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Public health 

 
Researchers in public health and health services research rely on Stata because of its breadth, reproducibility, and ease of use. Whether you study interventions to address obesity, investigate small-area variations in care, or conduct program evaluation, Stata provides a range of analysis methods for experimental and observational data. It also gives you data-management tools specifically designed for health research and the ability to make publication-quality graphics for presentations.



Features for public health professionals:

Treatment effects
Estimate treatment effects for continuous, binary, and count outcomes and for multilevel and multivalued treatments. Get access to inverse probability weights (IPW), propensity-score matching, covariate matching, regression adjustment, doubly-robust augmented IPW and IPW with regression adjustment. And more.

 

Structural equation models
Construct models for continuous, binary, count, ordinal, multinomial, or survival outcomes and incorporate unobserved components at any level. Specify weights at each level of the model. Use with complex survey data. And more.

 

Multilevel mixed-effects models
Obtain fixed- and random-effects mixed-effects models for multilevel data using continuous, binary, count, and survival outcomes. Construct models for different correlation structures and nesting levels. And more.

 

Panel data
Obtain descriptive statistics and estimates for linear, nonlinear, and dynamic panel-data models. Get access to instrumental-variables random-effects, fixed-effects, and population-averaged estimates. Build your own dynamic model or use traditional models like Arellano–Bond. Fit models for binary, count, and continuous outcomes. And more.

 

Survey methods
Handle probability sampling weights, multiple stages of cluster sampling, stage-level sampling weights, stratification, and poststratification. Use variance techniques of balanced repeated replications, the bootstrap, the jackknife, successive difference replication, and linearization. Fit many different statistical models on complex survey data. And more.

 

Survival analysis
Perform survival-data analysis for your descriptive statistics, Cox proportional hazards, linear regression models, structural equation models, binary response models, discrete response models, instrumental-variables models, and regressions models with selection. And more.

 

Multiple imputation
Use descriptive statistics such as means, proportions, and ratios, and fit linear and nonlinear regressions, multilevel mixed-effects models, panel-data models, survival models, and much more using multiple imputation to account for missing data in your sample.

 

Time series
Fit multivariate and univariate time-series models. Obtain ARIMA, GARCH, ARCH, VAR, structural VAR, VEC, multivariate GARCH, multivariate ARCH, dynamic factors, and unobserved-components models. And more. 

 

Bayesian analysis
Fit Bayesian regression models using a Metropolis–Hastings Markov chain Monte Carlo (MCMC) method. Choose from a variety of supported models or program your own. Check convergence visually using extensive graphical tools. Compute posterior mean estimates and credible intervals for model parameters and functions of model parameters. Perform interval and model-based hypothesis testing. Compare models using Bayes factors.

 

Contrasts, pairwise comparisons, and margins
Use estimation results to obtain estimates and graphs of interactions, average effects, partial effects, contrasts, and pairwise comparisions. Draw profile and interactions plots. And more.

 

Mata
Program your own estimator using Stata's built-in matrix language, MATA. Use MATA interactively with Stata. Obtain inversions, decompositions, eigenvalues and eigenvectors, and numerical derivatives. Use LAPACK routines, real and complex numbers, string matrices, and object-oriented programming. And more.

 

 

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