longitudinal_example: Example Longitudinal Data for Clinical Research

In corrselect: Correlation-Based and Model-Based Predictor Pruning

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

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.

Source

Simulated data based on typical clinical trial designs

See Also

modelPrune()

Examples

data(longitudinal_example)


# 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")
}

corrselect documentation built on July 19, 2026, 1:06 a.m.