Abstract
In cluster randomized trials, individuals from the same cluster tend to have more similar outcomes than individuals from different clusters. This correlation must be taken into account in the analysis of every cluster trial to avoid incorrect inferences. In this paper, we describe the principles guiding the analysis of cluster trials including how to correctly account for intra-cluster correlations as well as how to analyze more advanced designs such as stepped-wedge and cluster cross-over trials. We then describe how to handle specific issues such as small sample sizes and missing data.
| Original language | English |
|---|---|
| Article number | 202196 |
| Number of pages | 5 |
| Journal | Journal of Epidemiology and Population Health |
| Volume | 72 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Feb 2024 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
-
SDG 3 Good Health and Well-being
Keywords
- Analysis
- Cluster crossover trials
- Cluster randomized trials
- Stepped-wedge trials
Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver