Conditional distribution of Y|D,T for a mixed continuous/ordinal (Gaussian-copula) joint model, if with competing risk – biomarker-value prediction

Copula-aware counterpart to conditionalYDTBio(), used by dynamicPredictionBio() whenever long_fit_all$biomarker_type contains at least one "ordinal" biomarker and a competing-risk survival_fit_all is supplied. Both a continuous and an ordinal bio_i target are supported: for an ordinal bio_i, Y_all is expected to already be the vector of candidate category labels (the fitted factor's levels(), in threshold order) rather than a numeric grid – see compute_bio_marker_step(), which builds that vector and translates the result back into integer category codes for its caller. See conditionalYTBioCopula() for why no branching is actually needed here – build_conditional_design_copula() already looks up each row of biomarker bio_i generically via long_fit_all$biomarker_type[bio_i], whether that row is historical or the candidate row assigned below – and for why this function evaluates mvtnorm::pmvnorm() once per patient/l_i/ Y_all candidate/event-type branch, rather than one vectorized dmvnorm() call per patient/l_i as conditionalYDTBio() does.

Usage

conditionalYDTBioCopula(
  Y_all,
  time_new,
  bio_i,
  data_predict_all,
  long_fit_all,
  survival_fit_all,
  l_i,
  survival_variable,
  time_variable,
  survival_variable_all,
  survival_trans_function
)

Arguments

data_predict_all

This involves a collection of data.frame objects for dynamic prediction, each corresponding to a distinct longitudinal outcome. These data frames should contain the variables specified in long_sub_fixed and long_sub_random. Utilizing a list structure allows for the incorporation of multiple longitudinal outcomes, each potentially following different measurement protocols. In instances where all longitudinal outcomes are recorded at identical time points across patients, a singular data.frame object may be used in a list. It is presumed that each data frame is structured in a long format.

long_fit_all

Outputs from the model fitting process using the nlme package, encompassing the results and parameters obtained from the analysis.

l_i

A vector of time points to calculate the conditional probability.

survival_variable

Time-to-event outcomes variable name.

time_variable

The name of time variable in linear mixed model.

survival_variable_all

The name of the transformed time-to-event outcomes variable.

survival_trans_function

The transformation function used for time-to-event outcomes, in the order of survival_variable_all.