Accurate and flexible power calculations on the spot: Applications to genomic research.
- Authors
- Tiwari, Hemant K; Birkner, Thomas; Moondan, Ankur; Zhang, Shiju; Page, Grier P; Patki, Amit; Allison, David B
- Year
- 2011
- Journal
- Statistics and its interface
- PMID
- 22022634
- DOI
- 10.4310/sii.2011.v4.n3.a9
- PMCID
- PMC3196559
Often investigators need to calculate power to demonstrate feasibility of proposed genetic studies for grant proposals or simply to aid in their own study planning. Frequently, power can be easily calculated using a closed form formula. However, in some situations such formulae for calculating power have not been derived and derivation on demand may be difficult if not impossible. In these situations investigators typically perform simulations specific to the study. Yet such simulations can be computationally extensive and take weeks to months depending on the circumstances. Here, we provide a simple method to rapidly estimate power when one has power estimates available for corresponding situations that differ from the situation of interest only in sample size and/or alpha (type I error) level desired. We show by application to multiple published results from the genomics field that these methods are generally very accurate and applicable to a broad range of genomic studies.
EEE power versus available simulated study power when the sample size of the available study is smaller than the sample size of the planned study with α level same for both studies.
LLM interpretation
This is a scatter plot comparing "EEE Power" (x-axis) against "Available study Power" (y-axis) for N = 540 data points. The data points cluster closely around a dashed line of perfect concordance and a solid reduced major axis line, indicating a strong positive correlation. The figure reports a Concordance Correlation Coefficient (CCC) of 0.9886 with a 95% confidence interval of 0.9865 - 0.9903.
EEE power versus available simulated study power when the sample size of the available study is larger than the sample size of the planned study with α level same for both studies.
LLM interpretation
This is a scatter plot comparing "EEE Power" (x-axis) against "Available study Power" (y-axis) for a sample size of N = 504. The data points closely follow both the dashed line of perfect concordance and the solid reduced major axis line, indicating a strong positive linear correlation. A Concordance Correlation Coefficient (CCC) of 0.9939 (95% CI 0.9927 - 0.9948) is provided to quantify the agreement between the two measures.
EEE power versus available simulated study power when α level changes from small to large, but sample size remain constant in both studies.
LLM interpretation
This scatter plot compares "EEE Power" (x-axis) against "Available study Power" (y-axis) for 208 data points. The data points closely follow both the dashed line of perfect concordance and the solid reduced major axis, indicating a strong positive linear correlation. The figure reports a Concordance Correlation Coefficient (CCC) of 0.9802 with a 95% confidence interval of 0.974 - 0.9849.
EEE power versus available simulated study power when the α level changes from large to small, but sample size remains constant in both studies.
LLM interpretation
This is a scatter plot comparing "EEE Power" (x-axis) against "Available study Power" (y-axis) for N = 208 data points. The data shows a strong positive linear correlation, closely following both the dashed "Line of perfect concordance" and the solid "Reduced major axis" regression line. A Concordance Correlation Coefficient (CCC) of 0.984 (95% CI 0.9791 - 0.9877) is provided, indicating high agreement between the two power measures.
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| Best (but oft forgotten) practices: sample size planning for powerful studies. | Anderson SF | — | 2019 | → |
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| Imputation across genotyping arrays for genome-wide association studies: assessment of bias and a correction strategy. | Johnson EO et al. | — | 2013 | → |