Demographic and healthcare statistics

A.Y. 2019/2020
5
Max ECTS
40
Overall hours
SSD
ING-INF/05 ING-INF/06 MED/01
Language
Italian
Learning objectives
The purpose of teaching is:
- Apply the computer operating system's knowledge in order to manage data set
- Analyze the demographic and health phenomena using the statistical descriptive and inferential methods
Expected learning outcomes
The student will use the statistical descriptive and inferential methods in order to analyse the community care needs
The student will use the computer operating system to manage data set
Course syllabus and organization

Unique edition

Responsible
Prerequisites for admission
Required preliminary knowledge: Basic algebra and elements of geometry.
Assessement methods and criteria
Statistics: Written examination with a combination of multiple-choice questions (1 right, 4 wrong answers) and small problems or toy examples with a series of questions to reply. Every question has its mark indicated in the written examination. Students who gain a final mark higher than 30 will receive a mark of 30 cum laude. A calculator and the tables of the probability distributions found in Ariel are necessary to solve the written examination. Students are allowed to fill in a form with all the formulas to keep during the examination. Results will be provided to the students via Ariel.
Informatics: Resolution of a computer exercise using the Excel program (Microsoft). Each correct question is worth about 3 points. The sum of the points corresponds to the final outcome of the test. The test is passed if the sum of the points is greater than or equal to 18.
Sistemi di elaborazione delle informazioni
Course syllabus
Introduction to the procedures based on hypothesis testing for quantitative or interval variables
Description of the sample using the sample mean and the sample variance
Description of the sample through the distribution and calculation of percentiles
Hypothesis test for the assessment of differences between groups: F-test
Homoscedasticity test
Hypothesis test for the evaluation of the differences between two groups: t-test for unpaired data
Use of the t-test to isolate differences between groups in multiple comparison problems
Hypothesis test for repeated measures: t-test for paired data
Hypothesis test based on the rank sum: the Mann-Whitney test
Introduction to the procedures based on the hypothesis test for two mutually exclusive nominal variables
Description of the sample by the frequency of the two categories
Construction of the contingency table
Hypothesis test to evaluate the differences between groups: χ2 tests
Use of the χ2 test to isolate differences between groups in multiple comparison problems
Hypothesis test for repeated measures: McNemar test
Teaching methods
Teaching through slides and blackboard use with a problem-solving approach; practical sessions of exercises; slides and exercises available from Ariel for additional homeworks.
The lecturers and practical sessions have done in a classroom using computers using the Excel program (Microsoft). Each lesson has the structure of a practical training session: in each lesson one or more problems related to the various topics of the course are solved on the computer. Each practical training session is preceded by the projection of PowerPoint slides related to the theoretical topic of the session.
Bibliography
Pagano - Gauvreau "Principles of Biostatistics", Chapman and Hall/CRC
Bland "An Introduction to Medical Statistics" (English Edition) 4th Edition, Oxford University Press
Elmore - Wild - Nelson - Katz "Jekel's Epidemiology, Biostatistics, Preventive Medicine, and Public Health" Elsevier - Health Sciences Division, 2020 (in press)
S.A. Glantz, Primer on Bio-Statistics, McGraw-Hill. 1997
Microsfot Excel Tutorial, Microsoft
Bioingegneria elettronica ed informatica
Course syllabus
Introduction to the procedures based on hypothesis testing for quantitative or interval variables
Description of the sample using the sample mean and the sample variance
Description of the sample through the distribution and calculation of percentiles
Hypothesis test for the assessment of differences between groups: F-test
Homoscedasticity test
Hypothesis test for the evaluation of the differences between two groups: t-test for unpaired data
Use of the t-test to isolate differences between groups in multiple comparison problems
Hypothesis test for repeated measures: t-test for paired data
Hypothesis test based on the rank sum: the Mann-Whitney test
Introduction to the procedures based on the hypothesis test for two mutually exclusive nominal variables
Description of the sample by the frequency of the two categories
Construction of the contingency table
Hypothesis test to evaluate the differences between groups: χ2 tests
Use of the χ2 test to isolate differences between groups in multiple comparison problems
Hypothesis test for repeated measures: McNemar test
Teaching methods
Teaching through slides and blackboard use with a problem-solving approach; practical sessions of exercises; slides and exercises available from Ariel for additional homeworks.
The lecturers and practical sessions have done in a classroom using computers using the Excel program (Microsoft). Each lesson has the structure of a practical training session: in each lesson one or more problems related to the various topics of the course are solved on the computer. Each practical training session is preceded by the projection of PowerPoint slides related to the theoretical topic of the session.
Bibliography
Pagano - Gauvreau "Principles of Biostatistics", Chapman and Hall/CRC
Bland "An Introduction to Medical Statistics" (English Edition) 4th Edition, Oxford University Press
Elmore - Wild - Nelson - Katz "Jekel's Epidemiology, Biostatistics, Preventive Medicine, and Public Health" Elsevier - Health Sciences Division, 2020 (in press)
S.A. Glantz, Primer on Bio-Statistics, McGraw-Hill. 1997
Microsfot Excel Tutorial, Microsoft
Statistica medica
Course syllabus
Reliability of a measure
∙ Reliability and its components
∙ Systematic error and casual error
Variability
Between-subjects and within-subjects variability
Descriptive statistics
∙ Graphs
∙ Location, scale, and shape of a frequency distribution
∙ Measures of location and scale
∙ Accuracy and precision of a measure
∙ Quantiles and reference limits
∙ Correlation and Kappa statistic
Gaussian model
∙ Probability of events on the population within the Gaussian model
∙ How to model the error with a Gaussian model
Screening programs
∙ Events
∙ Probability: concept
∙ Probability of an event: algebra
∙ Basics of probability
∙ Screening programs: why
∙ True and false positives, true and false negatives
∙ Sensitivity and specificity of a diagnostic tool
∙ Positive (negative) predictive values
∙ Likelihood ratio: positive and negative
∙ Pre-test and post-test probabilities
∙ Bayes theorem
Inference
∙ Sampling variability
∙ Population and sample
∙ Estimate of a population parameter with sampling quantities
Sampling distribution
∙ The central limit theorem and the distribution of a sampling quantity
∙ Standard error
Confidence interval
∙ Definition and meaning
∙ Formulas
Hypothesis testing
∙ First type and second type errors and power of a test
∙ Sample size calculation
∙ Clinical statistics and clinical relevance
∙ Hypothesis testing on a population mean
Deterministic and probabilistic models
∙ Deterministic and probabilistic models: differences
∙ Simple linear regression model: interpretation and parameters
∙ Hypothesis testing on the parameters of a simple linear regression model
Teaching methods
Teaching through slides and blackboard use with a problem-solving approach; practical sessions of exercises; slides and exercises available from Ariel for additional homeworks.
The lecturers and practical sessions have done in a classroom using computers using the Excel program (Microsoft). Each lesson has the structure of a practical training session: in each lesson one or more problems related to the various topics of the course are solved on the computer. Each practical training session is preceded by the projection of PowerPoint slides related to the theoretical topic of the session.
Bibliography
Pagano - Gauvreau "Principles of Biostatistics", Chapman and Hall/CRC
Bland "An Introduction to Medical Statistics" (English Edition) 4th Edition, Oxford University Press
Elmore - Wild - Nelson - Katz "Jekel's Epidemiology, Biostatistics, Preventive Medicine, and Public Health" Elsevier - Health Sciences Division, 2020 (in press)
S.A. Glantz, Primer on Bio-Statistics, McGraw-Hill. 1997
Microsfot Excel Tutorial, Microsoft
Bioingegneria elettronica ed informatica
ING-INF/06 - ELECTRONIC AND INFORMATICS BIOENGINEERING - University credits: 2
Lessons: 16 hours
Professor: Porta Alberto
Sistemi di elaborazione delle informazioni
ING-INF/05 - INFORMATION PROCESSING SYSTEMS - University credits: 1
Lessons: 8 hours
Professor: Porta Alberto
Statistica medica
MED/01 - MEDICAL STATISTICS - University credits: 2
Lessons: 16 hours
Professor(s)
Reception:
For meetings, please write an email.
Campus Cascina Rosa, via A. Vanzetti, 5, 20133 Milano - room number 3 - 4