SOFTWARE/STATA

 

Survival analysis

Cox proportional hazards

  • time-varying covariates and censoring
  • Continuously time-varying covariates
  • conventional or robust estimates of variance
  • stratified estimation
  • Sampling weights and survey data
  • four ways to handle ties: Breslow, exact partial likelihood, exact marginal likelihood, and Efron
  • martingale, efficient score, Cox–Snell, Schoenfeld, and deviance residuals
  • tests for proportional hazards
  • estimates of baseline survival, hazard, and cumulative hazard functions
  • shared frailty models
  • Harrell’s C, Somers’ D, and Gönen and Heller’s K statistics measuring concordance
  • Multiple imputation
Competing-risks regression
  • Fine and Gray proportional subhazards model
  • Time-varying covariates
  • Cumulative-incidence graphs
  • Subhazard ratios
  • Multiple imputation  
  • Constraints

Parametric survival models

  • exponential
  • Weibull
  • Gompertz
  • lognormal
  • loglogistic
  • generalized log-gamma
  • Sampling weights and survey data
  • martingale-like, score, Cox–Snell, Schoenfeld, and deviance residuals
  • plots of predicted survival, hazard, and cumulative hazard functions
  • individual-level frailty
  • group-level or shared frailty
  • stratified models
  • linear constraints

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
  • conventional or robust estimates of variance
Contrasts*
  • Analysis of main effects, simple effects, interaction effects, partial interaction effects, and nested effects
  • Comparisons against reference groups, of adjacent levels, or against the grand mean
  • Orthogonal polynomials
  • Helmert contrasts
  • Custom contrasts
  • ANOVA-style tests
  • Contrasts of nonlinear responses
  • Multiple-comparison adjustments
  • Balanced and unbalanced data
  • Contrasts in odds-ratio metric
  • Contrasts of means, intercepts, and slopes
  • Graphs of contrasts
  • Interaction plots
Pairwise comparisons*
  • Compare estimated means, intercepts, and slopes
  • Compare marginal means, intercepts, and slopes
  • Balanced and unbalanced data
  • Nonlinear responses
  • Multiple-comparison adjustments: Bonferroni, Šidák, Scheffé, Tukey HSD, Duncan, and Student-Newman-Keuls adjustments
  • Group comparisons that are significant
  • Graphs of pairwise comparisons
 

Kaplan–Meier survival curves

  • graph estimates and confidence intervals
  • Confidence bands
  • Embedded risk tables
  • Adjustments for confounders
  • Stratification
  • Nelson–Aalen graphs of cumulative hazards

Life tables and analysis

  • Graphs and tables of estimates and confidence intervals
  • Mean survival times and confidence intervals
  • Cox regression adjustments
  • Actuarial adjustments
  • Tests for trend
  • Tests of equality—log-rank, Mantel–Haenszel, Wilcoxon–Breslow, Tarone–Ware, Fleming–Harrington, Peto–Peto–Prentice
Power analysis
  • Solve for sample size, power, or effect size
  • Log-rank test of survival curves
  • Cox proportional hazards model
  • Exponential regression
  • Time at risk, incidence rate, number of subjects, 25th, 50th, and 75th percentiles of survival time
  • Incidence-rate ratio and difference
  • Life tables
  • Rates and SMRs by one or more categorical variables
  • Stratified rate ratios

Utilities

  • create nested case-control datasets
  • split and join time records
  • conver snapshot data into time-span data
  • calculate person-time (person-years), incidence rates, and standardized mortality/morbidity ratios (SMR)

Predictions and estimates

  • mean or median time to failure
  • mean or median log time
  • hazard
  • hazard ratios
  • survival probabilities
Factor variables
  • Automatically create indicators based on categorical variables
  • Form interactions among discrete and continuous variables
  • Include polynomial terms
  • Perform contrasts of categories/levels
Marginal analysis
  • Estimated marginal means
  • Marginal and partial effects
  • Average marginal and partial effects
  • Least-squares means
  • Predictive margins
  • Adjusted predictions, means, and effects
  • Contrasts of margins*
  • Pairwise comparisons of margins*
  • Profile plots*
  • Graphs of margins and marginal effects*

A survival example session

* New in Stata 12

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