RFmstate: Random Forest-Based Multistate Survival Analysis
Fits cause-specific random survival forests for flexible
multistate survival analysis with covariate-adjusted transition
probabilities computed via product-integral. State transitions are
modeled by random forests. Subject-specific transition probability matrices
are assembled from predicted cumulative hazards using the product-integral formula.
Also provides a standalone Aalen-Johansen nonparametric estimator as
a covariate-free baseline. Supports arbitrary state spaces with any
number of states (three or more) and any set of allowed transitions,
applicable to clinical trials, disease progression, reliability
engineering, and other domains where subjects move among discrete
states over time. Provides per-transition feature importance,
bias-variance diagnostics, and comprehensive visualizations. Handles
right censoring and competing transitions. Methods are described in
Ishwaran et al. (2008) <doi:10.1214/08-AOAS169> for random survival
forests, Putter et al. (2007) <doi:10.1002/sim.2712> for multistate
competing risks decomposition, and Aalen and Johansen (1978)
<https://www.jstor.org/stable/4615704> for the nonparametric
estimator.
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=RFmstate
to link to this page.