Description Usage Format References Examples

A simulated dataset with time-varying longitudinal outcome, time-to-event, and time-varying covariates. The dataset is already converted into left-truncated right-censored (LTRC) format, so that the Cox model with time-varying longitudinal outcome as a covariate can be fit. See, for example, Fu and Simonoff (2017).

1 |

A data frame with 866 rows and 11 variables. The variables are as follows:

- ID
subject identifier (1 - 500)

- X1
continuous covariate between 0 and 1; time-varying

- X2
continuous covariate between 0 and 1; time-varying

- X3
binary covariate; time-varying

- X4
continuous covariate between 0 and 1; time-varying

- X5
categorical covariate taking values from 1, 2, 3, 4, 5; time-varying

- time_L
left-truncated time

- time_Y
right-censored time

- delta
censoring indicator, 1 if censored and 0 otherwise

- y
longitudinal outcome; time-varying

- 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

Fu, W. and Simonoff, J. S. (2017). Survival trees for left-truncated and right-censored data, with application to time-varying covariate data. Biostatistics, 18(2), 352-369.

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
# The data for the first five subjects (ID = 1 - 5):
#
# ID X1 X2 X3 X4 X5 time_L time_Y delta y g
# 1 0.27 0.53 0 0.0 4 0.09251632 1.536030 0 -0.2191137 1
# 1 0.49 0.71 1 0.0 5 1.53603028 4.366769 1 0.6429496 2
# 2 0.37 0.68 1 0.4 4 0.44674406 1.203560 0 0.5473454 2
# 2 0.65 0.67 0 0.2 5 1.20355968 1.330767 1 1.5515773 4
# 3 0.57 0.38 0 0.2 4 0.82944637 1.267248 0 1.1410397 3
# 3 0.79 0.19 1 0.4 4 1.26724819 5.749602 1 1.0888787 3
# 4 0.91 0.95 0 0.9 1 0.81237396 1.807741 1 2.2105303 4
# 5 0.20 0.12 1 0.3 5 0.80510669 1.029981 0 -0.1167814 1
# 5 0.02 0.31 0 0.4 5 1.02998145 6.404183 1 -0.1747389 1
``` |

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

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