    requireNamespace("msm", quietly = TRUE)) {

  library(survey)
  library(NHANES)
  library(dplyr)
  library(tidyr)
  library(msm)

  # --- 1. Data Preparation (NHANES Example) ---
  data(NHANESraw)

  vars_needed <- c("Age", "Race1", "BPSysAve", "BMI", "ObeseStatus", "Hypertension_130",
                   "SDMVPSU", "SDMVSTRA", "WTMEC2YR")

  nhanes_adults_processed <- NHANESraw %>%
    filter(Age >= 20) %>%
    mutate(
      ObeseStatus = factor(ifelse(BMI >= 30, "Obese", "Not Obese"),
                           levels = c("Not Obese", "Obese")),
      Hypertension_130 = factor(ifelse(BPSysAve >= 130, "Yes", "No"),
                                levels = c("No", "Yes")),
      Race1 = relevel(as.factor(Race1), ref = "White")
    ) %>%
    select(all_of(vars_needed)) %>%
    drop_na()

  adult_design_binary <- svydesign(
    id = ~SDMVPSU, strata = ~SDMVSTRA, weights = ~WTMEC2YR,
    nest = TRUE, data = nhanes_adults_processed
  )

  # --- 2. Fit Interaction Term Model ---
  interaction_model_logit <- svyglm(
    Hypertension_130 ~ Race1 * ObeseStatus + Age,
    design = adult_design_binary, family = quasibinomial()
  )

  # --- 3. Calculate all additive interactions ---
  all_interactions_table <- addintlist(
    model = interaction_model_logit,
    factor1_name = "Race1",
    factor2_name = "ObeseStatus",
    measures = "all"
  )

  # Print the results table
  print(all_interactions_table, n=50)

} else {
  print("Required packages (survey, NHANES, dplyr, tidyr, msm) not found.")
}
#> Calculating for: Race1_Black_vs_ObeseStatus_Obese
#> Calculating for: Race1_Hispanic_vs_ObeseStatus_Obese
#> Calculating for: Race1_Mexican_vs_ObeseStatus_Obese
#> Calculating for: Race1_Other_vs_ObeseStatus_Obese
#> # A tibble: 12 × 9
#>    Factor1 Level1   Factor2     Level2 Measure Estimate    SE CI_low CI_upp
#>    <chr>   <chr>    <chr>       <chr>  <chr>      <dbl> <dbl>  <dbl>  <dbl>
#>  1 Race1   Black    ObeseStatus Obese  RERI       0.025 0.312 -0.587  0.637
#>  2 Race1   Black    ObeseStatus Obese  AP         0.01  0.122 -0.23   0.25 
#>  3 Race1   Black    ObeseStatus Obese  S          1.02  0.205  0.68   1.52 
#>  4 Race1   Hispanic ObeseStatus Obese  RERI       0.258 0.194 -0.123  0.639
#>  5 Race1   Hispanic ObeseStatus Obese  AP         0.157 0.105 -0.048  0.362
#>  6 Race1   Hispanic ObeseStatus Obese  S          1.67  0.382  0.788  3.52 
#>  7 Race1   Mexican  ObeseStatus Obese  RERI       0.36  0.253 -0.136  0.857
#>  8 Race1   Mexican  ObeseStatus Obese  AP         0.187 0.116 -0.041  0.415
#>  9 Race1   Mexican  ObeseStatus Obese  S          1.64  0.348  0.829  3.24 
#> 10 Race1   Other    ObeseStatus Obese  RERI      -0.299 0.305 -0.896  0.299
#> 11 Race1   Other    ObeseStatus Obese  AP        -0.259 0.316 -0.878  0.36 
#> 12 Race1   Other    ObeseStatus Obese  S          0.337 1.76   0.011 10.6  
# }