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Generalized linear mixed models (GLMM) are commonly used to analyze clustered data, but when the number of clusters is small to moderate, standard statistical tests may produce elevated type I error rates. Small‐sample corrections have been proposed for continuous or binary outcomes without covariate adjustment. However, appropriate tests to use for count outcomes or under covariate‐adjusted models remains unknown. An important setting in which this issue arises is in cluster‐randomized trials (CRTs). Because many CRTs have just a few clusters (eg, clinics or health systems), covariate adjustment is particularly critical to address potential chance imbalance and/or low power (eg, adjustment following stratified randomization or for the baseline value of the outcome). We conducted simulations to evaluate GLMM‐based tests of the treatment effect that account for the small (10) or moderate (20) number of clusters under a parallel‐group CRT setting across scenarios of covariate adjustment (including adjustment for one or more person‐level or cluster‐level covariates) for both binary and count outcomes. We find that when the intraclass correlation is non‐negligible (≥$$ \ge $$0.01) and the number of covariates is small (≤$$ \le $$2), likelihood ratio tests with a between‐within denominator degree of freedom have type I error rates close to the nominal level. When the number of covariates is moderate (≥$$ \ge $$5), across our simulation scenarios, the relative performance of the tests varied considerably and no method performed uniformly well. Therefore, we recommend adjusting for no more than a few covariates and using likelihood ratio tests with a between‐within denominator degree of freedom. [ABSTRACT FROM AUTHOR]

Many individually randomized group treatment (IRGT) trials randomly assign individuals to study arms but deliver treatments via shared agents, such as therapists, surgeons, or trainers. Post‐randomization interactions induce correlations in outcome measures between participants sharing the same agent. Agents can be nested in or crossed with trial arm, and participants may interact with a single agent or with multiple agents. These complications have led to ambiguity in choice of models but there have been no systematic efforts to identify appropriate analytic models for these study designs. To address this gap, we undertook a simulation study to examine the performance of candidate analytic models in the presence of complex clustering arising from multiple membership, single membership, and single agent settings, in both nested and crossed designs and for a continuous outcome. With nested designs, substantial type I error rate inflation was observed when analytic models did not account for multiple membership and when analytic model weights characterizing the association with multiple agents did not match the data generating mechanism. Conversely, analytic models for crossed designs generally maintained nominal type I error rates unless there was notable imbalance in the number of participants that interact with each agent. [ABSTRACT FROM AUTHOR]