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Time-varying glucose factor: A new variable assessing the long-term average glucose level |
Zhao Chunhua1, Ma Qinghua2, Sun Hongpeng1 |
1 School of Public Health, Medical College of Soochow University, Suzhou 215123, China; 2 The Third Hospital of Suzhou Xiangcheng |
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Abstract Objective To create a new variable (time-varying glucose factor) to assess the long-term average glucose level through integrating multiple measuring data which contain the blood glucose and hemoglobin AlC. Methods Bayesian hierarchical latent variable model could make full use of the correlation between indicators. With the relative variation of each variable considered when generating aggregative indicator, in virtue of the information between individuals, it would give a stable estimation for individuals with fewer data. Results We estimated the time-varying glucose factor using the simulation data. The correlation coefficient between factor and average blood sugar was higher than the index between long-term blood sugar and average blood sugar (0.92 VS 0.58). Conclusion We successfully built a time-varying glucose factor with the blood glucose and hemoglobin AlC. And it could reflect the long-term blood sugar level well.
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Received: 12 September 2016
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[1] RAHMAN S, RAHMAN T, ISMAIL AA, et al. Diabetes-associated macrovasculopathy: pathophysiology and pathogenesis[J]. Diabetes Obes Metab, 2007,9(6):767-780. [2] PITOCCO D, TESAURO M, ALESSANDRO R, et al. Oxidative stress in diabetes: implications for vascular and other complications[J]. Int J Mol Sci, 2013,14(11):21525-21550. [3] BELIAEVA MI. Modern views of causes of microvascular complications development and progression in type 2 diabetes mellitus and peculiarities of their treatment[J]. Vestn Oftalmol, 2013,129(4):70-75. [4] ZASLAVSKY AM, SHAUL JA, ZABORSKI LB, et al. Combining health plan performance indicators into simpler composite measures[J]. Health Care Financ Rev, 2002,23(4):101-115. [5] BAHRAMI SAMANI E, GANJALI M. Bayesian latent variable model for mixed continuous and ordinal responses with possibility of missing responses[J]. Journal of Applied Statistics, 2011, 38(6):1103-1116. [6] DUNSON DB, HERRING AH. Bayesian latent variable models for mixed discrete outcomes[J]. Biostatistics, 2005,6(1):11-25. [7] LANDRUM MB, BRONSKILL SE, et al. Analytic Methods for Constructing Cross-Sectional Profiles of Health Care Providers[J]. Health Services & Outcomes Research Methodology, 2000,1(1):23-47. [8] GOLDSTEIN H.多水平统计模型[M].李晓松,译.成都:四川科学技术出版社,1999:2-48. |
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