ArticleStatistics in medicine2026
Estimating Marginal Odds Ratios for Cluster Randomized Trials: The Cluster-Level GLM.
Article in Statistics in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
backgroundMethods for estimating marginal effects in cluster randomized trials are limited. We describe and evaluate a cluster-level generalized linear model (CL-GLM) that applies a GLM to cluster-level summaries (means, proportions, or rates) and can estimate differences, ratios, or odds ratios. We focus on odds ratios.
methodsWe conducted a simulation study with 10-50 clusters, intracluster correlation coefficients of 0.05 or 0.15, and a mean cluster size of 120, either fixed or variable. Informative cluster size, where the intervention effect varies with cluster size, was also considered. Control arm prevalence was set to 50%. We compared cluster-level GLMs with normal and gamma distributions and logit link to generalized estimating equations (GEE) and independence estimating equations (IEEs). Weighted and unweighted analyses were examined to target cluster-average and individual-average estimands.
resultsCL-GLM with a normal distribution produced unbiased marginal cluster-average odds ratios with appropriate confidence interval coverage. CL-GLM with a gamma distribution resulted in over-coverage. Unweighted IEE produced unbiased marginal individual-average odds ratios with correct coverage. GEE was unbiased when cluster size was uninformative but biased under informative cluster size. Weighted CL-GLM led to under-coverage, whereas weighted IEE resulted in over-coverage. The CL-GLM had similar power to GEE and unweighted IEE.
conclusionsThe cluster-level GLM with a normal distribution provides a robust alternative to estimating equation approaches for marginal odds ratios in cluster randomized trials, with good performance under both informative and uninformative cluster size and no loss of power. We recommend the cluster-level GLM with a normal distribution when the marginal cluster-average estimand is of interest, and unweighted IEEs when the marginal individual-average estimand is of interest.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.