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How many repeated measures in repeated measures designs? Statistical issues for comparative trials
1Integrative Medicine Service, Biostatistics Service, Memorial Sloan Kettering Cancer Center, Howard 13, 1275 York Avenue NY, NY 10021, USA. vickersa@mskcc.org
BMC Medical Research Methodology
|October 29, 2003
Summary
Determining the optimal number of repeated measures in clinical trials is crucial for statistical power. This study provides a method to balance the benefits of additional assessments against diminishing returns, guiding researchers on when to stop collecting data.
Area of Science:
- Biostatistics
- Clinical Trial Design
- Statistical Power Analysis
Background:
- Researchers frequently use repeated measures in clinical trials to reduce intra-patient variability and enhance statistical power.
- Limited guidance exists on selecting the optimal number of repeated measures in study designs.
Purpose of the Study:
- To provide a rational method for determining the optimal number of repeated measures in comparative trials.
- To inform researchers on the diminishing marginal benefit of additional assessments.
Main Methods:
- Utilizing simple formulas to calculate the marginal benefit of adding further repeated measures.
- Applying these calculations to inform the optimal number of repeat assessments.
Main Results:
- The statistical power gains from repeated assessments diminish rapidly as the number of measures increases.
- Generally, more than four baseline or post-treatment assessments offer little additional value, with up to seven acceptable if baseline measures are included.
- Exceptions exist for conditions with low intra-measure correlations, such as episodic pain.
Conclusions:
- A rational framework is proposed for determining the number of repeat measures in study designs.
- This method aids in optimizing statistical power while avoiding unnecessary data collection.