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Medical Statistics
Luigi Ferrante

Seat Ingegneria
A.A. 2016/2017
Credits 6
Hours 48
Period II
Language ENG

Prerequisites
differential calculus and linear algebra

Learning outcomes
KNOWLEDGE AND UNDERSTANDING:
The course aims to introduce students to the application of biostatistical methods in the study of biomedical phenomena and provide the basic tools to read and interpret the results of a scientific study in biomedical engineering.
CAPACITY TO APPLY KNOWLEDGE AND UNDERSTANDING:
This free-choice course will allow students to learn and apply biostatistical methods to the study of biomedical phenomena and provide the basic tools to read and interpret the results of a scientific study in the field of biomedical engineering. The acquired skills can be applied in the design of experiments and in the statistical analysis of data for epidemiological and clinical purposes. In particular, the student at the end of the course must have acquired adequate knowledge of the basic methodologies of Medical Statistics in order to: - know how to organize and analyze data related to biomedical phenomena including the use of statistical software; - be able to read and interpret statistical results in the Biomedical Engineering literature; - be able to perform data processing and obtain a correct interpretation of the results.
TRANSVERSAL SKILLS:
Making judgements: capability of identify the information needed to design and analyze the results of experimental studies in the field of biomedical engineering. Communications: capability of clearly and exhaustively communicate notions and ideas, relative to study design and data processing, to interlocutors representative of the various and specific competencies involved in the study (engineer, biologist, biostatistician, etc.).

Program
Contents (frontal lessons 32 hours). The design of experimental and observational studies; sampling study; systematic and random error. Statistic units, population, types of variables. Summarizing and presenting data: frequency distributions, tables and graphs. Measures of the central tendency and position of the distribution; measures of variability of the distribution; shapes of frequency distribution. Probability of an event. Properties of probability. Random variables and probability distributions. Binomial, Poisson and Normal distribution. Sampling distribution. Population parameter estimation. Maximum likelihood method. Testing a hypothesis. Principles of significance tests. Significance levels and types of error; the power of a test and sample size. Comparing two means, test t. Comparison of two proportions. Comparing two variances by the F test. Comparing several means using analysis of variance. Assumptions of the analysis of variance. Comparison of means after analysis of variance. Regression and correlation. Linear regression. Logistic regression. Survival data and Cox regression. Practical exercises (16 hours). Basic statistics using R. Applications of statistical methods to the analysis of biomedica data.

Development of the examination
LEARNING EVALUATION METHODS
The assessment of student learning consists of solving a practical test with the use of statistical software and a subsequent oral discussion to evaluate his/her ability in the interpretation of the obtained results.

LEARNING EVALUATION CRITERIA
To successfully pass the assessment of learning, the student must demonstrate, through solving the pratical test and his/her answers to the oral discussion, that he/she has fully understood the concepts presented in the course and is able to apply them.

LEARNING MEASUREMENT CRITERIA
Attribution of the final mark up to thirty.

FINAL MARK ALLOCATION CRITERIA
In order to achieve a positive outcome of the overall evaluation, the student must achieve at least a pass mark, amounting to eighteen points, in the practical test. The vote out of thirty is given by the average of the marks obtained in the practical test and in the oral exam. The students who demonstrate personal in-depth analysis and excellent presentation (particular brilliance in the exposition) can pass the exam with distinction.

Recommended reading
An Introduction to Medical Statistics, 4th ed., Martin Bland. Oxford University Press (2015), ISBN: 9780199589920 R and accompanying manuals are available for free download from http://www.r-project.org. Students may wish to download An Introduction to R to keep as a reference.

Courses

• Biomedical Engineering (Corso di Laurea Magistrale (DM 270/04))