Greenwood Roadmap

Greenwood is built in dependency-ordered, individually shippable steps. This is the public capability roadmap.

Planned — Near Term

Descriptive and exploratory features building on the core estimators.

RMST Comparisons

Restricted mean survival time analysis between groups.

  • RMST differences with confidence intervals
  • Hypothesis tests for RMST equality
  • Ratio and percentage difference estimands
  • Stratified RMST comparisons

AFT Model Extensions

Advanced predictions and conditional inference for accelerated failure time (AFT) models.

  • Conditional expectation predictions: expected remaining lifespan E(T - t₀ | T ≥ t₀) for censored subjects
  • Log-normal and log-logistic closed-form expectation formulas using scipy (error function and incomplete beta)
  • Conditional mean survival E[T | T > t₀] and E[T - t₀ | T > t₀] for all AFT distributions
  • Mean survival time (RMST) and conditional mean predictions at arbitrary follow-up times

Exploratory Plots

Visualization for grouped survival analyses.

  • Cumulative incidence function curves (competing risks by group)
  • Forest plots for stratified hazard ratios and RMST differences
  • Aligned at-risk tables for grouped curves

Model Validation and Performance

Robust cross-validation and performance assessment for imbalanced survival data.

  • Stratified k-fold cross-validation ensuring balanced event representation across folds
  • Handling of highly imbalanced datasets (common in survival analysis) to prevent singular matrix errors
  • Performance metrics (concordance, Brier score) computed reliably with stratified splits
  • Documentation and examples for model selection with imbalanced event data
  • Performance optimization for large datasets (memory efficiency, computation speed)
  • Numerical stability benchmarks and recommendations for data scale

Confidence Intervals & Inference

Systematic confidence interval and standard error support across all estimators.

  • Confidence intervals for Cox model coefficients and hazard ratios (analytical)
  • Kaplan-Meier survival function confidence intervals (Greenwood method and alternatives)
  • Cumulative incidence function confidence intervals (competing risks)
  • Standard errors and CIs for parametric model predictions
  • Bootstrap and analytical methods for uncertainty quantification
  • Predictive intervals for time-varying Cox model forecasts

Univariate Parametric Models

Standalone parametric distributions for data exploration and model selection.

  • Weibull, exponential, log-normal, and log-logistic distributional models
  • Goodness-of-fit assessment and model comparison
  • Maximum likelihood parameter estimation with standard errors
  • Survival, hazard, and quantile predictions from fitted models

Planned — Medium Term

Regression model extensions and flexible semi-parametric approaches.

Time-Varying Covariates

Cox regression with covariates that evolve over follow-up time.

  • Counting-process form integration for covariate changes
  • Episode-splitting and data reshaping utilities
  • Risk-set calculations with time-varying exposure
  • Predictions at specified covariate trajectories

Cox Residual Diagnostics

Outlier detection and case-level assessment for Cox models.

  • Deviance and dfbeta residuals for influence assessment
  • Scaled Schoenfeld residuals for proportional-hazards diagnosis
  • Leverage and hat-matrix diagnostics
  • Visualizations for outlier and influential point detection

Advanced Proportional-Hazards Tests

Extended testing of the Cox model assumptions.

  • cox_zph with Kaplan-Meier and rank-based time transforms
  • Time-stratified tests for non-proportional hazards
  • Robust sandwich variance for model misspecification

Flexible Parametric Models

Semi-parametric and parametric spline-based hazard regression.

  • Royston-Parmar restricted cubic spline models for hazard and survival
  • Piecewise exponential models with optimal knot selection
  • Generalized gamma regression (encompasses Weibull, log-normal, exponential)
  • Parametric predictions: survival, hazard, and quantiles at arbitrary times

Planned — Long Term

Advanced estimators for complex survival problems and specialized applications.

Additive Hazards & Cure Models

Alternative hazard structures and zero-inflated survival models.

  • Aalen additive model for additive (vs. proportional) hazard regression with constrained optimization to ensure non-negative hazards and proper survival functions
  • Mixture cure models for populations with long-term survivors
  • Non-parametric maximum likelihood estimation (NPMLE) for cure fractions
  • Goodness-of-fit tests and model comparison for cure models

Advanced Competing Risks & Multi-State

Extended methods for cause-specific and multi-state analyses.

  • Gray’s test for differences in cumulative incidence across groups
  • Variance estimation for multi-state transition probabilities
  • Pseudo-observation approach for CIF and multi-state occupancy regression
  • Custom estimands via pseudo-observations framework

Frailty and Penalized Regression

Random-effects and regularized Cox models.

  • Shared frailty models (random intercept) for clustered or familial data
  • Frailty variance estimation and inference
  • Elastic-net Cox regression (ridge, lasso, elastic-net) for high-dimensional covariates
  • Regularization parameter selection via cross-validation

Advanced Performance Metrics

Discrimination and calibration assessment beyond point-in-time.

  • Time-dependent AUC (area under cumulative/dynamic ROC curve)
  • Integrated discrimination improvement (IDI) and net reclassification improvement (NRI)
  • Calibration curves and calibration-in-the-large over follow-up time
  • Time-dependent Brier score refinements and sensitivity analyses

Platform & Interop

Performance and ecosystem integration toward 1.0.

  • Full backend matrix algebra with accelerated kernels (JAX/Numba) for ultra-large datasets (100k+ rows)
  • Finalized extension protocols and Narwhals dataframe backend completeness
  • Full interoperability with Great Summaries (tbl_survfit, tbl_regression)
  • Migration guides for users transitioning from R’s survival package

Feedback & Contributions

Have ideas for features not listed here? Open an issue with the enhancement label. Contributions to any planned item are welcome so check existing issues first to avoid duplication.

This roadmap is a living document. It is updated as features ship and new priorities emerge.