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See what's new in survival analysis.

Cox proportional hazards

• Time-varying covariates and censoring
• Continuously time-varying covariates
• Four ways to handle ties: Breslow, exact partial likelihood, exact marginal likelihood, and Efron
• Robust, cluster–robust, bootstrap, and jackknife standard errors
• Stratified estimation
• Shared frailty models
• Sampling weights and survey data
• Multiple imputation
• Martingale, efficient score, Cox–Snell, Schoenfeld, and deviance residuals
• Likelihood displacement values, LMAX values, and DFBETA influence measures
• Harrell’s C, Somers’ D, and Gönen and Heller’s K statistics measuring concordance
• Tests for proportional hazards
• Graphs of estimated survivor, failure, hazard, and cumulative hazard functions Updated
• Goodness-of-fit plot Updated

Cox proportional hazards model for interval-censored data

• Case II interval-censored data
• Current status or case I interval-censored data
• Time-varying covariates
• Testing proportional-hazards assumption
• Two ways to estimate the baseline hazard function
• Four methods to estimate standard errors
• Robust and cluster–robust standard errors
• Graphs of estimated survivor, hazard, and cumulative hazard functions Updated
• Stratified models
• Proportional-hazards assumption plots
• Goodness-of-fit plot Updated
• Predictions
• Hazard ratio
• Hazard contributions for interval endpoints
• Baseline survivor function for interval endpoints
• Baseline cumulative hazard function for interval endpoints
• Martingale-like residuals
• Cox–Snell-like residuals
• Time-varying predictions

Marginal Cox PH model for interval-censored multiple-event data New

• Single-record-per-event interval-censored data
• Multiple-record-per-event interval-censored data
• Flexible model specification
• Time-varying covariates
• Test for proportional-hazards assumption
• Robust and cluster–robust standard errors
• Powerful test for common covariate effect across all events
• Stratified models
• Graphs of estimated survivor, hazard, and cumulative hazard functions
• Proportional-hazards assumption plots
• Goodness-of-fit plot
• Predictions
• Hazard ratio
• Hazard contributions for interval endpoints
• Baseline survivor function for interval endpoints
• Baseline cumulative hazard function for interval endpoints
• Martingale-like residuals
• Cox–Snell-like residuals
• Time-varying predictions

Competing-risks regression

• Fine and Gray proportional subhazards model
• Time-varying covariates
• Robust, cluster–robust, bootstrap, and jackknife standard errors
• Multiple imputation
• Efficient score and Schoenfeld residuals
• DFBETA influence measures
• Subhazard ratios
• Cumulative subhazard and cumulative incidence graphs

Parametric survival models

• Weibull, exponential, Gompertz, lognormal, loglogistic, or generalized gamma model
• Robust, cluster–robust, bootstrap, and jackknife standard errors
• Stratified models
• Individual-level frailty
• Group-level or shared frailty
• Sampling weights and survey data
• Multiple imputation
• Martingale-like, score, Cox–Snell, and deviance residuals
• Graphs of estimated survivor, failure, hazard, and cumulative hazard functions
• Goodness-of-fit plot Updated
• Predictions and estimates
• Mean or median time to failure
• Mean or median log time
• Hazard
• Hazard ratios
• Survival probabilities

Interval-censored parametric survival models

• Weibull, exponential, Gompertz, lognormal, loglogistic, or generalized gamma
• Both proportional-hazards and accelerated failure-time metrics
• Robust, cluster–robust, bootstrap, and jackknife standard errors
• Stratified models
• Sampling weights and survey data
• Flexible modeling of ancillary parameters
• Martingale-like, score, and Cox–Snell residuals
• Graphs of estimated survivor, failure, hazard, and cumulative hazard functions Updated
• Goodness-of-fit plot Updated
• Predictions and estimates
• Mean or median time to failure
• Mean or median log time
• Hazard
• Hazard ratios
• Survival probabilities

Bayesian parametric survival models

• Weibull, exponential, Gompertz, lognormal, loglogistic, or generalized gamma
• Both proportional-hazards and accelerated failure-time metrics
• Stratified models
• Individual-level frailty
• Group-level or shared frailty
• Flexible modeling of ancillary parameters
• Postestimation

Bayesian multilevel parametric survival models

• Weibull, exponential, lognormal, loglogistic, or gamma
• Both proportional-hazards and accelerated failure-time metrics
• Two-, three-, and higher-level models
• Nested and crossed random effects
• Random intercepts and random coefficients
• Flexible modeling of ancillary parameters
• Postestimation

Finite mixtures of parametric survival models

• Weibull, exponential, Gompertz, lognormal, loglogistic, or generalized gamma
• Both proportional-hazards and accelerated failure-time metrics
• Robust, cluster–robust, bootstrap, and jackknife standard errors
• Sampling weights and survey data
• Postestimation

Utilities

• Create nested case–control datasets
• Split and join time records
• Convert snapshot data into time-span data