This package is to help researchers design cost-efficient experimental studies assessing main treatment effects with adequate statistical precision by (a) solving optimal sample allocations, (b) comparing design precision and efficiency between different sample allocations, and (c) explicitly accommodating costs and budget in power analyses.
The package covers seven types of experiments aiming to detect main effects on continuous outcomes. These experiments are individual randomized controlled trials (RCTs), two-, three-, and four-level cluster-randomized trials (CRTs), and two-, three-, and four-level multisite randomized trials (MRTs). There are two categorical functions for each type of experiments and a uniform function for all types of experiments. The two categorical functions are 'od' and 'power'. The 'od' function can calculate the optimal sample allocation with and without constraint for each type of experiments. The 'power' function by default can calculate required budget (and required sample size) for desired power, minimum detectable effect size (MDES) under a fixed budget, statistical power under a fixed budget. The 'power' function also can perform conventional power analyses (e.g., required sample size, power, MDES calculation). The uniform function 're' (or 'rpe') is to compare the relative (precision and) efficiency between two designs with different sample allocations.
Zuchao Shen, Ben Kelcey
Maintainer: Zuchao Shen email@example.com
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