Comparison of Simulation-Guided Design to Closed-Form Power Calculations in Planning a Cluster Randomized Trial with Covariate-Constrained Randomization: A Case Study in Rural Chad
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| Format: | Preprint |
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2025
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| author | Park, Jay JH Metcalfe, Rebecca K. Dyrkton, Nathaniel Yan, Yichen Alam, Shomoita Phelan, Kevin Sana, Ibrahim Shepherd, Susan |
| author_facet | Park, Jay JH Metcalfe, Rebecca K. Dyrkton, Nathaniel Yan, Yichen Alam, Shomoita Phelan, Kevin Sana, Ibrahim Shepherd, Susan |
| contents | Current practices for designing cluster-randomized trials (cRCTs) typically rely on closed-form formulas for power calculations. For cRCTs using covariate-constrained randomization, the utility of conventional calculations might be limited, particularly when data is nested. We compared simulation-based planning of a nested cRCT using covariate-constrained randomization to conventional power calculations using OptiMAx-Chad as a case study. OptiMAx-Chad will examine the impact of embedding mass distribution of small-quantity lipid-based nutrient supplements within an expanded programme on immunization on first-dose measles-containing vaccine (MCV1) coverage among children aged 12-24 months in rural villages in Ngouri. Within the 12 health areas to be randomized, a random subset of villages will be selected for outcome collection. 1,000,000 assignments of health areas with different possible village selections were generated using covariate-constrained randomization to balance baseline village characteristics. The empirically estimated intracluster correlation coefficient (ICC) and the World Health Organization (WHO) recommended values of 1/3 and 1/6 were considered. The desired operating characteristics were 80% power at 0.05 one-sided type I error rate. Using conventional calculations target power for a realistic treatment effect could not be achieved with the WHO recommended values. Conventional calculations also showed a plateau in power after a certain cluster size. Our simulations matched the design of OptiMAx-Chad with covariate adjustment and random selection, and showed that power did not plateau. Instead, power increased with increasing cluster size. Planning complex cRCTs with covariate constrained randomization and a multi-nested data structure with conventional closed-form formulas can be misleading. Simulations can improve the planning of cRCTs. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_18818 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Comparison of Simulation-Guided Design to Closed-Form Power Calculations in Planning a Cluster Randomized Trial with Covariate-Constrained Randomization: A Case Study in Rural Chad Park, Jay JH Metcalfe, Rebecca K. Dyrkton, Nathaniel Yan, Yichen Alam, Shomoita Phelan, Kevin Sana, Ibrahim Shepherd, Susan Applications Methodology Current practices for designing cluster-randomized trials (cRCTs) typically rely on closed-form formulas for power calculations. For cRCTs using covariate-constrained randomization, the utility of conventional calculations might be limited, particularly when data is nested. We compared simulation-based planning of a nested cRCT using covariate-constrained randomization to conventional power calculations using OptiMAx-Chad as a case study. OptiMAx-Chad will examine the impact of embedding mass distribution of small-quantity lipid-based nutrient supplements within an expanded programme on immunization on first-dose measles-containing vaccine (MCV1) coverage among children aged 12-24 months in rural villages in Ngouri. Within the 12 health areas to be randomized, a random subset of villages will be selected for outcome collection. 1,000,000 assignments of health areas with different possible village selections were generated using covariate-constrained randomization to balance baseline village characteristics. The empirically estimated intracluster correlation coefficient (ICC) and the World Health Organization (WHO) recommended values of 1/3 and 1/6 were considered. The desired operating characteristics were 80% power at 0.05 one-sided type I error rate. Using conventional calculations target power for a realistic treatment effect could not be achieved with the WHO recommended values. Conventional calculations also showed a plateau in power after a certain cluster size. Our simulations matched the design of OptiMAx-Chad with covariate adjustment and random selection, and showed that power did not plateau. Instead, power increased with increasing cluster size. Planning complex cRCTs with covariate constrained randomization and a multi-nested data structure with conventional closed-form formulas can be misleading. Simulations can improve the planning of cRCTs. |
| title | Comparison of Simulation-Guided Design to Closed-Form Power Calculations in Planning a Cluster Randomized Trial with Covariate-Constrained Randomization: A Case Study in Rural Chad |
| topic | Applications Methodology |
| url | https://arxiv.org/abs/2510.18818 |