Hierarchical Mixed-Effects Models

Hierarchical (multilevel) mixed-effects models for analyzing nested pathology data structures. Essential for WSI analysis with Patient > Slide > ROI > Cell hierarchies, and prevents Type I errors from ignoring clustering effects.

Usage

hierarchicalpathology(
  data,
  dependent,
  level_3,
  level_2,
  level_1,
  covariates,
  model_type = "linear",
  descriptives = TRUE,
  variance_components = TRUE,
  icc_analysis = TRUE,
  random_effects = TRUE,
  model_comparison = FALSE,
  diagnostics = TRUE,
  confidence_level = 0.95,
  optimizer = "bobyqa",
  max_iterations = 1000
)

Arguments

data

the data as a data frame

dependent

The dependent variable from data, variable must be numeric

level_3

Highest level grouping variable (e.g., Patient ID, Institution)

level_2

Intermediate level grouping variable (e.g., Slide ID, Sample ID)

level_1

Lowest level grouping variable (e.g., ROI ID, Field ID)

covariates

Optional covariates for fixed effects (age, treatment, etc.)

model_type

Type of mixed-effects model based on outcome variable

descriptives

Show descriptive statistics by hierarchical level

variance_components

Calculate and display variance components

icc_analysis

Calculate Intraclass Correlation Coefficients

random_effects

Display random effects variance and standard deviations

model_comparison

Compare nested models with likelihood ratio tests

diagnostics

Generate diagnostic plots and residual analysis

confidence_level

Confidence level for parameter estimates and intervals

optimizer

Optimization algorithm for model fitting

max_iterations

Maximum number of iterations for model convergence

Value

A results object containing:

results$instructions					Instructions for hierarchical mixed-effects modeling
results$descriptives					Summary statistics for each hierarchical level
results$modelresults					Fixed effects parameter estimates and tests
results$randomeffects					Random effects variance components
results$variancecomponents					Partition of variance across hierarchical levels
results$icc					ICC values indicating clustering effects
results$modelcomparison					Likelihood ratio tests comparing nested models
results$diagnosticplot					Residual plots and diagnostic checks
results$residualplot					Residuals plotted by hierarchical level
results$interpretation					Clinical context and interpretation for hierarchical models

Tables can be converted to data frames with asDF or as.data.frame. For example:

results$descriptives$asDF

as.data.frame(results$descriptives)

Examples

data('histopathology')

hierarchicalpathology(data = histopathology,
                     dependent = measurement,
                     level_3 = patient_id,
                     level_2 = slide_id,
                     level_1 = roi_id)