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

TL;DR

This study compares simulation-based and traditional power calculations for complex cluster-randomized trials with covariate-constrained randomization, demonstrating that simulations provide more accurate planning insights in nested data structures.

Contribution

It introduces a simulation approach for planning covariate-constrained cluster trials, highlighting its advantages over conventional formulas in complex nested settings.

Findings

Simulation-based planning matched the trial design accurately.

Conventional calculations underestimated power and showed power plateauing.

Power increased with cluster size in simulations, unlike in traditional calculations.

Abstract

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.