Optimal Design of Cluster- and Multisite-Randomized Studies Using Fallible Outcome Measures.

Area of Science:

• Evaluation methodology
• Statistical power analysis
• Measurement error in research

Background:

• Evaluation studies often rely on outcomes with significant measurement error.
• Ignoring this error during study planning can compromise study sufficiency and efficiency.
• This can limit the evidence generated on program effectiveness.

Purpose of the Study:

• To develop formulas adjusting for outcome measurement error in randomized designs.
• To refine calculations for statistical power, minimum detectable effect (MDE), and sample allocation.
• To address two-level cluster and multisite randomized designs with fallible outcomes.

Main Methods:

• Development of simple formulas to adjust standard evaluation planning metrics.
• Application to two-level cluster-randomized and multisite-randomized designs.
• Implementation of adjusted formulas in R package PowerUpR and PowerUp software.

Main Results:

• Outcome measurement error typically increases uncertainty and reduces statistical power.
• Measurement error necessitates larger sample sizes for adequate power.
• Conventional optimal sampling schemes are undermined; design efficiency requires more sampling within clusters.

Conclusions:

• Adjusted formulas are crucial for accurate evaluation planning with fallible outcomes.
• Accounting for measurement error ensures more reliable evidence on program effectiveness.
• The developed tools (PowerUpR, PowerUp) facilitate the adoption of these adjusted formulas.