Assessment of haemoglobin a1c evolution using two statistical approaches (survival analysis and linear regression) in persons with diabetes mellitus

Department of Biophysics, Faculty of Medicine and Dentistry, Palacky University Olomouc, Czech Republic.
Biomedical papers of the Medical Faculty of the University Palacky, Olomouc, Czechoslovakia (Impact Factor: 1.2). 06/2009; 153(2):137-43. DOI: 10.5507/bp.2009.023
Source: PubMed


Intensive selfmonitoring is an important and cost-demanding part of diabetes treatment. Continuous glucose monitoring (CGM) using transcutaneous sensors offers "real time" information on glycemia. In the present study, we assessed the therapeutic efficacy of CGM on metabolic control using two different statistical methods: linear regression and "survival analysis".
(1) to assess the therapeutic efficacy of CGM on metabolic control using two different statistical methods: linear regression and survival analysis; (2) to demonstrate the particular advantages of each statistical method.
A total of 42 persons with diabetes mellitus treated by means of an insulin pump participated in this study. According to the means of selfmonitoring persons with diabetes were divided into two groups: 1. intervention group of 17 persons using CGM, 2. control group of 25 persons using a glucometer. Each person was followed for a period of three months. At the beginning of the study and at the end of each month HbA1c was determined.
Both the regression analysis and survival analysis brought evidence of significant changes of the HbA1c in either of the groups. The method of linear regression enables to analyse the evolution of HbA1c in each individual person followed by comparison of the groups. The survival analysis demonstrated that the probability of HbA1c decrease to the predefined level as well as its further maintaining at this level was higher in the CGM group. The mean time interval necessary to HbA1c decrease was shorter in the CGM group.
The efficacy of CGM was demonstrated. In addition to linear regression, survival analysis appears to be an useful complementary method in the statistical evaluation of the treatment efficacy.

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