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An Application of Generalized Linear Model in Survival Analysis


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1 S.K. University of Agricultural Sciences and Technology of Kashmir, Shalimar, Srinagar (J&K), India
     

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Diabetes is a chronic, often debilitating and sometimes fatal disease, in which the body either cannot produce insulin or cannot properly use the insulin it produces. Type 1 diabetes occurs when the immune system mistakenly attacks and kills the beta cells of the pancreas. Type 2 diabetes occurs when the body can’t properly use the insulin that is released (called insulin insensitivity) or does not make enough insulin. Diabetic nephropathy, also known as Kimmelstiel Wilson syndrome or nodular diabetic glomerulosclerosis or intercapillary glomerulonephritis, is a clinical syndrome characterized by albuminuria (>300 mg/day or >200 mcg/min), permanent and irreversible decrease in glomerular filtration rate (GFR), the rate of rise in serum creatinine (SrCr). According to the WHO, 31.7 million people were affected by diabetes mellitus (DM) in India in the year 2000. This figure is estimated to rise to 79.4 million by 2030, the largest number in any nation in the world. In this paper, survival analysis will be done of the type 2 diabetic nephropathy patients through generalized linear model. Most appropriate distribution for duration of diabetes is selected through Bayesian information criterion value. Then two generalized linear models are fitted by taking the duration of diabetes as response variable and the predictors as SrCr, number of successes; GFR, number of successes, respectively. These covariates are linked with the response variable using different link functions. At the last, survival function under different links will be compared.

Keywords

Generalized Linear Model, Link Function, Bayesian Information Criterion, Survival Function, Diabetic Nephropathy, GFR.
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  • Akaike, H. (1973). Maximum likelihood identification of gaussian autoregressive moving average models. Biometrika, 255-265.

  • Akram, M., Ullah, M.A. and Taj, R. (2007). Survival analysis of cancer patients using parametric and non-parametric approaches. Pakistan Veterinary J., 27 : 194.

  • Cox, C., Chu, H., Schneider, M.F. and Muñoz, A. (2007). Parametric survival analysis and taxonomy of hazard functions for the Generalized Gamma Distribution. Statistics Med., 26 : 4352-4374.

  • Cox, J. and Mann, M. (2008).Maxquant enables high peptide identification rates, individualized Ppb-Range mass accuracies and proteome-wide protein quantification.Nature Biotechnol., 26 : 1367-1372.

  • Grover, G., Sabharwal, A.S.A. and Mittal, J. (2013). An application of gamma generalized linear model for estimation of survival function of diabetic nephropathy patients. Internat. J. Statistics Med. Res., 2 : 209-219.

  • Hakulinen, T. and Tenkanen, L. (1987). Regression analysis of relative survival rates. Appl. Statistics, 36 (3) : 309-317.

  • Hall, Phillip M. (2006). Mechanisms in Diabetic Nephropathy Prevention of Progression in Diabetic Nephropathy. Diabetes Spectrum, 19(1): 18-24.

  • Hurvich, C.M. and Tsai, C.L. (1989). Regression and time series model selection in small samples. Biometrika, 76 (2) : 297-307.

  • Karen, A. (2006).Application of the generalized linear model to the prediction of lung cancer survival. 2006; 1-18. http:// analytics.ncsu.edu/sesug/2006/ST09_06.PDF

  • Kass, R.E. and Raftery, A.E. (1995). Bayes factors. J. American Statistical Association, 90 : 773-795.

  • McCullagh, P. and Nelder, J.A. (1989). Generalized linear models, No. 37 in Monograph on Statistics and Applied Probability.”

  • Nelder, J.A. and Wedderburn, R.W.M. (1972). Generalized Linear Models. J. Royal Statistical Society. Series A (General), 135 (3) : 370-384.

  • Schwarz, G. (1978). Estimating the dimension of a model. The Ann. Statistics, 6 : 461-464.

  • Stroup, W.W. and Kachman, S.D. (1994). Generalized Linear Mixed Models-an Overview. Annual Conference on Applied Statistics in Agriculture

  • US Renal Data System and USRDS (2003). Annual Data Report; Atlas of end stage renal diseases, in the united states. Bethesda MD. National Institute of Health. National Instuitute of Diabetes, Digestive and Kidney Disease.

  • Yuan, X., Hong, D. and Shyr, Y. (2007). Survival model and estimation for lung cancer patients 2007; 201-22. http://capone.mtsu.edu/dhong/YuanHongShyr07.pdf.

  • World Health Organisation (2004). The diabetes program.


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  • An Application of Generalized Linear Model in Survival Analysis

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Authors

Yasmeena Ismail
S.K. University of Agricultural Sciences and Technology of Kashmir, Shalimar, Srinagar (J&K), India
S. A. Mir
S.K. University of Agricultural Sciences and Technology of Kashmir, Shalimar, Srinagar (J&K), India
M. A. Bhat
S.K. University of Agricultural Sciences and Technology of Kashmir, Shalimar, Srinagar (J&K), India
Nageena Nazir
S.K. University of Agricultural Sciences and Technology of Kashmir, Shalimar, Srinagar (J&K), India

Abstract


Diabetes is a chronic, often debilitating and sometimes fatal disease, in which the body either cannot produce insulin or cannot properly use the insulin it produces. Type 1 diabetes occurs when the immune system mistakenly attacks and kills the beta cells of the pancreas. Type 2 diabetes occurs when the body can’t properly use the insulin that is released (called insulin insensitivity) or does not make enough insulin. Diabetic nephropathy, also known as Kimmelstiel Wilson syndrome or nodular diabetic glomerulosclerosis or intercapillary glomerulonephritis, is a clinical syndrome characterized by albuminuria (>300 mg/day or >200 mcg/min), permanent and irreversible decrease in glomerular filtration rate (GFR), the rate of rise in serum creatinine (SrCr). According to the WHO, 31.7 million people were affected by diabetes mellitus (DM) in India in the year 2000. This figure is estimated to rise to 79.4 million by 2030, the largest number in any nation in the world. In this paper, survival analysis will be done of the type 2 diabetic nephropathy patients through generalized linear model. Most appropriate distribution for duration of diabetes is selected through Bayesian information criterion value. Then two generalized linear models are fitted by taking the duration of diabetes as response variable and the predictors as SrCr, number of successes; GFR, number of successes, respectively. These covariates are linked with the response variable using different link functions. At the last, survival function under different links will be compared.

Keywords


Generalized Linear Model, Link Function, Bayesian Information Criterion, Survival Function, Diabetic Nephropathy, GFR.

References