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## Survival analysis

 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 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 Convert snapshot data into time-span data Calculate person-time (person-years), incidence rates, and standardized mortality/morbidity ratios (SMR) Kaplan–Meier survival curves Graphs and comparative graphs Confidence bands Embedded risk tables Adjustments for confounders Stratification Nelson–Aalen graphs of cumulative hazards 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 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 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 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 Explore more about survival analysis in Stata.

See New in Stata 12 for more about what was added in Stata Release 12.

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