API Reference

The response

The Surv object, the spine of every analysis.

Kaplan-Meier survival and Nelson-Aalen cumulative hazard.

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.

Cox proportional hazards, parametric AFT models, 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).
AFT: Parametric accelerated failure time model.
RoystonParmar: Royston-Parmar flexible parametric survival model (proportional hazards scale).

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.

The log-rank test, trend tests for ordered groups, the G-rho (Fleming-Harrington) family, and restricted mean survival time (RMST) comparisons.

logrank_test(): Compare survival across groups using the weighted log-rank (G-rho) test.
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.
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.

Concordance, IPCW Brier score, time-dependent AUC, calibration, and k-fold cross-validation.

concordance_index(): Harrell’s concordance index: discrimination of risk scores against observed survival.
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.
cross_validate(): Evaluate a survival model’s out-of-sample performance using k-fold cross-validation.

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

plot_survival(): Plot Kaplan-Meier survival curve(s).
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.
risk_table(): Return the numbers-at-risk table as a Great Tables object.

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.

Built-in datasets and dataset discovery helpers.

Broom-style model summaries and extension points.

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.

Exported subpackages for direct access to grouped functionality.

data.available_datasets(): Return the names of all bundled datasets.
data.load_dataset(): Load a bundled dataset by name.
summaries.augment(): Return an observation-level DataFrame for a fitted model.
summaries.glance(): Return a one-row model-summary DataFrame for a fitted model.
summaries.register_augment(): Register an augment adapter for a model class.
summaries.register_glance(): Register a glance adapter for a model class.
summaries.register_tidier(): Register a tidy adapter for a model class.
summaries.tidy(): Return a standardised term-level DataFrame for a fitted model.
viz.plot_survival(): Plot Kaplan-Meier survival curve(s).
viz.plot_cif(): Plot cumulative incidence functions from a fitted Aalen-Johansen estimator.
viz.plot_forest(): Forest plot of hazard ratios (or other contrasts) with confidence intervals.
viz.theme_forest(): A minimal plotnine theme for forest plots.
viz.risk_table(): Return the numbers-at-risk table as a Great Tables object.
viz.get_risk_table_frame(): Return a tidy frame of the number at risk per stratum at each of times.