Statistical Power for Incremental Cost-Effectiveness Ratios Based on Monte Carlo Intervals in (Multi-Site) Randomized Trials (Poster 10)

• In Event: Innovations in Statistical Power, Intervention Planning and Design, and Causal Effects

Abstract

A critical aspect to consider in designing cost-effectiveness analysis (CEA) studies is statistical power analysis. This analysis is essential for determining the required sample sizes at each level to ensure adequate power to detect a program's cost-effectiveness. This paper aims to: (1) develop and validate methods for statistical power analyses of incremental cost-effectiveness ratio (ICER) based on the Monte Carlo Confidence Interval method in randomized controlled trials (RCTs) and multi-site individual randomized trials (MIRTs), and (2) create accessible and user-friendly software programs in Excel, SAS, R, and R Shiny web applications, aiding researchers in education in designing cost-effectiveness studies. We presents our simulation results for validating the efficacy of the procedures and demonstrate the software for power calculation of ICER.

Authors

• Nianbo Dong, University of North Carolina - Chapel Hill

• Wei Li, University of Florida

• Benjamin Kelcey, University of Cincinnati