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English Abstracts
Power Estimation and Follow-Up Period Evaluation in Korea Radiation Effect and Epidemiology Cohort Study.
In Seong Cho, Minkyo Song, Yunhee Choi, Zhong Min Li, Yoon Ok Ahn
J Prev Med Public Health. 2010;43(6):543-548.
DOI: https://doi.org/10.3961/jpmph.2010.43.6.543
  • 5,200 View
  • 72 Download
  • 2 Crossref
AbstractAbstract PDF
OBJECTIVES
The objective of this study was to calculate sample size and power in an ongoing cohort, Korea radiation effect and epidemiology cohort (KREEC). METHOD: Sample size calculation was performed using PASS 2002 based on Cox regression and Poisson regression models. Person-year was calculated by using data from '1993-1997 Total cancer incidence by sex and age, Seoul' and Korean statistical informative service. RESULTS: With the assumption of relative risk=1.3, exposure:non-exposure=1:2 and power=0.8, sample size calculation was 405 events based on a Cox regression model. When the relative risk was assumed to be 1.5 then number of events was 170. Based on a Poisson regression model, relative risk=1.3, exposure:non-exposure=1:2 and power=0.8 rendered 385 events. Relative risk of 1.5 resulted in a total of 157 events. We calculated person-years (PY) with event numbers and cancer incidence rate in the non-exposure group. Based on a Cox regression model, with relative risk=1.3, exposure:non-exposure=1:2 and power=0.8, 136 245PY was needed to secure the power. In a Poisson regression model, with relative risk=1.3, exposure:non-exposure=1:2 and power=0.8, person-year needed was 129517PY. A total of 1939 cases were identified in KREEC until December 2007. CONCLUSIONS: A retrospective power calculation in an ongoing study might be biased by the data. Prospective power calculation should be carried out based on various assumptions prior to the study.
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  • Comparative Analysis of Driver Mutations and Transcriptomes in Papillary Thyroid Cancer by Region of Residence in South Korea
    Jandee Lee, Seonhyang Jeong, Hwa Young Lee, Sunmi Park, Meesson Jeong, Young Suk Jo
    Endocrinology and Metabolism.2023; 38(6): 720.     CrossRef
  • Cancer Risk in Adult Residents near Nuclear Power Plants in Korea - A Cohort Study of 1992-2010
    Yoon-Ok Ahn, Zhong Min Li
    Journal of Korean Medical Science.2012; 27(9): 999.     CrossRef
A Review of Power and Sample Size Estimation in Genomewide Association Studies.
Ae Kyung Park, Ho Kim
J Prev Med Public Health. 2007;40(2):114-121.
DOI: https://doi.org/10.3961/jpmph.2007.40.2.114
  • 4,549 View
  • 74 Download
  • 2 Crossref
AbstractAbstract PDF
Power and sample size estimation is one of the crucially important steps in planning a genetic association study to achieve the ultimate goal, identifying candidate genes for disease susceptibility, by designing the study in such a way as to maximize the success possibility and minimize the cost. Here we review the optimal two-stage genotyping designs for genomewide association studies recently investigated by Wang et al(2006). We review two mathematical frameworks most commonly used to compute power in genetic association studies prior to the main study: Monte-Carlo and non-central chi-square estimates. Statistical powers are computed by these two approaches for case-control genotypic tests under one-stage direct association study design. Then we discuss how the linkagedisequilibrium strength affects power and sample size, and how to use empirically-derived distributions of important parameters for power calculations. We provide useful information on publicly available softwares developed to compute power and sample size for various study designs.
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Citations

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  • Sample Size and Statistical Power Calculation in Genetic Association Studies
    Eun Pyo Hong, Ji Wan Park
    Genomics & Informatics.2012; 10(2): 117.     CrossRef
  • The Effect of Increasing Control-to-case Ratio on Statistical Power in a Simulated Case-control SNP Association Study
    Moon-Su Kang, Sun-Hee Choi, In-Song Koh
    Genomics & Informatics.2009; 7(3): 148.     CrossRef

JPMPH : Journal of Preventive Medicine and Public Health