A Bayesian Approach for Estimating Onset Time of Nephropathy for Type 2 Diabetic Patients Under Various Health Conditions

Gurprit Grover, Alka Sabharwal, Juhi Mittal

Abstract


Nephropathy is a life-threatening complication of diabetes mellitus and is the leading cause of end-stage renal disease. The rate of rise in Serum Creatinine (SrCr) is a well-accepted marker for the progression of Diabetic Nephropathy (DN). The objective of this paper is to estimate the DN onset times and for this a retrospective data from 132 type 2 diabetic patients were collected as per American Diabetes Association (ADA) standards. This data is divided into four groups: Less Advance DN (LADN) group (1.4mg/dl $\leq$ SrCr $\leq$ 1.9 mg/dl), Advance DN (ADN) group (SrCr $>$ 1.9 mg/dl), Non-Informative group (SrCr $<$ 1.4 mg/dl \& duration of diabetes $\leq$ 15 years) and Controlled group (SrCr $<$ 1.4 mg/dl \& duration of diabetes $>$15 years). For estimating the time of onset of DN for each group we have applied Bayesian approach and Bayes estimate is obtained under squared error loss function for the unknown scale parameter. We found that patients with DN as complication will reach less advance and advance stages of DN in approximately 15.012 years and 16.890 years respectively. And it was also found that patients with duration of diabetes less than 15 years (non-informative group) will be free from DN for at least 7.781 years. And, patients with duration of diabetes greater than 15 years (controlled group) will not develop any complication up to 20.109 years. This estimation can be used to predict the future onset times of new type 2 diabetic patients.

Full Text: PDF DOI: 10.5539/ijsp.v2n2p89

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International Journal of Statistics and Probability   ISSN 1927-7032(Print)   ISSN 1927-7040(Online)

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