| ICcforest-package {ICcforest} | R Documentation |
Construct a conditional inference forest model for interval-censored survival data
Description
Construct a conditional inference forest model for interval-censored survival data.
The main function of this package is ICcforest.
Details
Problem setup and existing methods
In many situations, the survival time cannot be directly observed and it is only
known to have occurred in an interval obtained from a sequence of examination times.
Methods like the Cox proportional hazards model rely on restrictive assumptions such as
proportional hazards and a log-linear relationship between the hazard function and
covariates. Furthermore, because these methods are often parametric, nonlinear effects
of variables must be modeled by transformations or expanding the design matrix to
include specialized basis functions for more complex data structures in real world
applications. The function ICtree in the LTRCtrees
package provides a conditional inference tree method for interval-censored survival data,
as an extension of the conditional inference tree method ctree
for right-censored data. Tree estimators are nonparametric and as such often exhibit
low bias and high variance. Ensemble methods like bagging and random forest can
reduce variance while preserving low bias.
ICcforest model
This package implements ICcforest, which extends the conditional inference forest
(see cforest) to interval censored data. ICcforest uses
conditional inference survival trees (see ICtree) as base learners.
The main function ICcforest fits a
conditional inference forest for interval-censored survival data, with parameter
mtry tuned by tuneICRF; gettree.ICcforest extracts
the i-th individual tree from the established ICcforest objects; and
predict.ICcforest computes predictions from ICcforest objects.
See Also
ICcforest, gettree.ICcforest, predict.ICcforest,
tuneICRF, sbrier_IC