uddbart: Unified Dynamic Deep 'BART' for Interval-Censored Survival
Implements U-DDBART-IC, a unified Bayesian workflow for dynamic
risk prediction from irregular longitudinal biomarkers when event times are
interval-censored between clinical visits. The package turns long-format
biomarker histories and patient-level interval endpoints L, R, C and delta
into a discrete-time follow-up grid, summarises each landmark history with
nine interpretable trajectory features (current, baseline and previous
biomarker values, last visit gap, local slope, cumulative decline, best
value, elapsed time and visit count), fits discrete-time interval hazards
using optional logit-link Bayesian additive regression trees, a generalized
linear model fallback, or a lightweight variational approximation,
accumulates survival from the discrete-time product, and evaluates the
interval-censored likelihood. Fitted models return landmark risk
predictions over user-specified horizons with posterior or bootstrap
uncertainty by evaluating survival ratios across fitted hazard draws.
Utilities are provided for simulation, staged model fitting, plotting and
summarising dynamic risk curves, IPCW Brier scores, cumulative/dynamic
time-dependent area under the curve, calibration tables, and an anonymised
chronic myeloid leukaemia molecular-monitoring example data set.
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