ID:
500026
Duration (hours):
40
CFU:
5
SSD:
STATISTICA MEDICA
Year:
2025
Overview
Date/time interval
Secondo Semestre (02/03/2026 - 05/06/2026)
Syllabus
Course Objectives
The course, structured with lectures and computer exercises, aims primarily to provide the theoretical knowledge, operational skills, and practical abilities essential for collecting and analyzing statistical data in the medical field.
The course first seeks to introduce students to study design, providing a solid understanding of the various types of designs used in medical research. Subsequently, students will learn how to calculate and interpret frequency distributions for different variables, a fundamental skill for data analysis.
Another key objective of the course is to teach students how to calculate measures of central tendency (such as median, mean, and mode) and variability (such as range and standard deviation). These skills are essential for describing and understanding the collected data.
Additionally, the course will provide the foundation for calculating and interpreting measures of occurrence (such as prevalence and incidence) and effect (such as relative risk and odds ratio), which are crucial for assessing the impact of specific variables on health.
A significant portion of the course will be dedicated to learning the R environment, the widely used open-source software for data manipulation, analysis, and graphical representation. Students will become familiar with R's basic objects and syntax, facilitating their use of the software for analyses.
The course will also cover statistical significance tests, providing students with the skills to conduct and interpret tests such as the t-test and chi-square test. Techniques for constructing and interpreting confidence intervals, useful for estimating the uncertainty of measures, will also be taught.
Another important aspect of the course will be sample size determination, which is fundamental to ensuring that research results are meaningful. Students will learn to calculate the necessary sample size for various types of studies.
The course will also address the use of regression models (linear and logistic), essential tools for analyzing relationships between variables. Furthermore, students will acquire skills in survival analysis, which includes analyzing survival curves and Cox proportional hazards models.
Finally, the course will cover the implementation of multivariate models, allowing students to analyze complex data involving multiple variables simultaneously.
The course first seeks to introduce students to study design, providing a solid understanding of the various types of designs used in medical research. Subsequently, students will learn how to calculate and interpret frequency distributions for different variables, a fundamental skill for data analysis.
Another key objective of the course is to teach students how to calculate measures of central tendency (such as median, mean, and mode) and variability (such as range and standard deviation). These skills are essential for describing and understanding the collected data.
Additionally, the course will provide the foundation for calculating and interpreting measures of occurrence (such as prevalence and incidence) and effect (such as relative risk and odds ratio), which are crucial for assessing the impact of specific variables on health.
A significant portion of the course will be dedicated to learning the R environment, the widely used open-source software for data manipulation, analysis, and graphical representation. Students will become familiar with R's basic objects and syntax, facilitating their use of the software for analyses.
The course will also cover statistical significance tests, providing students with the skills to conduct and interpret tests such as the t-test and chi-square test. Techniques for constructing and interpreting confidence intervals, useful for estimating the uncertainty of measures, will also be taught.
Another important aspect of the course will be sample size determination, which is fundamental to ensuring that research results are meaningful. Students will learn to calculate the necessary sample size for various types of studies.
The course will also address the use of regression models (linear and logistic), essential tools for analyzing relationships between variables. Furthermore, students will acquire skills in survival analysis, which includes analyzing survival curves and Cox proportional hazards models.
Finally, the course will cover the implementation of multivariate models, allowing students to analyze complex data involving multiple variables simultaneously.
Course Prerequisites
No prerequisites are required for the course. The theoretical part, the practical part, and the use of software are designed for learners without a background in data analysis.
Teaching Methods
The teaching activities include lectures with a problem-solving approach and exercises/labs with applications in R to data sets, aimed at addressing specific research questions. This is designed to help students acquire the necessary skills for planning studies and conducting biostatistical analyses, while also developing their critical thinking abilities.
Assessment Methods
The exam will be held in a computer lab and will consist of a test that includes sections on epidemiology, statistics, and the history of medicine. The statistics section features 24 True/False questions, covering both problem-solving tasks and direct theoretical questions. Additionally, there are 8 True/False questions in the epidemiology section. The final score will be determined by summing the correct answers, with 0.5 points deducted for each incorrect response.
Texts
1- Bland M. Statistica Medica, Ed. Apogeo 2009
2- Whitlock M.C., Schluter D. Analisi statistica per dati biologici, Ed. Zanichelli 2010
3- La metodologia statistica nelle applicazioni biomediche , Rossi C., Serio G., Sprinter, Berlino, 1990.
In addition to the textbooks, the course materials include presentations, articles, and tutorials provided by the instructor on the website https://labstat.wixsite.com/mms-golgi (password: statistica).
The website also features a forum section with dozens of worked-out exercises and explanations that students can use to deepen their understanding of the course topics. Students can interact by asking questions on the forum in real-time.
2- Whitlock M.C., Schluter D. Analisi statistica per dati biologici, Ed. Zanichelli 2010
3- La metodologia statistica nelle applicazioni biomediche , Rossi C., Serio G., Sprinter, Berlino, 1990.
In addition to the textbooks, the course materials include presentations, articles, and tutorials provided by the instructor on the website https://labstat.wixsite.com/mms-golgi (password: statistica).
The website also features a forum section with dozens of worked-out exercises and explanations that students can use to deepen their understanding of the course topics. Students can interact by asking questions on the forum in real-time.
Contents
1. Study Design
Introduction to study designs: observational vs. experimental studies
Types of observational studies: cross-sectional, cohort, case-control
Types of experimental studies: randomized controlled trials, quasi-experimental
Bias and confounding in study design
Ethical considerations in medical research
2. Frequency Distributions
Understanding and constructing frequency tables
Graphical representations: histograms, bar charts, pie charts
Relative frequency, cumulative frequency, and their importance
Identification and interpretation of patterns and outliers in data
3. Measures of Central Tendency/Variability
Calculation of measures of central tendency: mean, median, mode
Calculation of measures of variability: range, interquartile range, variance, standard deviation
Applications and limitations of each measure
Understanding the importance of dispersion and distribution shape
4. Measures of Occurrence/Effect
Measures of occurrence: prevalence, incidence, cumulative incidence
Measures of effect: relative risk, odds ratio, attributable risk
Calculation and interpretation of these measures in medical studies
Applications in public health and clinical research
5. The R Environment: Objects and Syntax
Introduction to R and the RStudio interface
Basic objects in R: vectors, matrices, data frames, lists
Writing and executing scripts in R
Data manipulation and cleaning in R
Visualization in R: basic functions for creating graphs
6. Significance Tests
Introduction to hypothesis testing
Understanding p-values and statistical significance
Common statistical tests
Interpretation of test results and assumptions
7. Confidence Intervals
Concept of confidence intervals and their interpretation
Calculation of confidence intervals for means, proportions, and differences
Understanding the relationship between confidence intervals and hypothesis tests
Practical applications and examples
8. Study Size
Importance of sample size in research studies
Calculating sample size for various study designs: cross-sectional, cohort, clinical trials
Factors affecting sample size: effect size, power, significance level
Practical tools and software for sample size calculation
9. Regression Models
Introduction to regression analysis: purposes and types
Simple linear regression: assumptions, interpretation, diagnostics
Multiple linear regression: managing multiple predictors, interaction terms
Logistic regression: binary outcomes, odds ratios, model fitting
Practical applications in medical research
10. Survival Analysis
Introduction to survival data and censoring
Kaplan-Meier estimator and survival curves
Log-rank test for comparing survival curves
Cox proportional hazards model: assumptions, interpretation, model diagnostics
Applications in clinical studies and epidemiology
11. Multivariate Models
Introduction to multivariate analysis: purposes and types
Principal Component Analysis (PCA): dimensionality reduction
Trivariate regression models
Practical applications and examples in medical research
Each module will include theoretical lessons, practical examples, and hands-on exercises using real medical datasets. The use of the R environment will be integrated throughout the course to ensure that students gain practical experience in data analysis and visualization.
Introduction to study designs: observational vs. experimental studies
Types of observational studies: cross-sectional, cohort, case-control
Types of experimental studies: randomized controlled trials, quasi-experimental
Bias and confounding in study design
Ethical considerations in medical research
2. Frequency Distributions
Understanding and constructing frequency tables
Graphical representations: histograms, bar charts, pie charts
Relative frequency, cumulative frequency, and their importance
Identification and interpretation of patterns and outliers in data
3. Measures of Central Tendency/Variability
Calculation of measures of central tendency: mean, median, mode
Calculation of measures of variability: range, interquartile range, variance, standard deviation
Applications and limitations of each measure
Understanding the importance of dispersion and distribution shape
4. Measures of Occurrence/Effect
Measures of occurrence: prevalence, incidence, cumulative incidence
Measures of effect: relative risk, odds ratio, attributable risk
Calculation and interpretation of these measures in medical studies
Applications in public health and clinical research
5. The R Environment: Objects and Syntax
Introduction to R and the RStudio interface
Basic objects in R: vectors, matrices, data frames, lists
Writing and executing scripts in R
Data manipulation and cleaning in R
Visualization in R: basic functions for creating graphs
6. Significance Tests
Introduction to hypothesis testing
Understanding p-values and statistical significance
Common statistical tests
Interpretation of test results and assumptions
7. Confidence Intervals
Concept of confidence intervals and their interpretation
Calculation of confidence intervals for means, proportions, and differences
Understanding the relationship between confidence intervals and hypothesis tests
Practical applications and examples
8. Study Size
Importance of sample size in research studies
Calculating sample size for various study designs: cross-sectional, cohort, clinical trials
Factors affecting sample size: effect size, power, significance level
Practical tools and software for sample size calculation
9. Regression Models
Introduction to regression analysis: purposes and types
Simple linear regression: assumptions, interpretation, diagnostics
Multiple linear regression: managing multiple predictors, interaction terms
Logistic regression: binary outcomes, odds ratios, model fitting
Practical applications in medical research
10. Survival Analysis
Introduction to survival data and censoring
Kaplan-Meier estimator and survival curves
Log-rank test for comparing survival curves
Cox proportional hazards model: assumptions, interpretation, model diagnostics
Applications in clinical studies and epidemiology
11. Multivariate Models
Introduction to multivariate analysis: purposes and types
Principal Component Analysis (PCA): dimensionality reduction
Trivariate regression models
Practical applications and examples in medical research
Each module will include theoretical lessons, practical examples, and hands-on exercises using real medical datasets. The use of the R environment will be integrated throughout the course to ensure that students gain practical experience in data analysis and visualization.
Course Language
Italian
More information
Students taking the course are required to have a laptop available for the R exercises.
The instructor meets with students by appointment only, which can be arranged by sending an email to:
davide.gentilini@unipv.it
The instructor meets with students by appointment only, which can be arranged by sending an email to:
davide.gentilini@unipv.it
Degrees
Degrees
MEDICINE AND SURGERY
Single-cycle Master’s Degree (6 Years)
6 years
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