Epitab in Stata: Classical Epidemiologic Analysis with 2×2 (confusion matrix) and Stratified Tables

Overview

Epitab is a suite of Stata commands designed for classical epidemiologic analyses based on 2×2 and stratified tables. It provides design-consistent estimation of effect measures, confidence intervals, and attributable fractions across cohort, case–control, cross-sectional, and matched study designs.

Unlike regression models (e.g., logistic, poisson, stcox), Epitab commands are table-based, transparent, and ideal for:

• Crude and stratified analyses

• Teaching epidemiologic estimands

• Reproducing textbook and guideline analyses

• Sanity-checking regression outputs

The Epitab family includes commands for rates, risks, odds, trend tests, Mantel–Haenszel adjustment, and matched data.

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Command Families by Study Design

1. Incidence-Rate Data (Person-Time)

ir — Incidence-Rate Ratio and Difference

Used when outcomes are expressed as events per person-time.

Key outputs

• Incidence rates (exposed vs unexposed)

• Incidence-rate ratio (IRR)

• Incidence-rate difference (IRD)

• Attributable / prevented fractions

• Exact or asymptotic CIs

Stratified options

• Mantel–Haenszel pooled IRR

• Homogeneity tests across strata

• Direct or indirect standardization

Immediate form

• iri — calculator-style input (counts + person-time)

When to prefer

• Unequal follow-up

• Dynamic populations

• Precursor to Poisson regression

Related models

• poisson, glm, family(poisson)

• stcox (when censoring matters)

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2. Cohort and Cross-Sectional Risk Data

cs — Risk Difference, Risk Ratio (± Odds Ratio)

Used when follow-up time is equal for all subjects or when analyzing cumulative incidence.

Key outputs

• Risk in exposed vs unexposed

• Risk difference (RD)

• Risk ratio (RR)

• Optional odds ratio (OR)

• Attributable / prevented fractions

Enhancements

• Fisher’s exact test

• Internal (Mantel–Haenszel) standardization

• Stratified analyses

Immediate form

• csi — calculator-style input from summary counts

When to prefer

• Cohort studies with fixed follow-up

• Teaching RR vs OR

• Baseline risk tables in trials

Regression analogues

• glm, family(binomial) link(log)

• Modified Poisson (glm, family(poisson))

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3. Case–Control and Cross-Sectional Odds

cc — Odds Ratio from 2×2 Tables

Used when sampling is conditioned on outcome.

Key outputs

• Odds ratio with CI

• Attributable fractions (exposed / population)

• Stratified Mantel–Haenszel ORs

Homogeneity testing

• Breslow–Day test

• Tarone’s test

Immediate form

• cci — calculator-style entry

Design principle

• Estimates OR correctly under case–control sampling

• Does not estimate risks or rates

Regression analogue

• logistic

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4. Odds Across Multiple Exposure Categories

tabodds — Odds, Odds Ratios, and Trend Tests

Used when exposure is categorical or ordinal.

Capabilities

• Tabulates odds by exposure category

• Computes ORs using user-defined reference

• Mantel–Haenszel adjusted ORs

• Score-based χ² tests:

  • Homogeneity

  • Linear trend in log-odds

Visualization

• Optional CI plots across categories

Use cases

• Dose–response screening

• Ordinal exposures

• Teaching trend concepts before regression

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5. Adjusted Odds Ratios (Stratified Control)

mhodds — Mantel–Haenszel Odds Ratios

Estimates adjusted odds ratios controlling for categorical confounders.

Features

• 1-df trend tests for ordered exposures

• Approximate log-OR using score statistics

• One-step Newton–Raphson estimation

Interpretation

• Population-averaged OR

• Transparent alternative to logistic regression

Best for

• Teaching confounding control

• Small datasets

• Reproducing classical epidemiology examples

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6. Matched Case–Control Studies

mcc — McNemar-Based Analysis

Used for pair-matched or set-matched designs.

Key outputs

• McNemar’s χ²

• Paired risk difference

• Paired risk ratio

• Odds ratio with CI

Immediate form

• mcci — direct cell-count entry

Design note

• Conditions on matched pairs

• Correct for within-pair correlation

Regression analogue

• clogit

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Why Use Epitab in Modern Clinical Research?

Strengths

• Design-faithful estimands

• Minimal assumptions

• Fully transparent calculations

• Ideal for methods sections and teaching

• Excellent diagnostic tool before regression

Limitations

• Limited covariate adjustment

• Not suitable for continuous predictors

• No flexible functional forms

• Not prediction-oriented

Best practice:Use Epitab first, then confirm with regression models.

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Teaching & Workflow Recommendations

• Cohort study? Start with cs, confirm with log-binomial or Poisson

• Person-time? Use ir, then Poisson or Cox

• Case–control? Begin with cc or tabodds, then logistic

• Matched data? Use mcc, then clogit

Epitab provides the epidemiologic intuition that regression often obscures.

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Conclusion

Epitab remains one of Stata’s most valuable—but underused—toolkits for classical epidemiologic analysis. For CECS PhD students and clinical researchers, it bridges the gap between design-based reasoning and model-based inference, ensuring that effect estimates remain interpretable, reproducible, and scientifically grounded.