Description Usage Arguments Details References Examples
Provides an implementation to boost joint models for longitudinal and time-to-event outcomes.
1 2 3 4 |
y |
Longitudinal outcome including observation at or before event (last). |
Xl |
covariate matrix for longitudinal predictor |
Xls |
covariate matrix for shared predictor |
last |
vector indicating if observation refers to longitduinal (FALSE) or shared predictor (TRUE); i.e. if it is the patient's last observation before event. |
delta |
censoring indicator (person had event or not) |
id |
1xN vector of subjects |
time |
all time points (included in Xls if betatimeind != 0) |
lambda |
starting value baseline hazard |
alpha |
starting value association parameter |
mstop_l |
Stopping iteration longitudinal predictor. |
mstop_ls |
Stopping iteration shared predictor. |
step.length |
Step length for boosting updates, typical value is 0.1 (default). |
betatimeind |
indicating which coefficient in shared predictor is time variable (number), |
Implements a gradient boosting algorithn to fit joint models considering a longitudinal predictor (w.r.t outcome y) and a shared predictor (affecting the longitudinal outcome and the time-to-event outcome). The relation between the two outcomes is described via the association parameter alpha. For more details, see Waldmann et al. (2016).
Waldmann, E., Taylor-Robinson, D., Klein, N., Kneib, T., Pressler, T., Schmid, M., & Mayr, A. (2016). Boosting Joint Models for Longitudinal and Time-to-Event Data. arXiv preprint arXiv:1609.02686.
1 2 3 4 5 6 7 8 9 10 | set.seed(123)
dat <- simJM(n = 400, n_i = 3, alpha = .5,
beta = c(1,2,3), betals = c(2,3,1),
betatimeind = 3, lambda = 0.6)
j1 <- JMboost(y = dat$y, Xl = dat$X, Xls = dat$Xls,
last = dat$last, delta = dat$delta,
id = dat$id, time = dat$time, lambda = 1, alpha = 0.1,
mstop_l = 100, mstop_ls = 100, step.length = 0.1,
betatimeind = 3)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.