Quartile-based Logistic Regression Analysis

Analyzes continuous predictors by quartile groups, fitting:

1. Model1 (unadjusted)

2. Model2/3/4 (sequentially adding covariates) Calculates OR, 95% CI for Q2/Q3/Q4 vs Q1 and trend P-value.

Usage

quartile_logistic_analysis(
  data,
  outcomes,
  predictors,
  models_list,
  output_dir = NULL,
  save_format = c("none", "docx", "csv", "all")
)

Arguments

data

Data frame containing all variables

outcomes

Character vector of binary outcome variable names

predictors

Character vector of continuous predictor variable names

models_list

Named list of length 3 with covariates for model2, model3, model4

output_dir

Output directory (optional)

save_format

Save format: "none", "docx", "csv", "all" (default "none")

Value

List containing results data frame and optional saved file paths

Examples

if (FALSE) { # \dontrun{
models_list <- list(
  model2 = c("cyl", "gear"),
  model3 = c("carb"),
  model4 = c("hp")
)

result <- quartile_logistic_analysis(
  data = mtcars,
  outcomes = "vs",
  predictors = c("mpg", "wt"),
  models_list = models_list
)
} # }