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Epidemiology 

 
Epidemiologists have relied on Stata for over 30 years because of its specialized epidemiologic commands, accuracy, and ease of use. Whether you are researching infectious diseases, investigating exposure to pathogens, or conducting genome-wide association studies, Stata provides the data-management and statistical tools to support your research. It also gives you the ability to make publication-quality graphics so you can clearly display your findings.



Features for epidemiologists:

Epidemiological tables
Obtain contingency tables to analyze prospective and retrospective studies, cohort study data, case-control data, and matched case-control data. 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.

 

Survival analysis
Preform 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.

 

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.

 

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.

 

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.

 

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.

 

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.

 

 

 

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