EconPapers    
Economics at your fingertips  
 

Sample size estimation for the ratio of count outcomes in a cluster randomized trial using GEE

Jijia Wang, Song Zhang and Chul Ahn

Communications in Statistics - Theory and Methods, 2025, vol. 54, issue 17, 5470-5479

Abstract: Count outcomes often occur in cluster randomized trials. Particularly in the context of epidemiology, the ratio of incidence rates has been used to assess the effectiveness of an intervention. In practice, cluster sizes typically vary across clusters, and sample size estimation based on a constant cluster size assumption may lead to underpowered studies. To address this issue, we propose a sample size method based on the generalized estimating equation (GEE) approach to test the ratio of two incidence rates. A closed–form sample size formula is presented, which is flexible to account for unbalanced randomization and randomly varying cluster sizes. Simulations were performed to assess its performance. In cluster randomized trials of vaccine efficacy, the ratio of disease incidence rates has been frequently used to demonstrate that the vaccine reduces the occurrence of a disease compared to placebo or active control. An application example to the design of a vaccine efficacy cluster randomized trial is presented.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2024.2439998 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:54:y:2025:i:17:p:5470-5479

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20

DOI: 10.1080/03610926.2024.2439998

Access Statistics for this article

Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe

More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2025-08-05
Handle: RePEc:taf:lstaxx:v:54:y:2025:i:17:p:5470-5479