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Despite advances in the design and analysis of CRTs, several challenges remain. First, there are many possible ways to specify the causal effect of interest (eg, at the individual\u2010level or at the cluster\u2010level). Second, the theoretical and practical performance of common methods for CRT analysis remain poorly understood. Here, we present a general framework to formally define an array of causal effects in terms of summary measures of counterfactual outcomes. Next, we provide a comprehensive overview of CRT estimators, including the<jats:italic>t<\/jats:italic>\u2010test, generalized estimating equations (GEE), augmented\u2010GEE, and targeted maximum likelihood estimation (TMLE). Using finite sample simulations, we illustrate the practical performance of these estimators for different causal effects and when, as commonly occurs, there are limited numbers of clusters of different sizes. 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Given its flexibility to estimate a variety of user\u2010specified effects and ability to adaptively adjust for covariates for precision gains while maintaining Type\u2010I error control, we conclude TMLE is a promising tool for CRT analysis.<\/jats:p>","DOI":"10.1002\/sim.9813","type":"journal-article","created":{"date-parts":[[2023,6,12]],"date-time":"2023-06-12T23:17:41Z","timestamp":1686611861000},"page":"3443-3466","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Defining and estimating effects in cluster randomized trials: A methods comparison"],"prefix":"10.1002","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4826-3611","authenticated-orcid":false,"given":"Alejandra","family":"Benitez","sequence":"first","affiliation":[{"name":"Genentech Inc. 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