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Tables for epidemiologists

  • 2 × 2 and 2 × 2 stratified tables for longitudinal, cohort study, case–control, and matched case–control data
  • Odds ratio, incidence ratio, risk ratio, risk difference, and attributable fraction
  • Confidence intervals for the above
  • Chi-squared, Fisher’s exact, and Mantel–Haenszel tests
  • Tests for homogeneity
  • Choice of weights for stratified tables: Mantel–Haenszel, standardized, or user specified
  • Exact McNemar test for matched case–control data
  • Tabulated odds and odds ratios
  • Score test for linear trend

Power and sample size

  • Stratified 2×2 tables (Cochran–Mantel–Haenszel test)
  • 1:M matched case–control studies
  • Trend in J×2 tables (Cochran–Armitage test)

Standardization of rates

  • Direct standardization
  • Indirect standardization

Generalized linear models for the binomial family

  • Individual-level or grouped data
  • Odds ratios, risk ratios, health ratios, and risk differences
  • Bayesian estimation

Additive models of risk New

  • relative excess risk due to interaction, excess relative risks, attributable proportion, and synergy index
  • confidence intervals for the above
  • models for binary and count outcomes, and survival–time data

Table symmetry and marginal homogeneity tests

  • n x n tables where there is one-to-one matching of cases and controls
  • Asymptotic symmetry and marginal homogeneity tests
  • Exact symmetry tests
  • Transmission disequilibrium test (TDT)

Kappa measure of interrater agreement

  • Two unique raters
  • Weights for weighting disagreements
  • Nonunique raters, variables record ratings for each rater
  • Nonunique raters, variables record frequency of ratings

Two-way table of frequencies

Brier score decomposition

U.S. Food and Drug Administration (FDA) submittals


  • Effect sizes for binary and continuous outcomes StataNow
  • Common-effect, fixed-effects, and random-effects models
  • Forest plots, funnel plots, and more plots
  • Subgroup meta-analysis
  • Meta-regression
  • Small-study effects and publication bias
  • Cumulative meta-analysis
  • Multivariate meta-analysis
  • And more

Receiver operating characteristic (ROC) analysis

  • Fit ROC regression models, with covariates
  • Calculate area under the curve
  • Calculate partial area under the curve
  • Obtain sensitivity for a given specificity, and vice versa
  • Test equality of ROC area against a "gold standard"
  • Šidák adjustment for multiple comparisons
  • Easy ROC curve plots for different classifiers and covariate values
  • ROC curve with simultaneous confidence bands

ICD-10 and ICD-9 codes

  • Designed for use with
    • The US National Center for Health Statistics (NCHS) ICD-10-CM diagnosis codes for healthcare encounter and claims data
    • The US Centers for Medicare and Medicaid Services (CMS) ICD-10-PCS procedure codes for healthcare claims data
    • The World Health Organization’s ICD-10 codes for morbidity and mortality reporting
    • NCHS ICD-9-CM diagnosis codes for healthcare encounter and claims data
    • CMS ICD-9-CM procedure codes for healthcare claims data
  • Suite of commands lets you:
    • Easily generate new variables based on codes
      • Indicators for different conditions
      • Short descriptions
      • Category codes from billable codes
      • And more
    • Verify that a variable contains valid codes and flag invalid codes
    • Standardize the format of codes
  • Interactive utilities let you
    • Look up descriptions for codes
    • Search for codes from keywords
  • ICD-10 and ICD-10-CM/PCS commands let you indicate the version of the codes in your dataset

Survival analysis

Causal inference/Treatment effects


Additional resources

See New in Stata 18 to learn about what was added in Stata 18.