Search
   >> Home >> Products >> Features >> Survival analysis
Order Stata

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, hazard, and cumulative hazard functions

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

Treatment-effects estimation for observational survival-time data New

  • 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

Random-effects parametric survival models New

  • Weibull, exponential, lognormal, loglogistic, or gamma model
  • Robust, cluster–robust, bootstrap, and jackknife standard errors

Multilevel mixed-effects parametric survival models New

  • Weibull, exponential, lognormal, loglogistic, or gamma models
  • Robust and cluster–robust standard errors
  • Sampling weights and survey data
  • Marginal predictions and marginal means

Structural equation models with survival outcomes New

  • 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

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 New

  • View and run all postestimation features for your command
  • Automatically updated as estimation commands are run

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

Power analysis

  • Solve for sample size, power, or effect size
  • Log-rank test of survival curves Updated
  • Cox proportional hazards model Updated
  • Exponential regression Updated
  • 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

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

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

A survival example session

Additional resources

See New in Stata 14 for more about what was added in Stata 14.

The Stata Blog: Not Elsewhere Classified Find us on Facebook Follow us on Twitter LinkedIn Google+ Watch us on YouTube