Longitudinal Models · GEE-2G-T

Sample Size for Two-Group GEE: Difference at a Specified Follow-Up

Plan a population-averaged Wald test of the comparison-minus-reference marginal mean difference at one prespecified follow-up.

1. Planning setup

Include a baseline measurement

Baseline receives zero weight in the target contrast.

Working correlation structure

Follow-up measurements per participant

Time from baseline to final follow-up

Follow-ups are equally spaced.

Target follow-up measurement

2. Information obtained from previous studies or pilot data

Marginal outcome standard deviation

Assumed common across groups and visits.

3. Inputs decided by the researcher for the new study

Smallest clinically important difference at the target follow-up

Population-average comparison-group minus reference-group mean. Negative values are accepted.

Comparison-to-reference allocation ratio

Type I error, alpha (%)

Alternative hypothesis

Power (%)

Anticipated participant dropout/non-response (%)

4. Results

Enter the marginal design assumptions, select the target follow-up, and select Calculate.

Formula, assumptions, limitations, and methodological note

Example clinical scenario

A randomized hypertension trial measures systolic blood pressure at baseline and at 1, 3, 6, and 12 months. The primary endpoint is the population-average treatment-group difference at 12 months. Earlier measurements remain in the GEE analysis, but the hypothesis concerns only the prespecified 12-month comparison.

Formula and methodological note

For marginal covariance V = A½R(ρ)A½, categorical-visit model-based GEE information is D′V⁻¹D. The target contrast has weight one at the selected comparison-minus-reference follow-up coefficient and zero elsewhere. Its allocation-unit variance is c′M⁻¹c. Exact integer allocation units are searched until normal-theory Wald power reaches the target.

This version assumes correctly specified model-based planning covariance, a continuous outcome, common marginal SD, equally spaced visits, independent participants, and randomized groups for common-baseline adjustment. Robust-sandwich analysis does not protect planning from incorrect variance or correlation assumptions. Dropout is recruitment inflation only. Binary outcomes, irregular schedules, visit-specific variances, informative missingness, extra clustering, omnibus contrasts, and small-sample sandwich corrections are outside scope. This tool supports research planning and does not replace statistical consultation.

Technical results

Method references

Rochon J. Application of GEE procedures for sample size calculations in repeated measures experiments. Stat Med. 1998;17:1643–1658.
Liang KY, Zeger SL. Longitudinal data analysis using generalized linear models. Biometrika. 1986;73:13–22.
Statsmodels developers. Generalized Estimating Equations and NormalIndPower documentation.

Suggested citation

Yaacob, Najib Majdi. “Sample Size for Two-Group GEE: Difference at a Specified Follow-Up (GEE-2G-T).” Web calculator. NajibMY, 2026. Accessed August 1, 2026. https://najibmy.netlify.app/tools/sample-size/clinical-complex-study-designs/gee-difference-at-follow-up/.