Biostatistics and data analysis
Learning outcomes of the course unit
By the end of the course the student should have learnd about the basic principles of medical statistics and of epidemiology. The student will be able to use the main medical statistical indicators, to program sampling, to determine if a diagnostic test is accurate, and to describe relationships among variables.
Course contents summary
Introduction to biostatistics.
Experimental design. Sampling.
Regression and correlation
Clinical and environmental epidemiology.
Evaluating the health status of a population, the effectiveness of a therapy. Diagnostic tests.
Use of principal statistical software’s.
In addition, the course will have at least two lectures from a Clinical Research associate and one Epidemiologist.
• Part 1: revision of descriptive statistics with exercises with Excel or Past (trend and variability indicators, normal distribution and standardized normal distribution, confidence interval, t-Student tests, contingency table, correlation, regression analysis) and lesson on data visualization.
• Part2: Clinical trials: different phases and sampling (professionals outcomes)
• Part3: diagnostic tests from the biological and statistical point of view ( sensibility, specificity, predictive values, ROC curves ) with exercises with Excel or Past
• Part4: basis of epidemiology ( definition of differences between cause and risk, introduction to the main indicators used in epidemiology, such as odds ratio and relative risk ratio)
Slides will be available to students through the platform Elly.
R.Beaglehole, R. Bonita, T. Kjellström, Epidemiologia di base, Edizione italiana a cura di G. Agazzotti, Editoriale Fernando Folini, Casalnoceto, 1997.
M.L. Bacchi Reggiani, E. Baldi Cosseddu, A. Dormi. Principi di statistica ed epidemiologia, Società editrice Esculapio, 2010
Glantz, Statistica per le discipline biomediche, McGraw-Hill.
Frontal lectures, several practise exercises on the PC.
Possibility of visiting the lab of statistical epidemiology of UNIPR
Assessment methods and criteria
At the end of the course each student will receive a written exam to prepare at home, and that will be discussed in detail orally.
With respect to the Dublin’s descriptors, it will be evaluated:
• knowledge and comprehension of the topics discussed in the classroom, considering also the use of text books
• the capacity to apply the acquired knowledge to case studies, where of critical importance will be the capacity to collect data, to interpret them, and to communicate them in a clear and critical way