conditional distribution of Y|T, if no competing risk;

This function computes the conditional probability density function of longitudinal variable Y, given the survival time T without competing risk D.

Usage

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

Arguments

time_new

Prediction time add horizon

bio_i

Biomarker used to do prediction

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.

Value

The output is a list containing probability matrices of log densities (so that they neither overflow nor underflow; see predictRisk() for how they are exponentiated). In the presence of competing risks, this list includes two elements; otherwise, it contains only one element. Each element within the list is a probability matrix, with the number of rows (l_i) corresponding to specific time points and columns representing different patients. Every matrix element represents the conditional probability derived from the conditional distribution of longitudinal variable Y given the survival time T without competing risk D for a particular patient at a specific time point.