Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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A statistical analysis of the fat mass repeated measures data using mixed model

혼합모형을 이용한 체지방 반복측정자료에 대한 통계적 분석

  • Jo, Jinnam (Department of Statistics and Information Science, Dongduk Women's University) ;
  • Chang, Un Jae (Department of Food and Nutrition, Dongduk Women's University)
  • 조진남 (동덕여자대학교 정보통계학과) ;
  • 장은재 (동덕여자대학교 식품영양학과)
  • Received : 2013.02.08
  • Accepted : 2013.03.12
  • Published : 2013.03.31
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Abstract

Forty two female students whose fat mass ratio was over 30% were participated in the experiment of fat mass loss of two treatments for 8 weeks. They kept diary for foods they ate every day, took a picture of the foods, transmitted the picture to the experimenter by the camera phone. Among those, 28 students took the picture by regular camera phone (Treatment A), and the other students used smart phone (Treatment B). Fat mass weight and its related variables had been measured repeatedly four times at an interval of two weeks during 8 weeks. It was shown from mixed model analysis of repeated measurements data that AR(1) covariance matrix was selected as the optimal covariance matrix pattern. The correlation between two successive times is highly correlated as 0.838. Based upon the AR(1) covariance matrix structure, the students using smart phones were somewhat more effective in losing fat mass weight than the students using regular camera phones. The time effect was highly significant, but the treatment-time interaction effect was insignificant. The baseline effect and total cholesterol were found to be significant, but the calories with taking foods were somewhat significant, but the waist to hip ratio was found to be insignificant.

체지방 감량에 대한 효과를 분석하고자 실험에 참가한 체지방율이 30% 이상인 42명의 여대생을 대상으로 일반폰을 사용하는 그룹과 스마트폰을 사용하는 그룹으로 나누어서 측정자료를 2주 간격으로 정리하여 8주간에 걸친 체지방 및 관련자료를 얻었다. 이 실험자료를 바탕으로 혼합모형을 이용하여 분석한 결과 AR(1)의 공분산행렬이 가장 적합한 모형으로 선택되었으며, 시점 간의 상관계수는 0.838로 상당히 밀접한 관련을 보여주었다. AR(1)의 공분산행렬을 설정하여 분석한 결과 처리간의 차이에서 스마트폰의 사용자가 일반폰의 사용자보다 0.654kg 정도의 체지방 감량 효과를 보여주었으며, 시간이 지날수록 체지방 감소효과가 있음을 알 수 있다. 그러나 처리와 시간과의 교호작용은 존재하지 않는다. 또한 실험실시 전의 체지방값과 총콜레스테롤은 유의하게 나타났으며, 섭취하는 칼로리는 약간 관련이 있으나, 허리엉덩이비율은 유의하지 않는 것으로 판명되었다.

Keywords

References

  1. Brown, H. and Kempton, R. A. (1994). The application of REML in clinical trials. Statistics in Medicine, 16, 1601-1617.
  2. Brown, H. and Prescott, R. (1999). Applied mixed models in medicine, John Wiley & Sons Inc., New York.
  3. Choi, J. (2008). A marginal logit mixed-effects model for repeated binary response data. Journal of the Korean Data & Information Science Society, 19, 413-420.
  4. Diggle, P. J. (1989). Testing for random dropouts in repeated measurement data. Biometrics, 43, 1255-1258.
  5. Fitzmaurice, G. M., Laird, N. M. and Ware, J. H. (2004). Applied longitudinal analysis, John Wiley & Sons Inc., New York.
  6. Frees, E. D. (2006). Longitudinal and panel data, Cambridge University Press, New York.
  7. Hand, D. and Crowder, M. (1996). Practical longitudinal data analysis, Chapman & Hall, London.
  8. Hay, C. R. M., Ludlam, C. A., Lowe, G. D. O., Mayne, E. E., Lee, R. J., Prescott, R. J. and Lee, C. A. (1998). The effect of monoclonal or ion-exchange purified factor VIII concentrate on HIV disease progression: A prospective cohort comparison. British Journal of Haematology, 101, 632-637. https://doi.org/10.1046/j.1365-2141.1998.00753.x
  9. Hinkelman, K. and Kempthorne, O. (1994). Design and analysis of experiments, Vol. 1, John Wiley & Sons Inc., New York.
  10. Jo, J. (2009). An empirical study on the selection of the optimal covariance pattern model for the weight loss data. Journal of the Korean Data & Information Science Society, 20, 377-385.
  11. Jo, J. (2011). A statistical analysis of the fat mass experimental data using random coefficient model. Journal of the Korean Data & Information Science Society, 22, 287-296.
  12. Jo, J. and Baik, J. W. (2010). A statistical analysis on the selection of the optimal covariance matrix pattern for the cholesterol data. Journal of the Korean Data & Information Science Society, 21, 1263-1270.
  13. Littell, R. C., Milliken, G. A., Stroup, W. W. and Wolfinger, R. D. (1996). SAS system for mixed models, SAS Institute Inc., N.C..
  14. Longford, N. T. (1993). Random coefficient models, Oxford University Press, Oxford.
  15. Satterthwaite, F. E. (1946). An approximate distribution of estimates of variance components. Biometrics Bulletin, 2, 110-114. https://doi.org/10.2307/3002019
  16. Smyth, J. F., Brown, A., Perren, T., Wilkinson, P., Prescott, R. J., Quinn, K. J. and Tedeschi, M. (1997). Glutathione reduces the toxicity and improves quality of the life of women diagnosed with ovarian cancer treated with cisplatin: Results of a double blind, randomized trial. Annals of Oncology, 8, 569-5873. https://doi.org/10.1023/A:1008211226339
  17. Verbeke, G. and Molenberghs, G. (2000). Linear mixed models for longitudinal data, Springer Verlag, New York.

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