Specification curve analysis

# Grilling the data: Application of specification curve analysis to red meat and all-cause mortality 24: r

# specr::setup

install.packages("specr")

install.packages("tidyverse")

library(tidyverse)

library(specr)

# Function to create logistic regression models

log_glm <- function(formula, data) {

  glm(formula, data = data, family = binomial())

}

# Simulate example data

set.seed(123)  # For reproducibility

n <- 1000  # Number of observations

example_data <- tibble(

  x1 = rnorm(n, mean = 50, sd = 10),  # Simulate red meat consumption

  c1 = sample(c("Male", "Female"), n, replace = TRUE),  # Sex

  c2 = rnorm(n, mean = 50, sd = 10),  # Age

  c3 = rnorm(n, mean = 25, sd = 5),   # BMI

  c4 = rbinom(n, 1, 0.2),             # Diabetes status (0 = no, 1 = yes)

  y1 = as.factor(rbinom(n, 1, plogis(-3 + 0.02 * x1 + 0.1 * c4)))  # Mortality (logistic model)

)

# Setup the specifications for the logistic regression model

specs <- setup(data = example_data,

               x = "x1",

               y = "y1",

               model = "log_glm",

               controls = c("c1", "c2", "c3", "c4"))

# Print the summary of the setup

summary(specs)

# Run the model using specr

results <- specr(specs)

# Show the first few rows of the results

results$data

plot(results)