Sample Size Calculation and Power Analysis of Time-Averaged Difference
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Little research has been done on sample size and power analysis under repeated measures design. With detailed derivation, we have shown sample size calculation and power analysis equations for timeaveraged difference to allow unequal sample sizes between two groups for both continuous and binary measures and explored the relative importance of number of unique subjects and number of repeated measurements within each subject on statistical power through simulation.
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The power estimation indicated that 40 patients in each group were needed to obtain an 80% chance of detecting differences between the two groups.
“For a design with two repeated measurements, an average 0.2 point increase, a standard deviation of 0.3, a p-level of 5% and two-tailed tests, 40 patients in each group were needed to obtain an 80% chance of detecting differences between the two groups.”
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- Authors
- Liu, H., & Wu, T.
- Affiliations
- University of California, Los Angeles (US)
- Venue
- Journal of Modern Applied Statistical Methods
- Publisher
- Scilight Press Pty Ltd
- Published
- 2005-11-01
- Volume
- 4
- Issue
- 2
- Pages
- 434-445
- ISSN
- 1538-9472 (ISSN-L)
- OpenAlex
- W2587104197
- Cited by
- 60
- References
- 13
- Citations by year
- 2025: 5 · 2024: 5 · 2023: 4 · 2022: 4 · 2021: 3 · 2020: 4 · 2019: 8 · 2018: 3 · 2017: 3 · 2016: 4 · 2015: 4 · 2014: 2 · 2013: 1 · 2012: 4
- FWCI
- 0.35
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Journal of Modern Applied Statistical Methods (publishedVersion)
- Human Biology (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- sample size determination, statistical power analysis, repeated measures designs, unequal sample sizes, binary outcomes, continuous outcomes
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