Fit the marginal model for a single ordinal/categorical biomarker

Fits a cumulative link mixed model (probit link) via ordinal::clmm(), used as the marginal model for a categorical biomarker in the Gaussian-copula extension of longitudinalSub() (see longitudinalSubCopula()). The probit link is used specifically (rather than the more common logit) because it integrates directly with the latent-Gaussian-score representation the copula extension relies on: the underlying continuous latent variable implied by a probit cumulative link model is, by construction, Gaussian, which is exactly the representation needed to fold a categorical biomarker into the same random-effects covariance structure as the continuous biomarkers.

This follows the classical two-stage IFM (Inference Functions for Margins; Joe 2005) strategy already used throughout BJM: fit each biomarker's marginal model separately first (here, exactly as nlme::lme() does for continuous biomarkers), then re-estimate the joint random-effects covariance across all biomarkers afterwards (see longitudinalSubVarCopula()).

Usage

fit_marginal_ordinal(fixed_formula, random_formula, data)

Value

A fitted ordinal::clmm object.