General linear models
Fit one- and two-way models. Or fit models with three, four, or even more factors. Analyze data with nested factors, with fixed and random factors, or with repeated measures. Use ANCOVA models when you have continuous covariates and MANOVA models when you have multiple outcome variables. Further explore the relationships between your outcome and predictors by estimating effect sizes and computing least-squares and marginal means. Perform contrasts and pairwise comparisons. Analyze and plot interactions. And much more.
Linear, binary, and count regressions
Fit classical ANOVA and linear regression models of the relationship between a continuous outcome, such as weight, and the determinants of weight, such as height, diet, and level of exercise. If your response is binary, ordinal, categorical, or count, don't worry. Stata has estimators for these types of outcomes too. Use logistic regression to estimate odds ratios. Estimate incidence rates using a Poisson model. Analyze matched case–control data with conditional logistic regression. A vast array of tools is available after fitting such models. Predict outcomes and their confidence intervals. Test equality of parameters. Compute linear and nonlinear combinations of parameters. And much more.
Power and sample size
Before you conduct your experiment, determine the sample size needed to detect meaningful effects without wasting resources. Do you intend to perform tests of means, variances, proportions, or correlations? Do you want to fit a one-way, two-way, or repeated-measures ANOVA model? Do you plan to fit a Cox proportional-hazards model or compare survivor functions using a log-rank test or exponential regression? Use Stata's power commands or interactive Control Panel to compute power and sample size, create customized tables, and automatically graph the relationships between power, sample size, and effect size for your planned study. And much more.
Marginal means, contrasts, and interactions
Marginal means and contrasts let you analyze the relationships between your outcome variable and your covariates, even when that outcome is binary, count, ordinal, categorical, or survival. Compute adjusted predictions with covariates set to interesting or representative values. Or compute marginal means for each level of a categorical covariate. Make comparisons of the adjusted predictions or marginal means using contrasts. If you have multilevel data and random effects, these effects are automatically integrated out to provide marginal (that is, population-averaged) estimates. After fitting almost any model in Stata, analyze the effect of covariate interactions, and easily create plots to visualize those interactions. And much more.
Multilevel mixed-effects models
Whether the groupings in your data arise in a nested fashion (patients nested in clinics and clinics nested in regions) or in a nonnested fashion (regions crossed with occupations), you can fit a multilevel model to account for the lack of independence within these groups. Fit models for continuous, binary, count, ordinal, and survival outcomes. Estimate variances of random intercepts and random coefficients. Compute intraclass correlations. Predict random effects. Estimate relationships that are population averaged over the random effects. And much more.
Account for missing data in your sample using multiple imputation. Choose from univariate and multivariate methods to impute missing values in continuous, censored, truncated, binary, ordinal, categorical, and count variables. Then, in a single step, estimate parameters using the imputed datasets, and combine results. Fit a linear model, logit model, Poisson model, hierarchical model, survival model, or one of the many other supported models. Use the mi command, or let the Control Panel interface guide you through your entire MI analysis. And much more.
Analyze duration outcomes—outcomes measuring the time to an event such as failure or death—using Stata's specialized tools for survival analysis. Account for the complications inherent in survival data, such as sometimes not observing the event (censoring), individuals entering the study at differing times (delayed entry), and individuals who are not continuously observed throughout the study (gaps). You can estimate and plot the probability of survival over time. Or model survival as a function of covariates using Cox, Weibull, lognormal, and other regression models. Predict hazard ratios, mean survival time, and survival probabilities. Do you have groups of individuals in your study? Adjust for within-group correlation with a random-effects or shared frailty model. And much more.
Want to analyze data from a prospective (incidence) study, cohort study, case–control study, or matched case–control study? Stata's tables for epidemiologists make it easy to summarize your data and compute statistics such as incidence-rate ratios, incidence-rate differences, risk ratios, risk differences, odds ratios, and attributable fractions. You can analyze stratified data too—compute Mantel–Haenszel combined estimates, perform tests of homogeneity, and standardize estimates. If you have an ordinal rather than binary exposure, you can perform a test for a trend. And much more.
Stata is designed for reproducible research, including the ability to create dynamic documents incorporating your analysis results. Create Word or PDF files, populate Excel worksheets with results and format them to your liking, and mix Markdown, HTML, Stata results, and Stata graphs, all from within Stata. And much more.
I've used a lot of stat packages over the years, but I find that I'm using Stata 95% of the time now. It's wonderful! Its speed and power are much touted, but its simplicity for beginners is perhaps one of its best features.
— Rodney Hayward
University of Michigan's Schools of Medicine & Public Health, Ann Arbor VA's Center for Clinical Management Research
Intuitive and easy to use.
Once you learn the syntax of one estimator, graphics command, and data management tool, you will effortlessly understand the rest.
Accuracy and reliability.
Stata is extensively and continually tested. Stata's tests produce approximately 4 million lines of output.
One package. No modules.
When you buy Stata, you obtain everything for your statistical, graphical, and data analysis needs. You do not need to buy separate modules or import your data to specialized software.
Write your own Stata programs.
You can easily write your own Stata programs and commands to share with others or to simplify your work using Stata's do-files, ado-files, and matrix-language program, Mata. Moreover, you can benefit from the thousands of Stata user-written programs.
Stata offers 27 volumes with more than 14,000 pages of PDF documentation containing calculation formulas, detailed examples, references to the literature, and in-depth discussions. Stata's documentation is a great place to learn about Stata and the statistics, graphics, or data management tools you are using for your research.
Top-notch technical support.
Stata's technical support is known for their prompt, accurate, detailed, and clear responses. People answering your questions have master's and PhD degrees in relevant areas of research.
Stata's YouTube has over 100 videos with a dedicated playlist of methodologies important to medical researchers. And they are a convenient teaching aid in the classroom.
Stata Press offers books with clear, step-by-step examples that make teaching easier and that enable students to learn and medical researchers to implement the latest best practices in analysis.