Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub. 2009, 153(2):137–144.
© K. Langova, H. Pribylova, M. Kajabova, J. Luza
ASSESSMENT OF HAEMOGLOBIN A1C EVOLUTION USING TWO STATISTICAL
APPROACHES (SURVIVAL ANALYSIS AND LINEAR REGRESSION) IN PERSONS
WITH DIABETES MELLITUS
Katerina Langovaa*, Helena Pribylovab, c, Marketa Kajabovad, Jiri Luzab
a Department of Biophysics, Faculty of Medicine and Dentistry, Palacky University Olomouc, Czech Republic
b Department of Physiology, Faculty of Medicine and Dentistry, Palacky University Olomouc
c Department of Nursing, Faculty of Health Sciences, Palacky University Olomouc
d Department of Clinical Biochemistry, University Hospital Olomouc
Received: September 7, 2008; Accepted (with revision): December 12, 2008
Key words: Survival analysis/Linear regression/Haemoglobin A1c/Continuous glucose monitoring/Diabetes mellitus
Background: 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”.
Objectives: (1) to assess the therapeutic efficacy of CGM on metabolic control using two different statistical meth-
ods: linear regression and survival analysis; (2) to demonstrate the particular advantages of each statistical method.
Methods: 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.
Results: 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 de-
crease 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.
Conclusions: 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.
Intensive selfmonitoring is an important and costly
part of diabetes treatment5, 17, 19, 29, particularly in persons
using insulin pumps27. In recent years, continuous glu-
cose monitoring (CGM) with transcutaneous sensors,
transmitters and monitors has become a sophisticated
approach offering “real time” information on glycemia.
Several studies have shown the effectiveness of CGM2, 6.
However, the benefits, hazards, accuracy, reliability and
clinical applicability of CGM6, 12, 13 need to be re-estab-
lished using both case reports and appropriate statistical
methods even though recent trials demonstrate that in-
terstinal fluid glucose and blood glucose concentrations
could be made identical by resorting to algorithmus based
on concurrent blood glucose levels alone20.
Since 2002 we have done statistical analyses for a
number of clinical studies on diabetes (7-13, 21, 23-26). The
glycaemic profiles and haemoglobin A1c were evaluated
as parameters of diabetes control indicating the success
of treatment. Concentration of haemoglobin A1c highly
correlates with the mean plasma glucose concentration.
Various statistical methods but survival analysis were ap-
plied according to the analyzed data and objectives of the
Linear regression is a form of regression analysis in
which the relationship between one or more independent
variables and another variable (dependent variable), is
modelled by a special function, namely, linear regression
Survival analysis is a set of statistical methods which
evaluate the time interval from the beginning of the obser-
vation until the occurrence of a certain event. Generally,
this time interval is called the survival interval (although
it does not need to identify the survival of a patient). The
survival interval identifies the number of years, months,
weeks or days from the beginning of the observation until
the occurrence of a defined event.
Most studies are complete before the observed event
occurs for all subjects. This situation is in survival analysis
described as “censoring”.
An example of such event would be the achieve-
ment of a certain level of diabetes compensation.
Survival analysis was first described by Kaplan and Meier