ICcforest-package: Construct a conditional inference forest model for...
In ICcforest: An Ensemble Method for Interval-Censored Survival Data

Construct a conditional inference forest model for interval-censored survival data. The main function of this package is ICcforest.

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.