Surv-TCAV is introduced as a concept-based interpretability
framework for gradient-boosted accelerated failure time (AFT) survival
models on clinical tabular data. Using the Primary Biliary Cholangitis
dataset (historical pbc; PBC-276 complete cases over 17 covariates), an
XGBoost model with AFT objective is trained and assessed for
discrimination with Harrell’s concordance index (C-index) and for
calibration with the integrated Brier score (IBS) using
inverse-probability-of-censoring weights. Interpretability is delivered
at two levels: feature attributions via TreeSHAP and directional concept
sensitivity via Surv-TCAV, which quantifies how clinician-defined
concepts perturb the predicted log-time location parameter μ. Across 25
independent 80/20 train/validation splits with fixed boosting rounds (no
early stopping), the boosted AFT attains C-index = 0.835 ± 0.039 and IBS
= 0.357 ± 0.018. A one-sample t-test of split-wise C-indices against
0.85 yields t = −1.863 (df = 24), two-sided p = 0.075, thus
statistically indistinguishable from strong mid–0.8 discrimination at α
= 0.05. Surv-TCAV reveals clinically coherent negative directional
effects for low albumin, older age, and clinical complications, with
more modest but still negative effects for cholestasis and coagulopathy.
Feature-level attributions further suggest a small residual protective
association for female sex after adjustment. The protocol delivers
concept-based interpretability for boosted survival models on structured
clinical data with full reproducibility.