# Power for Cluster-Randomized Trials: Software, Web app, and Methods

> **NIH NIH R01** · UNIVERSITY OF MASSACHUSETTS AMHERST · 2020 · $279,371

## Abstract

Summary
Cluster-randomized trials are an increasingly common trial design that has unique advantages for some
questions and is required for others. It has also spawned several related trial designs. Recent high-profile
studies using these designs include trials of Ebola vaccines and for preventing the spread of resistant
pathogens in hospitals. The key feature of cluster-randomized trials is that subjects are randomized in groups,
rather than as individuals, so that all members of a community, hospital, or practice receive the same
treatment.
All trials must have accurate power and sample size calculation for moral and ethical reasons. It is wrong to
randomize more persons than are needed for good power, as this exposes the excess persons to the risks of
randomization unnecessarily. It is also wrong to randomize fewer persons than create good power, as then all
are exposed to the risks of the study for naught—there is little hope of showing a benefit of any treatment-- and
a lack of study effect may be due to poor power rather than ineffective treatments. Though it is a lesser
concern, it is also unethical to have too small or large a sample size, as this wastes scarce resources such as
the investigators’ time and the funder’s dollars.
For some cluster-randomized trial designs, there exist analytic (closed-from) sample size formulae that rely on
assumptions that can be unrealistic. For other designs, only approximate formulae exist. In general, these
calculations can be found only in textbooks, scientific papers, and in software that is costly and can be difficult
to understand and apply. There are very limited options for the most accurate calculations, which are based
on simulations. Simulation-based power calculations can accommodate complex designs and realistic
scenarios that are only awkwardly possible in formulae.
We propose to generate a comprehensive free and open-source software suite to provide approximate,
analytic, and simulation-based power assessment. In addition, we will develop a web app for the code to allow
users who have less computing knowledge to make use of the software. Finally, we will make use of the
software to answer outstanding questions in the design of cluster-randomized trials.

## Key facts

- **NIH application ID:** 9897605
- **Project number:** 5R01GM121370-04
- **Recipient organization:** UNIVERSITY OF MASSACHUSETTS AMHERST
- **Principal Investigator:** Kenneth P. Kleinman
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $279,371
- **Award type:** 5
- **Project period:** 2017-04-15 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9897605

## Citation

> US National Institutes of Health, RePORTER application 9897605, Power for Cluster-Randomized Trials: Software, Web app, and Methods (5R01GM121370-04). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/9897605. Licensed CC0.

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