Split Longitudinal and Survival Data into Subject-level Samples

This function partitions a longitudinal dataset and a survival dataset at the subject level, returning two lists of sliced datasets for model fitting in parallel or series.

Usage

slicer(n_slices, id_var, data_long, data_surv, seed = 123L)

Arguments

n_slices: an integer scalar giving the number of data slices (subsamples) to create.
id_var: a character scalar with the name of the subject identifier variable in both data_long and data_surv.
data_long: a data frame containing the longitudinal measurements.
data_surv: a data frame containing the survival (time-to-event) information.
seed: an integer seed used to randomize the assignment of subject IDs to slices.

Value

A list with two components:

long: a list of length n_slices with class "sliced_data". Each element is a data frame containing the longitudinal rows for the subjects assigned to that slice.
surv: a list of length n_slices with class "sliced_data". Each element a data frame containing the survival rows for the subjects assigned to that slice.

Author

Pedro Miranda-Afonso p.mirandaafonso@erasmusmc.nl

Examples

data(pbc2, package = "JMbayes2")
data(pbc2.id, package = "JMbayes2")

pbc2_slc <- slicer(n_slices = 2, id_var = "id", data_long = pbc2, 
                   data_surv = pbc2.id, seed = 123L)
length(pbc2_slc$long) # 2
#> [1] 2
length(pbc2_slc$surv) # 2
#> [1] 2