API Reference

The response

The Surv object, the spine of every analysis.

Surv: A validated time-to-event response for survival analysis.
CensoringType: The censoring mechanism of a Surv response.

Non-parametric estimators

Kaplan-Meier survival, Nelson-Aalen cumulative hazard, and Turnbull’s NPMLE for interval-censored data.

KaplanMeier: Kaplan-Meier product-limit estimator of the survival function.
NelsonAalen: Nelson-Aalen estimator of the cumulative hazard.
Turnbull: Turnbull’s self-consistent NPMLE for interval-censored survival data.

Univariate parametric models

Standalone parametric distributions for data exploration and model selection.

Parametric: Univariate parametric survival distribution.
compare_distributions(): Fit multiple parametric distributions and return an AIC/BIC comparison table.

Regression

Cox proportional hazards, parametric AFT models, rank-based Buckley-James regression, and time-varying covariate utilities.

split_episodes(): Convert repeated-measurement data into counting-process (episode-split) format.
CoxPH: Cox proportional hazards model.
CoxNetCVResult: Result of cross-validated penalizer selection for CoxNet.
cv_coxnet(): Select the CoxNet penalizer by k-fold cross-validation.
CoxNet: Elastic-net penalized Cox proportional hazards model.
ZPHResult: Proportional-hazards test results (Grambsch-Therneau).
ZPHWindowResult: Test results for a single time window within a windowed proportional-hazards test.
SmoothHRResult: Smooth non-linear hazard ratio curve for a continuous covariate.
AFT: Parametric accelerated failure time model.
BuckleyJames: Buckley-James rank-based accelerated failure time regression.
PiecewiseExponential: Piecewise exponential survival model.
RoystonParmar: Royston-Parmar flexible parametric survival model (hazard or odds scale).

Machine learning

Tree-based ensemble survival models grown with the log-rank splitting rule, producing per-subject survival and cumulative-hazard functions.

SurvivalTree: A survival decision tree grown with the log-rank splitting rule.
RandomSurvivalForest: A random survival forest: a bagged ensemble of log-rank survival trees.
ExtraSurvivalTrees: An extremely-randomized survival forest (extra survival trees).
GradientBoostingSurvivalAnalysis: Gradient-boosted survival model minimizing the Cox partial-likelihood loss.

Competing risks & multi-state

Cumulative incidence, the Fine-Gray model, and multi-state transition probabilities.

AalenJohansen: Aalen-Johansen estimator of cumulative incidence functions for competing risks.
FineGray: Fine-Gray subdistribution hazard model for a competing-risks endpoint.
MultiState: Aalen-Johansen estimator of multi-state transition and occupancy probabilities.

Group comparisons

The log-rank test, MaxCombo test for non-proportional hazards, trend tests for ordered groups, the G-rho (Fleming-Harrington) family, Gray’s test for cumulative incidence, and restricted mean survival time (RMST) comparisons.

logrank_test(): Compare survival across groups using the weighted log-rank (G-rho) test.
maxcombo_test(): MaxCombo test: maximum of multiple weighted log-rank Z-statistics.
trend_test(): Test for linear trend across ordered groups using the log-rank test family.
pairwise_logrank_test(): Pairwise log-rank tests for all group pairs with multiple-comparison correction.
grays_test(): Compare cumulative incidence functions across groups using Gray’s test.
TestResult: The outcome of a log-rank group comparison test.
rmst_test(): Test for equality of RMST across two or more groups.
rmst_diff(): Compute the RMST difference between two groups and return a tidy DataFrame.
pairwise_rmst_test(): Pairwise RMST tests for all group pairs with multiple-comparison correction.
RMSTResult: Results of an RMST comparison test or difference calculation.
logrank_n_events(): Number of events needed for the log-rank test to reach a target power.
logrank_power(): Power of the log-rank test given the number of observed events.
logrank_sample_size(): Total sample size needed for the log-rank test to reach a target power.

Resampling & bootstrap

Bootstrap confidence intervals and k-fold cross-validation for survival models.

bootstrap(): Bootstrap confidence interval for a Kaplan-Meier summary statistic.
BootstrapResult: Result of a bootstrap confidence interval computation.
cross_validate(): Evaluate a survival model’s out-of-sample performance using k-fold cross-validation.

Prediction performance

Concordance, IPCW Brier score, time-dependent AUC, and calibration.

concordance_index(): Harrell’s concordance index: discrimination of risk scores against observed survival.
concordance_index_ipcw(): IPCW concordance index (Uno et al., 2011) for right-censored survival data.
brier_score(): IPCW (Graf) Brier score of predicted survival probabilities at specified times.
integrated_brier_score(): Integrated (time-averaged) Brier score across multiple time points.
time_dependent_auc(): IPCW (Uno) time-dependent AUC at specified times.
integrated_auc(): Time-averaged IPCW AUC across multiple time points.
calibration(): Assess calibration of predicted survival probabilities at a fixed time.

Visualization

Interactive survival curves, forest plots, and cumulative incidence visualizations with aligned numbers-at-risk tables.

plot_survival(): Plot Kaplan-Meier survival curve(s).
plot_predicted_survival(): Plot per-subject predicted survival or cumulative-hazard curves.
plot_forest(): Forest plot of hazard ratios (or other contrasts) with confidence intervals.
plot_cif(): Plot cumulative incidence functions from a fitted Aalen-Johansen estimator.
plot_influence(): Diagnostic scatter plots for identifying influential observations in a Cox model.
plot_smooth_hr(): Plot a smooth hazard ratio curve for a continuous covariate.
risk_table(): Return the numbers-at-risk table as a Great Tables object.

Core kernel

The risk-set / event-table tabulation shared by KM, log-rank, and Cox.

EventTable: Per-time risk-set tabulation (optionally within strata).
event_table(): Tabulate the event history: risk sets and events at each observed time.

Data access

Built-in datasets and dataset discovery helpers.

load_dataset(): Load a bundled dataset by name.
available_datasets(): Return the names of all bundled datasets.

Tidy summaries

Broom-style model summaries and extension points.

tidy(): Return a standardised term-level DataFrame for a fitted model.
glance(): Return a one-row model-summary DataFrame for a fitted model.
augment(): Return an observation-level DataFrame for a fitted model.

Visualization helpers

Low-level plotting helpers exported at the top level.

get_risk_table_frame(): Return a tidy frame of the number at risk per stratum at each of times.
theme_forest(): A minimal plotnine theme for forest plots.