Build the per-patient longitudinal design pieces for a mixed continuous/ordinal (Gaussian-copula) joint model

Copula-aware counterpart to build_conditional_design() (see R/conditionalDesign.R), for use whenever long_fit_all$biomarker_type contains at least one "ordinal" biomarker (see longitudinalSubCopula()). Continuous biomarkers' rows are handled identically to build_conditional_design(): their exact observed value is recorded. For an ordinal biomarker's rows, the exact underlying latent Gaussian score is unobserved – only the pair of cumulative-link thresholds (long_fit_all$thresholds[[m]]) bracketing the observed category is known (the classical Albert & Chib probit data-augmentation representation, same convention as impute_latent_ordinal() in R/copulaLongitudinal.R: latent score Z = eta + epsilon, epsilon ~ N(0, 1), category k iff full_alpha[k] < Z <= full_alpha[k+1] with full_alpha = c(-Inf, alpha_m, Inf), thresholds used directly with no sign flip).

The random-effects design (A_i) and Sigma_all construction is unchanged in form from build_conditional_design() – marker type only affects sigma.longitudinal (already fixed at 1 for ordinal markers by the caller, the probit identification constraint; see longitudinalSubCopula()), which flows in exactly the same way for both continuous and ordinal markers.

Usage

build_conditional_design_copula(
  rep_num_i_list,
  data_num_i_list,
  lfit,
  Sigma,
  sigma.longitudinal,
  time_variable,
  n_longitudinal,
  biomarker_type,
  long_sub_fixed,
  thresholds,
  long_sub_random
)

Value

A list with y_all_vec (stacked observed values; NA at ordinal-marker rows), alpha_lower_vec/alpha_upper_vec (stacked threshold bounds; NA at continuous-marker rows), row_marker_type (stacked per-row "continuous"/"ordinal" label), parameter_matrix (block-diagonal fixed-effect coefficients, lfit[[i]]$coefficients$fixed for continuous markers, lfit[[i]]$beta – no intercept – for ordinal markers), and Sigma_all.