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Biostatistics 

 

Biostatisticians rely on Stata because of its breadth, accuracy, extensibility, and reproducibility. Regardless of your focus area — public health, cancer, HIV, clinical observational studies, clinical trials — or your statistical approach, whether cross-sectional, longitudinal, or time-to-event, Stata provides all the statistics, graphics, and data-management tools needed to implement and study a wide range of biostatistical methods.



Features for biostatisticians

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

 

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

 

Treatment effects
Estimate treatment effects for continuous, binary, count, and survival outcomes and for multilevel and multivalued treatments. Obtain estimates of effects under endogeneity. Choose from inverse probability weights (IPW), propensity-score matching, covariate matching, regression adjustment, doubly-robust augmented IPW and IPW with regression adjustment models. And more.

 

Contingency tables
Obtain contingency tables to analyze prospective and retrospective studies, cohort data, case–control data, matched case–control data. 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.

 

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.

 

Linear and generalized linear models (GLMs)
Fit linear, quantile, truncated, and censored regressions and maximum likelihood models for binary, count, fractional, continuous, ordered, and multivariate outcomes. And more.

 

ANOVA/MANOVA.
Use ANOVA and multivariate ANOVA to test for differences between continuous outcomes by groups. Study balanced, unbalanced, factorial, nested, and mixed designs. Analyze repeated measures, marginal means, and contrasts. Draw profile and interactions plots. And more.

 

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.

 

Power and sample size
Determine the sample size needed for your experiment to recover meaningful effects without wasting resources. Obtain one-sample and two-sample tests of means, variances, proportions, and correlations. 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.

 

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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