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Sample size for partially nested designs and other nested or crossed designs with a continuous outcome when adjusted
Steven Teerenstra1, Jessica Kasza2, Ruslan Leontjevas3,4
1Department for Health Evidence, Section Biostatistics, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.
Statistics in Medicine
|June 22, 2023
Summary
Adjusting for baseline measurements in randomized controlled trials significantly reduces the required sample size. This optimization is crucial for efficient trial design and analysis, especially with correlated follow-up data.
Area of Science:
- Biostatistics
- Clinical Trial Design
- Health Services Research
Background:
- Randomized controlled trials (RCTs) often involve baseline and follow-up measurements.
- Treatment can induce correlation in follow-up outcomes due to group or therapist effects.
- Existing literature focuses on analyses using only follow-up data.
Purpose of the Study:
- To compare sample size requirements for RCTs with and without baseline adjustment.
- To develop sample size formulas incorporating baseline-follow-up correlations.
- To explore practical design considerations for partially nested designs.
Main Methods:
- Comparative analysis of sample size requirements.
- Derivation of sample size formulas for various designs (nested, cluster RCTs).
- Investigation of factors influencing sample size reduction (variance, correlation).
Main Results:
- Adjusting for baseline measurements reduces the required sample size in RCTs.
- Sample size reduction is dependent on baseline and follow-up variances and their correlation.
- New formulas are provided for partially/fully nested and cluster randomized trials.
Conclusions:
- Baseline adjustment is an effective strategy for optimizing sample size in RCTs.
- The derived formulas aid in efficient trial planning for complex designs.
- Practical design considerations are discussed for partially nested trials.
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