Survival analysis
Stata has a complete suite of features for analyzing survival data,
including features to fit and analyze Cox proportional hazards models and
parametric survival models. Models may include shared frailties,
time-varying covariates, left truncation, right censoring, and
stratification. Baseline survival, hazard, cumulative hazard, hazard
ratios, time-to-failure, and survival probabilities can also be estimated.
Competing risks can be modeled with a subportional-subhazards estimator.
Power and sample-size computations are available for Cox and exponential
regression models. You can graph estimates and their confidence intervals,
test for equality of survivor functions, test for trends, and graph
Kaplan–Meier survivor functions and Nelson–Aalen cumulative hazard
functions. Parametric models can fit time-to-failure from several
distributions: exponential, Weibull, Gompertz, lognormal, loglogistic, and
generalized log-gamma.
For an overview, see the
introduction to Stata’s survival features from
Stata’s Survival Analysis and Epidemiological Tables Reference Manual
or see the list of survival analysis
features under Stata’s capabilities.
Explore Stata 12’s resources on survival analysis
Stata is a complete integrated statistical package that provides
everything you need for data analysis, data management, and graphics.
To learn more, click here.
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