RFmstate: Random Forest-Based Multistate Survival Analysis

Fits transition-specific cause-specific random survival forests on a clock-reset duration scale for acyclic, non-recurrent multistate processes. Entry-conditioned state-occupation probabilities are assembled from predicted cumulative hazards by semi-Markov entry-mass and sojourn convolution on a validated regular grid. The one-row-per-subject interface supports one common initial state, one recorded entry per state, baseline time-fixed covariates, competing exits, and independent right censoring. Left truncation, recurrent visits, directed cycles, time-dependent covariates, and ongoing-sojourn dynamic prediction are not supported. The package also provides calendar-time Aalen-Johansen point estimates as a covariate-free descriptive baseline, transition-specific permutation importance, genuine ranger edge OOB concordance, and patient-level cross-validated IPCW state-probability scoring. 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.

install.packages("RFmstate")

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