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Inference for the treatment effect in staircase designs with continuous outcomes: a simulation study
Ehsan Rezaei-Darzi1, Kelsey L Grantham1, Andrew B Forbes1
1School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
BMC Medical Research Methodology
|May 10, 2025
Summary
Staircase designs require careful use, especially with few clusters. Correctly specifying the correlation structure is crucial for accurate treatment effect inference in clinical trials.
Area of Science:
- Clinical Trial Design
- Statistical Methodology
- Health Services Research
Background:
- Staircase designs are a type of incomplete stepped wedge design.
- They differ from standard designs by requiring fewer data periods per cluster.
- Existing formulas for power calculations are based on asymptotic results.
Purpose of the Study:
- To evaluate the finite sample performance of existing power calculation formulas for staircase designs.
- To assess the impact of misspecifying the correlation structure on treatment effect inference.
- To examine performance under various realistic trial settings.
Main Methods:
- Simulation study of basic staircase designs with continuous outcomes.
- Analysis of data under exchangeable and block-exchangeable intracluster correlation structures.
- Use of linear mixed models with small-sample corrections (Kenward-Roger, Satterthwaite).
Main Results:
- The Satterthwaite correction with an exchangeable structure controls Type I error well with sufficient clusters.
- Misspecifying the correlation structure (exchangeable vs. block-exchangeable) can inflate Type I error and reduce confidence interval coverage.
- Inference is reliable with sufficient clusters, but caution is advised for designs with one cluster per sequence.
Conclusions:
- Staircase designs with only one cluster per sequence warrant cautious application.
- Employing a correlation structure that accounts for decay is recommended for valid treatment effect estimation.
- Accurate modeling of the correlation structure is essential for reliable statistical inference.
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