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Reliability and Data Integration of Duplicated Test Results Using Two Bioelectrical Impedence Analysis Machines in the Korean Genome and Epidemiology Study

  • Park, Bo-Young (Department of Preventive Medicine, Seoul National University College of Medicine) ;
  • Yang, Jae-Jeong (Department of Preventive Medicine, Seoul National University College of Medicine) ;
  • Yang, Ji-Hyun (Department of Preventive Medicine, Seoul National University College of Medicine) ;
  • Kim, Ji-Min (Department of Preventive Medicine, Seoul National University College of Medicine) ;
  • Cho, Lisa-Y. (Department of Preventive Medicine, Seoul National University College of Medicine) ;
  • Kang, Dae-Hee (Department of Preventive Medicine, Seoul National University College of Medicine) ;
  • Shin, Chol (Department of Internal Medicine, Korea University College of Medicine) ;
  • Hong, Young-Seoub (Department of Preventive Medicine, Dong-A University College of Medicine) ;
  • Choi, Bo-Youl (Department of Preventive Medicine, Hanyang University College of Medicine) ;
  • Kim, Sung-Soo (Center for Genome Science, National Health Institute, Korea Centers for Disease Control and Prevention) ;
  • Park, Man-Suck (Center for Genome Science, National Health Institute, Korea Centers for Disease Control and Prevention) ;
  • Park, Sue-K. (Department of Preventive Medicine, Seoul National University College of Medicine)
  • Received : 2010.06.07
  • Accepted : 2010.09.09
  • Published : 2010.11.30

Abstract

Objectives: The Korean Genome and Epidemiology Study (KoGES), a multicenter-based multi-cohort study, has collected information on body composition using two different bioelectrical impedence analysis (BIA) machines. The aim of the study was to evaluate the possibility of whether the test values measured from different BIA machines can be integrated through statistical adjustment algorithm under excellent inter-rater reliability. Methods: We selected two centers to measure inter-rater reliability of the two BIA machines. We set up the two machines side by side and measured subjects' body compositions between October and December 2007. Duplicated test values of 848 subjects were collected. Pearson and intra-class correlation coefficients for inter-rater reliability were estimated using results from the two machines. To detect the feasibility for data integration, we constructed statistical compensation models using linear regression models with residual analysis and R-square values. Results: All correlation coefficients indicated excellent reliability except mineral mass. However, models using only duplicated body composition values for data integration were not feasible due to relatively low $R^2$ values of 0.8 for mineral mass and target weight. To integrate body composition data, models adjusted for four empirical variables that were age, sex, weight and height were most ideal (all $R^2$ > 0.9). Conclusions: The test values measured with the two BIA machines in the KoGES have excellent reliability for the nine body composition values. Based on reliability, values can be integrated through algorithmic statistical adjustment using regression equations that includes age, sex, weight, and height.

Keywords

References

  1. Center for Genomic Science, National Institute of Health, KCDC. Korean Genome and Epidemiology Study (KoGES). [cited 2010 Sept 5]. Available from: http://www.nih.go.kr/bio/koges/a_a_a.jsp.
  2. Yoo KY, Shin HR, Chang SH, Choi BY, Hong YC, Kim DH, et al. Genomic epidemiology cohorts in Korea: present and the future. Asian Pac J Cancer Prev 2005; 6(3): 238-243.
  3. Gassman JJ, Owen WW, Kuntz TE, Martin JP, Amoroso WP. Data quality assurance, monitoring, and reporting. Control Clin Trials 1995; 16(2 Suppl): 104S-136S.
  4. The Korean Society for Preventive Medicine. Preventive Medicine and Public Health. Seoul: Gyechuk-Munhwasa; 2010, p. 83-86. (Korean).
  5. Ahn YO, Yoo KY, Park BJ. Manual for Medical Statistics. Seoul: Seoul National University Press; 2005, p. 105-113. (Korean).
  6. Altman DG. Practical Statistics for Medical Research. London: Chapman & Hall.; 1992. p. 409-419.
  7. Dehghan M, Merchant AT. Is bioelectrical impedance accurate for use in large epidemiological studies? Nutr J 2008; 7: 26. https://doi.org/10.1186/1475-2891-7-26
  8. Biau DJ, Halm JA, Ahmadieh H, Capello WN, Jeekel J, Boutron I, et al. Provider and center effect in multicenter randomized controlled trials of surgical specialties: an analysis on patient-level data. Ann Surg 2008; 247(5): 892-898. https://doi.org/10.1097/SLA.0b013e31816ffa99
  9. Gordis L. Epidemiology, 3rd ed. Pennsylvenia: Elsevier Saunders; 2004. p. 87-88.
  10. Bangdiwala SI, de Paula CS, Ramiro LS, Munoz SR. Coordination of international multicenter studies: governance and administrative structure. Salud Publica Mex 2003; 45(1): 58-66. https://doi.org/10.1590/S0036-36342003000100008

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