Statistics and Assessment of Evidence in Medicine
A.Y. 2024/2025
Learning objectives
The course aims to provide the students with knowledge of a) descriptive models that allows to estimate different sources of variability; b) diagnostic test and corresponding measures of accuracy and diagnostic relevance; c) basic principles of observational studies design, interpretation of measures of disease occurrence and association between risk factors and disease occurrence; iv) basic principles of clinical trials design and of the concept of statistical inference.
Expected learning outcomes
Students are expected to develop the ability to use appropriate methodological tools to know, apply, and evaluate, through a critical evaluation of the scientific literature: a) the validity of anamnestic data and physical examinations, b) the usefulness of diagnostic tests and prognostic factors, c) the effectiveness of therapies, rehabilitation practices, prevention programs, estimated by observational and clinical 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
Responsible
Course syllabus
Assessment of the reliability of the methods of collection and measurement of biomedical data
1. Illustrate the concept of sample, population, estimate and parameter
2. Provide examples of systematic errors and random errors
3. Quantitative and qualitative variables
4. Variability of quantitative variables within subject and between subjects
5. Graphical representations of a distribution
6. Position and dispersion indices, and shape of a distribution
7. Indexes of accuracy and precision of a measure
8. Quantiles (quartiles, centiles) and reference limits
9. Correlation and Kappa statistic
Gaussian model
10. Describe the characteristics of the gaussian model of the distribution of measurement errors
11. Interpretation of population data using the Gaussian model
Diagnostic tests
12. Elements of set theory
13. Probability of an event and its calculation
14. Calculate the probability of the intersection of independent events, and the probability of conditioned events
15. The logic of a diagnostic test
16. Define the concept of a false positive and a false negative outcome based on a given threshold value of the diagnostic marker
17. Define the concepts of sensitivity and specificity of a diagnostic test
18. Calculate and discuss the concept of positive and negative predictive value
19. Define the likelihood ratio for a positive (or negative) outcome in a diagnostic test
20. Define the concept of probability and odds pre-test and post-test
21. Explain the meaning of Bayes' theorem
Sample estimate of population parameters and sampling distribution
22. Illustrate the concept of sample variability. Distinguish the concepts of sample and population
23. Distinguish the concepts of sample estimation of a parameter and population parameter
24. Illustrate the meaning of distribution of the sample estimates
25. Illustrate the meaning of standard error of a sample estimate
Confidence interval and its interpretation
26. Concept of confidence interval and its interpretation
27. Calculation of confidence interval
Hypothesis test
28. Type I and type II error, concept of statistical power of a test
29. Calculation of sample size
30. Difference between statistical significance and clinical relevance
31. Knowing how to construct a statistical test when the efficacy is expressed as a mean or difference between means
32. Knowing how to distinguish between independent and paired samples.
32. Knowing how to construct a statistical test when the efficacy is expressed as the difference between dependent samples
33. Knowing how to construct a statistical test when the efficacy is expressed as a proportion or difference between proportions
34. Test of independence between variables: the chi-square
Deterministic model and probabilistic model
35. Distinguish between deterministic model and probabilistic model
36. Illustrate the characteristics of the simple linear regression model and the interpretation of the parameters of the simple linear regression
37. Calculate hypothesis tests on the parameters of the simple linear regression model
38. Explain how to correct (adjust) for a confounding variable the association between two sets of data
Identification of risk factors and prognostic factors
39. Observational studies and their role in evidence-based medicine
40. Transversal studies / retrospective or case-control studies / longitudinal or cohort studies
41. Measures of occurrence in epidemiology and clinical epidemiology: prevalence, incidence, cumulative incidence
42. Measures of association between risk factor and disease: absolute risk, attributable risk, relative risk, odds ratio
1. Illustrate the concept of sample, population, estimate and parameter
2. Provide examples of systematic errors and random errors
3. Quantitative and qualitative variables
4. Variability of quantitative variables within subject and between subjects
5. Graphical representations of a distribution
6. Position and dispersion indices, and shape of a distribution
7. Indexes of accuracy and precision of a measure
8. Quantiles (quartiles, centiles) and reference limits
9. Correlation and Kappa statistic
Gaussian model
10. Describe the characteristics of the gaussian model of the distribution of measurement errors
11. Interpretation of population data using the Gaussian model
Diagnostic tests
12. Elements of set theory
13. Probability of an event and its calculation
14. Calculate the probability of the intersection of independent events, and the probability of conditioned events
15. The logic of a diagnostic test
16. Define the concept of a false positive and a false negative outcome based on a given threshold value of the diagnostic marker
17. Define the concepts of sensitivity and specificity of a diagnostic test
18. Calculate and discuss the concept of positive and negative predictive value
19. Define the likelihood ratio for a positive (or negative) outcome in a diagnostic test
20. Define the concept of probability and odds pre-test and post-test
21. Explain the meaning of Bayes' theorem
Sample estimate of population parameters and sampling distribution
22. Illustrate the concept of sample variability. Distinguish the concepts of sample and population
23. Distinguish the concepts of sample estimation of a parameter and population parameter
24. Illustrate the meaning of distribution of the sample estimates
25. Illustrate the meaning of standard error of a sample estimate
Confidence interval and its interpretation
26. Concept of confidence interval and its interpretation
27. Calculation of confidence interval
Hypothesis test
28. Type I and type II error, concept of statistical power of a test
29. Calculation of sample size
30. Difference between statistical significance and clinical relevance
31. Knowing how to construct a statistical test when the efficacy is expressed as a mean or difference between means
32. Knowing how to distinguish between independent and paired samples.
32. Knowing how to construct a statistical test when the efficacy is expressed as the difference between dependent samples
33. Knowing how to construct a statistical test when the efficacy is expressed as a proportion or difference between proportions
34. Test of independence between variables: the chi-square
Deterministic model and probabilistic model
35. Distinguish between deterministic model and probabilistic model
36. Illustrate the characteristics of the simple linear regression model and the interpretation of the parameters of the simple linear regression
37. Calculate hypothesis tests on the parameters of the simple linear regression model
38. Explain how to correct (adjust) for a confounding variable the association between two sets of data
Identification of risk factors and prognostic factors
39. Observational studies and their role in evidence-based medicine
40. Transversal studies / retrospective or case-control studies / longitudinal or cohort studies
41. Measures of occurrence in epidemiology and clinical epidemiology: prevalence, incidence, cumulative incidence
42. Measures of association between risk factor and disease: absolute risk, attributable risk, relative risk, odds ratio
Prerequisites for admission
Algebra skills acquired at high school
Teaching methods
Each credit includes hours of frontal and innovative teaching. The innovative teaching activities consist in the deepening of specific topics of the course syllabus, that will be selected by the students and the teacher. Such activity will be carried out in active collaboration between students and teacher.
The frontal teaching hours of this course are divided into frontal lessons and exercises.
All the material is stored on a specific ARIEL website. Furthermore, in ARIEL scientific articles in English and additional exercises are available for the students to deepen the topics addressed during the frontal lessons.
The frontal teaching hours of this course are divided into frontal lessons and exercises.
All the material is stored on a specific ARIEL website. Furthermore, in ARIEL scientific articles in English and additional exercises are available for the students to deepen the topics addressed during the frontal lessons.
Teaching Resources
Discretionary among the following:
M. Pagano e K. Gauvreau Biostatistica (2ª edizione) Editore: Idelson-Gnocchi 2003
M. Bland Statistica medica (2° edizione) Editore: Apogeo 2019
JF. Jekel, DL. Katz, JG. Elmore, DMG. Wild Epidemiologia Biostatistica e Medicina Preventiva (3ª edizione) Editore: Masson 2009
W. Daniel Biostatistica. Concetti di base per l'analisi statistica delle scienze dell'area medico-sanitaria (3° edizione) Editore: Edises 2019
M. Pagano e K. Gauvreau Biostatistica (2ª edizione) Editore: Idelson-Gnocchi 2003
M. Bland Statistica medica (2° edizione) Editore: Apogeo 2019
JF. Jekel, DL. Katz, JG. Elmore, DMG. Wild Epidemiologia Biostatistica e Medicina Preventiva (3ª edizione) Editore: Masson 2009
W. Daniel Biostatistica. Concetti di base per l'analisi statistica delle scienze dell'area medico-sanitaria (3° edizione) Editore: Edises 2019
Assessment methods and Criteria
The aim of the examination is to verify the theoretical / methodological knowledge acquired by the student, the ability to correctly apply descriptive and interpretative models to small sets of real data, to use critically diagnostic tests, through the application of the concepts of norm and probability in medicine, and finally to evaluate the results of a clinical, experimental or observational study, also through the critical analysis of the medical literature.
The examination includes a written computerized test consisting of multiple choice questions and calculation exercises. A score is assigned to each question, acknowledging the correct answer. No penalty is assigned to the wrong answer. The sum of the scores gives the final mark, expressed in thirtieths.
During the written test, the students will be allowed to consult all the material they deem appropriate (books, course slides or notes), but they will not be allowed to use electronic devices (mobile phones, iPads, etc.). It is also necessary to use a calculator to perform correctly the calculations.
The results of the written test are published on Ariel, usually in the week following the examination. The mark is recorded on SIFA in a "student refusable outcome" mode; therefore the student will have time (technical times of the UNIMI system) to decide whether or not to accept the achieved mark.
There are no intermediate tests. There is no oral examination.
The examination includes a written computerized test consisting of multiple choice questions and calculation exercises. A score is assigned to each question, acknowledging the correct answer. No penalty is assigned to the wrong answer. The sum of the scores gives the final mark, expressed in thirtieths.
During the written test, the students will be allowed to consult all the material they deem appropriate (books, course slides or notes), but they will not be allowed to use electronic devices (mobile phones, iPads, etc.). It is also necessary to use a calculator to perform correctly the calculations.
The results of the written test are published on Ariel, usually in the week following the examination. The mark is recorded on SIFA in a "student refusable outcome" mode; therefore the student will have time (technical times of the UNIMI system) to decide whether or not to accept the achieved mark.
There are no intermediate tests. There is no oral examination.
MED/01 - MEDICAL STATISTICS - University credits: 7
Lessons: 56 hours
: 28 hours
: 28 hours
Professor:
Bravi Francesca
Shifts:
Turno
Professor:
Bravi FrancescaProfessor(s)