longitudinal_example: Example Longitudinal Data for Clinical Research

Description

A simulated longitudinal study dataset with 50 subjects measured at 10 timepoints each, with 20 correlated predictors and nested random effects (subject and site).

Usage

longitudinal_example

Arguments

Format

A data frame with 500 rows and 25 variables:

obs_id: Integer. Observation identifier (1-500)
subject: Factor. Subject identifier (1-50)
site: Factor. Study site identifier (1-5)
time: Integer. Measurement timepoint (1-10)
outcome: Numeric. Continuous outcome variable
x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11, x12, x13, x14, x15, x16, x17, x18, x19, x20: Numeric. Correlated predictor variables

Details

This dataset represents a typical longitudinal study with repeated measures. Predictors are correlated both within and between subjects:

Predictors x1-x10: Highly correlated (r ~= 0.75)
Predictors x11-x20: Moderately correlated (r ~= 0.50)

The outcome depends on time (linear trend), random effects (subject and site), and a subset of fixed-effect predictors (x1, x5, x15).

Use case: Demonstrating modelPrune() with mixed models (lme4 engine) to prune fixed effects while preserving random effects structure.

Examples

Run this code

data(longitudinal_example)

if (FALSE) {
# Prune fixed effects in mixed model (requires lme4)
if (requireNamespace("lme4", quietly = TRUE)) {
  pruned <- modelPrune(
    outcome ~ x1 + x2 + x3 + x4 + x5 + (1|subject) + (1|site),
    data = longitudinal_example,
    engine = "lme4",
    limit = 5
  )

  # Random effects preserved, only fixed effects pruned
  attr(pruned, "selected_vars")
}
}