data_timeinv: A simulated dataset with time-invariant longitudinal outcome...

In jlctree: Joint Latent Class Trees for Joint Modeling of Time-to-Event and Longitudinal Data

Description

A simulated dataset with time-invariant longitudinal outcome, time-to-event, and time-invariant covariates. Since longitudinal outcome and all of the covariates are time-invariant, there is only one observation per subject. The time-to-event data is right-censored.

Usage

data(data_timeinv)

Format

A data frame with 500 rows and 10 variables.

ID

subject identifier (1 - 500)

X1

continuous covariate between 0 and 1; time-invariant

X2

continuous covariate between 0 and 1; time-invariant

X3

binary covariate; time-invariant

X4

continuous covariate between 0 and 1; time-invariant

X5

categorical covariate taking values from 1, 2, 3, 4, 5; time-invariant

time_Y

right-censored event time

delta

censoring indicator, 1 if censored and 0 otherwise

y

longitudinal outcome; time-invariant

g

true latent class identifier 1, 2, 3, 4, which is determined by the outcomes of 1\{X1 > 0.5\} and 1\{X2 > 0.5\}, with some noise

Examples

# The data for the first five subjects (ID = 1 - 5):
#
#  ID   X1   X2 X3  X4 X5    time_Y delta         y g
#   1 0.27 0.53  1 0.8  1 10.703940     0 0.8923776 2
#   2 0.37 0.68  1 0.5  3  9.153915     1 0.6871529 2
#   3 0.57 0.38  1 0.2  1  4.489658     1 0.8410745 3
#   4 0.91 0.95  0 0.4  3  1.009941     1 2.1058681 4
#   5 0.20 0.12  0 0.8  5 11.125094     0 0.1383508 1

jlctree documentation built on April 15, 2021, 5:06 p.m.