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, hazard, and cumulative hazard functions
Watch How to fit a Cox PH model and check PH assumption.
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, hazard, and cumulative hazard functions
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, hazard, and cumulative hazard functions
Predictions and estimates
Mean or median time to failure
Mean or median log time
Hazard
Hazard ratios
Survival probabilities
Watch Parametric models for interval-censored survival-time data.
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
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
Treatment-effects estimation for observational survival-time data*
Regression adjustment
Inverse-probability weighting (IPW)
Doubly robust methods
IPW with regression adjustment
Weighted regression adjustment
Weibull, exponential, gamma, or lognormal outcome model
Average treatment effects (ATEs)
ATEs on the treated (ATETs)
Potential-outcome means (POMs)
Robust, bootstrap, and jackknife standard errors
Watch Treatment effects for survival models.
Random-effects parametric survival models*
Weibull, exponential, lognormal, loglogistic, or gamma model
Robust, cluster–robust, bootstrap, and jackknife standard errors
Watch Panel-data survival models.
Multilevel mixed-effects parametric survival models*
Weibull, exponential, lognormal, loglogistic, or gamma models
Robust and cluster–robust standard errors
Sampling weights and survey data
Marginal predictions and marginal means
Watch Multilevel survival analysis.
Structural equation models with survival outcomes*
Latent predictors of survival outcomes
Path models, growth curve models, and more
Weibull, exponential, lognormal, loglogistic, or gamma models
Survival outcomes with other outcomes
Sampling weights and survey data
Marginal predictions and marginal means
Watch Survival models for SEM.
Features of survival models
Single- or multiple-failure data
Left-truncation
Right-censoring
Time-varying regressors
Gaps
Recurring events
Start–stop format
Different types of failure events
Multiple time scales allowed
Postestimation Selector*
View and run all postestimation features for your command
Automatically updated as estimation commands are run
Watch Postestimation Selector.
Life tables and analysis
Graphs and tables of estimates and confidence intervals
Mean survival times and confidence intervals
Cox regression adjustments
Actuarial adjustments
Tests of equality: log-rank, Cox, Wilcoxon–Breslow–Gehan, Tarone–Ware, Peto–Peto–Prentice, and Fleming–Harrington
Tests for trend
Stratified test
Watch How to construct life tables.
Watch How to calculate the Kaplan–Meier survivor and Nelson–Aalen cumulative hazard functions.
Watch How to test the equality of survivor functions.
Power analysis
Solve for sample size, power, or effect size
Log-rank test of survival curves
Cox proportional hazards model
Clustered Data
Exponential regression
See all the power and sample size features.
Utilities
Create nested case–control datasets
Split and join time records
Convert snapshot data into time-span data
Watch How to set up your data for survival analysis.
Obtain summary statistics, confidence intervals, etc.
Confidence intervals for incidence-rate ratio and difference
Confidence intervals for means and percentiles of survival time
Tabulate failure rate
Calculate person-time (person-years), incidence rates, and standardized mortality/morbidity ratios (SMR)
Calculate rate ratios with the Mantel–Haenszel or Mantel–Cox method
Watch How to describe and summarize survival data.
Watch How to calculate incidence rates and incidence-rate ratios.
Graphs of survivor, hazard, or cumulative hazard function
Kaplan–Meier survival or failure function
Nelson–Aalen cumulative hazard
Graphs and comparative graphs
Confidence bands
Embedded risk tables
Adjustments for confounders
Stratification
Watch How to graph survival curves.
Additional resources
An Introduction to Survival Analysis Using Stata, Revised Third Edition by Mario Cleves, William Gould, and Yulia V. Marchenko
Flexible Parametric Survival Analysis Using Stata: Beyond the Cox Model by Patrick Royston and Paul C. Lambert
In the spotlight: Competing-risks regression
NetCourse 631: Introduction to Survival Analysis Using Stata
