Marginal distribution of T

Marginal distribution of T

Usage

marginalT(data_predict_all, long_fit_all, survival_fit_all, l_i)

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.

survival_fit_all

Results and parameters generated from the model fitting procedure, utilizing the coxph function. These outputs include the comprehensive findings and variables derived from the analysis.

l_i

A vector of time points to calculate the conditional probability.

Value

Marginal density probability of the survival variable T is represented as a probability matrix. In this matrix, the rows (l_i) are aligned with specific time points, while the columns correspond to individual patients. Each entry in the matrix denotes the marginal density probability of survival for a given patient at a particular time point.