Methods of Experimental and Technological Research
A.Y. 2020/2021
Learning objectives
- know and interpret the role of variability in biomedical research
- know how to apply and interpret basic statistical methods
- apply the necessary methodology to assess the accuracy and precision of the data
- know the quantitative approaches
- interpret quantitative results
- know how to apply and interpret basic statistical methods
- apply the necessary methodology to assess the accuracy and precision of the data
- know the quantitative approaches
- interpret quantitative results
Expected learning outcomes
At the end of the course the student must be able to know how to apply the acquired knowledge to read critically the results of scientific studies.
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Didactic methods.
Half of the students, according to pre-established shifts, will participate from time to time in face-to-face lessons in classrooms suitably equipped for the Covid emergency, where safety conditions allow it.
Furthermore, to allow them to be used both in synchronous and asynchronous mode for students not present in the classroom, the lessons will be held simultaneously on the Microsoft Teams platform and can be followed both in synchrony on the basis of the first semester and in asynchronous mode, because they will be registered and left on the same platform available to students not present in the classroom.
Lessons may also include pre-recorded materials.
If the situation worsens, the lessons will be delivered through the Microsoft Teams platform and can be followed both in synchronous according to the scheduled time and asynchronously, because they are recorded through the same platform.
Program and reference material.
The program and reference material will not change. The teaching material (slides displayed during the lectures) will be available on the Ariel platform dedicated to teaching.
Methods for verifying learning and evaluation criteria.
The exam will take place in writing in the presence or in the event of an aggravation of the pandemic, with the moodle + seb + webconference modality to be activated in collaboration with the Center for educational innovation and multimedia technologies of the University of Milan.
Half of the students, according to pre-established shifts, will participate from time to time in face-to-face lessons in classrooms suitably equipped for the Covid emergency, where safety conditions allow it.
Furthermore, to allow them to be used both in synchronous and asynchronous mode for students not present in the classroom, the lessons will be held simultaneously on the Microsoft Teams platform and can be followed both in synchrony on the basis of the first semester and in asynchronous mode, because they will be registered and left on the same platform available to students not present in the classroom.
Lessons may also include pre-recorded materials.
If the situation worsens, the lessons will be delivered through the Microsoft Teams platform and can be followed both in synchronous according to the scheduled time and asynchronously, because they are recorded through the same platform.
Program and reference material.
The program and reference material will not change. The teaching material (slides displayed during the lectures) will be available on the Ariel platform dedicated to teaching.
Methods for verifying learning and evaluation criteria.
The exam will take place in writing in the presence or in the event of an aggravation of the pandemic, with the moodle + seb + webconference modality to be activated in collaboration with the Center for educational innovation and multimedia technologies of the University of Milan.
Prerequisites for admission
No prior knowledge required.
Assessment methods and Criteria
Written exam with 30 quizzes (with 3 or 4 answer options). Each exact answer will be assigned 1 point. There are no penalties for unanswered or incorrect answers. The test can also be taken twice in the same session.
The final exam aims to assess the achievement of the set educational objectives.
The booking on the UNIMIA portal, within the dates established for each appeal, is mandatory for the examination.
The final exam aims to assess the achievement of the set educational objectives.
The booking on the UNIMIA portal, within the dates established for each appeal, is mandatory for the examination.
Computer systems for quality control in biomedical applications
Course syllabus
Using Excel for basic statistics.
Statistics program R.
Statistics application with R.
Definition of quality.
Quality management.
Legislation related to quality controls.
Statistical methods for quality control.
Use of clinical G2.
Statistics program R.
Statistics application with R.
Definition of quality.
Quality management.
Legislation related to quality controls.
Statistical methods for quality control.
Use of clinical G2.
Teaching methods
The course is divided into a series of lectures with slide shows in Power Point. The lectures slides are uploaded to the Ariel website.
Teaching Resources
POLI: Excel 2016. Formule e analisi dei dati - Hoepli editore, ISBN-13: 978-8820375102
Information processing and computer networks
Course syllabus
Computer network services.
Network classification.
General structure of network architectures, introduction to protocols.
The transmission media.
Local networks.
Network level: routing and IP protocol.
Transport level: TCP and UDP.
Level of TCP / IP applications.
Microsoft protocols.
Network classification.
General structure of network architectures, introduction to protocols.
The transmission media.
Local networks.
Network level: routing and IP protocol.
Transport level: TCP and UDP.
Level of TCP / IP applications.
Microsoft protocols.
Teaching methods
The course is divided into a series of lectures with slide shows in Power Point. The lectures slides are uploaded to the Ariel website.
Teaching Resources
GERACE: La logica dei sistemi di elaborazione - Editori Riuniti (University Press)
Research in medical radiology
Course syllabus
1. Evidence-Based Medicine.
1.1. Hierarchy of studies on diagnostic tests.
2. Diagnostic performance measures.
2.1. The results of a survey compared to a standard reference.
2.2. Sensitivity and specificity.
2.3. Predictive values, diagnostic accuracy and influence of disease prevalence.
2.4. Bayes theorem or subjective probability or conditional probability and likelihood ratio.
2.5. Discriminant thresholds and ROC curves.
3. Overdiagnosis.
4. Study design, systematic reviews and levels of evidence.
4.1. Phases 1, 2, 3 and 4 of the pharmacological research.
4.2. Classification of studies.
4.3. Experimental studies and control group.
4.4. Randomized trials.
4.4. Observational studies.
4.5. Sample size.
4.6. Systematic reviews (metanalysis).
4.7. Levels of evidence.
5. How to write a scientific radiological work.
5.1. Are informed consent and approval by the Ethics Committee always necessary?
5.2. Title, running title and Title page.
5.3. The four-section diagram, their dimensions and the editing sequence.
5.4. "Introduction": why did you do it?
5.5. "Materials and methods": what did you do and how did you do it?
5.6. «Results»: what did you find?
5.7. "Discussion": what is the meaning of your results?
5.8. "References."
5.9. «Abstract» and «Key words».
1.1. Hierarchy of studies on diagnostic tests.
2. Diagnostic performance measures.
2.1. The results of a survey compared to a standard reference.
2.2. Sensitivity and specificity.
2.3. Predictive values, diagnostic accuracy and influence of disease prevalence.
2.4. Bayes theorem or subjective probability or conditional probability and likelihood ratio.
2.5. Discriminant thresholds and ROC curves.
3. Overdiagnosis.
4. Study design, systematic reviews and levels of evidence.
4.1. Phases 1, 2, 3 and 4 of the pharmacological research.
4.2. Classification of studies.
4.3. Experimental studies and control group.
4.4. Randomized trials.
4.4. Observational studies.
4.5. Sample size.
4.6. Systematic reviews (metanalysis).
4.7. Levels of evidence.
5. How to write a scientific radiological work.
5.1. Are informed consent and approval by the Ethics Committee always necessary?
5.2. Title, running title and Title page.
5.3. The four-section diagram, their dimensions and the editing sequence.
5.4. "Introduction": why did you do it?
5.5. "Materials and methods": what did you do and how did you do it?
5.6. «Results»: what did you find?
5.7. "Discussion": what is the meaning of your results?
5.8. "References."
5.9. «Abstract» and «Key words».
Teaching methods
The course is divided into a series of lectures with slide shows in Power Point. The lectures slides are uploaded to the Ariel website.
Teaching Resources
SARDANELLI, DI LEO: Biostatistics for radiologists - Springer, 2009
SARDANELLI, DI LEO: Biostatistica in radiologia - Springer, 2009
SARDANELLI, DI LEO: Biostatistica in radiologia - Springer, 2009
Statistics for clinical and technological research
Course syllabus
1. Variables and measurement scales, normal distribution and confidence intervals.
1.1. Variables and measurement scales.
1.1.1. Categorical variables.
1.1.2. Discrete numerical variables.
1.1.3. Continuous numeric variables.
1.2. Measurement scales.
1.3. The Gauss distribution.
1.4. Notes on descriptive statistics.
1.5. Indices of central tendency.
1.6. Dispersion around the central tendency: variance and standard deviation.
1.7. Standard error of the average.
1.8. The confidence intervals.
2. Null hypothesis, significance and statistical power.
2.1. Hypothesis null and principle of falsifiability.
2.2. Significance threshold, type I error and type II error.
2.3. Statistical power.
2.4. Why 0.05?
2.5. Interpreting the p.
3. Parametric and non-parametric statistics.
3.1. Parametric statistics.
3.1.1. The basics of parametric statistics.
3.1.2 Comparison between two sample averages: Student's t test.
3.1.3 Comparison of three or more sample averages: the analysis of variance.
3.1.4. ANOVA for independent and dependent groups.
3.1.5 The parametric statistics in radiology.
3.2. Non-parametric statistics.
3.2.1 A sample with two dependent or coupled measurements.
3.2.2. Variables measured on a dichotomous scale.
3.2.3. Variables measured on ordinal scale.
3.2.4. Variables measured on an interval or rational scale.
3.2.5 Two independent samples.
3.2.6. Variables measured on nominal or ordinal scale.
3.2.7. Variables measured on an interval or rational scale.
3.2.8. Three or more (k) dependent samples.
3.2.9. Variables measured on a dichotomous scale.
3.2.10. Variables measured on ordinal, interval or rational scale.
3.2.11 Three or more (k) independent samples.
3.2.12. Data measured on nominal or ordinal scale.
3.2.13. Variables measured on an interval or rational scale.
3.2.14. Considerations on non-parametric tests.
4. Correlation and linear regression.
4.1. Association and causation.
4.2. Correlation between continuous variables.
4.3. Interpretation of the correlation coefficient.
4.4. Significance test.
4.5. Rank correlation.
4.6. Linear regression.
4.6.1. The calculation of the coefficients.
4.7. Interpretation of the regression line.
4.8. Limitations on the use of linear regression.
5. Reproducibility: intraobserver and interobserver variability.
5.1. Sources of variability.
5.2. Why is it important to know the variability of the measurements?
5.3. The intra- and interobserver variability for continuous variables. The Bland-Altman method.
5.4. Interpretation of the results of the Bland-Altman method.
5.5. Intra- and interobserver variability for categorical variables: Cohen's kappa.
1.1. Variables and measurement scales.
1.1.1. Categorical variables.
1.1.2. Discrete numerical variables.
1.1.3. Continuous numeric variables.
1.2. Measurement scales.
1.3. The Gauss distribution.
1.4. Notes on descriptive statistics.
1.5. Indices of central tendency.
1.6. Dispersion around the central tendency: variance and standard deviation.
1.7. Standard error of the average.
1.8. The confidence intervals.
2. Null hypothesis, significance and statistical power.
2.1. Hypothesis null and principle of falsifiability.
2.2. Significance threshold, type I error and type II error.
2.3. Statistical power.
2.4. Why 0.05?
2.5. Interpreting the p.
3. Parametric and non-parametric statistics.
3.1. Parametric statistics.
3.1.1. The basics of parametric statistics.
3.1.2 Comparison between two sample averages: Student's t test.
3.1.3 Comparison of three or more sample averages: the analysis of variance.
3.1.4. ANOVA for independent and dependent groups.
3.1.5 The parametric statistics in radiology.
3.2. Non-parametric statistics.
3.2.1 A sample with two dependent or coupled measurements.
3.2.2. Variables measured on a dichotomous scale.
3.2.3. Variables measured on ordinal scale.
3.2.4. Variables measured on an interval or rational scale.
3.2.5 Two independent samples.
3.2.6. Variables measured on nominal or ordinal scale.
3.2.7. Variables measured on an interval or rational scale.
3.2.8. Three or more (k) dependent samples.
3.2.9. Variables measured on a dichotomous scale.
3.2.10. Variables measured on ordinal, interval or rational scale.
3.2.11 Three or more (k) independent samples.
3.2.12. Data measured on nominal or ordinal scale.
3.2.13. Variables measured on an interval or rational scale.
3.2.14. Considerations on non-parametric tests.
4. Correlation and linear regression.
4.1. Association and causation.
4.2. Correlation between continuous variables.
4.3. Interpretation of the correlation coefficient.
4.4. Significance test.
4.5. Rank correlation.
4.6. Linear regression.
4.6.1. The calculation of the coefficients.
4.7. Interpretation of the regression line.
4.8. Limitations on the use of linear regression.
5. Reproducibility: intraobserver and interobserver variability.
5.1. Sources of variability.
5.2. Why is it important to know the variability of the measurements?
5.3. The intra- and interobserver variability for continuous variables. The Bland-Altman method.
5.4. Interpretation of the results of the Bland-Altman method.
5.5. Intra- and interobserver variability for categorical variables: Cohen's kappa.
Teaching methods
The course is divided into a series of lectures with slide shows in Power Point. The lectures slides are uploaded to the Ariel website.
Teaching Resources
SARDANELLI, DI LEO: Biostatistics for radiologists - Springer, 2009
SARDANELLI, DI LEO: Biostatistica in radiologia - Springer, 2009
SARDANELLI, DI LEO: Biostatistica in radiologia - Springer, 2009
Computer systems for quality control in biomedical applications
FIS/07 - APPLIED PHYSICS - University credits: 1
Lessons: 10 hours
Professor:
De Marco Paolo
Information processing and computer networks
ING-INF/05 - INFORMATION PROCESSING SYSTEMS - University credits: 2
Lessons: 20 hours
Professor:
Colombo Alberto
Research in medical radiology
MED/36 - IMAGING AND RADIOTHERAPY - University credits: 1
Lessons: 10 hours
Professor:
Sardanelli Francesco
Statistics for clinical and technological research
SECS-S/02 - STATISTICS FOR EXPERIMENTAL AND TECHNOLOGICAL RESEARCH - University credits: 1
Lessons: 10 hours
Professor:
Sardanelli Francesco
Professor(s)