Version 1
: Received: 24 December 2023 / Approved: 25 December 2023 / Online: 27 December 2023 (05:36:37 CET)
How to cite:
Yoseph, A. How to Design and Analyze Efficient Cluster Randomized Controlled Trials: A Review and Recent Development in Low and Middle-Income Countries. Preprints2023, 2023121826. https://doi.org/10.20944/preprints202312.1826.v1
Yoseph, A. How to Design and Analyze Efficient Cluster Randomized Controlled Trials: A Review and Recent Development in Low and Middle-Income Countries. Preprints 2023, 2023121826. https://doi.org/10.20944/preprints202312.1826.v1
Yoseph, A. How to Design and Analyze Efficient Cluster Randomized Controlled Trials: A Review and Recent Development in Low and Middle-Income Countries. Preprints2023, 2023121826. https://doi.org/10.20944/preprints202312.1826.v1
APA Style
Yoseph, A. (2023). How to Design and Analyze Efficient Cluster Randomized Controlled Trials: A Review and Recent Development in Low and Middle-Income Countries. Preprints. https://doi.org/10.20944/preprints202312.1826.v1
Chicago/Turabian Style
Yoseph, A. 2023 "How to Design and Analyze Efficient Cluster Randomized Controlled Trials: A Review and Recent Development in Low and Middle-Income Countries" Preprints. https://doi.org/10.20944/preprints202312.1826.v1
Abstract
Abstract Background: Cluster randomized controlled trials are increasingly becoming popular in public health research interventions and health services research in primary health care design and use in low and middle-income countries. However, the majority of researchers in these trials did not account correctly for the effect of confounders and clustering in the design and analysis stages. Objectives: This article aimed to review the main implications of implementing a cluster randomized controlled trials design in public health research and emphasize the practical application of proper analytical methods and the performance of different statistical models. Methods: The practical application of various analytical methods is validated via the utilization of experimental data from a public health research case study. This article also demonstrated the performance of hierarchical models and made comparisons in terms of empirically precise effect size estimates. Results: Three hierarchical statistical models were provided with highly varied effect sizes during the analysis of the intervention effect on outcome, such as model 1(AOR: 14.32; 95% CI: 3.75–54.68), model 2(ARR: 3.52; 95% CI: 2.16–5.77), and model 3 (ARR: 4.79; 95% CI: 2.69–5.52).Conclusion: Inaccurate results and potentially misleading conclusions can arise from improper cluster randomized controlled trial designs and analyses. This article has shown that real-world data can be adjusted for the effects of confounders and clustering, and we advocate for the routine use of suitable design and analytical methods.This article concludes that researchers should be careful when selecting hierarchical models to analyze cluster randomized controlled trials.
Keywords
cluster randomized controlled trial; variance inflation factor; interclass correlation; multi-level analysis; public health research; effect modification
Subject
Public Health and Healthcare, Public Health and Health Services
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
