Training and Research

PhD Programme Courses/classes - 2024/2025

This page shows the PhD course's training activities for the academic year 2024/2025. Further activities will be added during the year. Please check regularly for updates!

Instructions for teachers: lesson management

PhD school courses/classes - 2024/2025

Please note: Additional information will be added during the year. Currently missing information is labelled as “TBD” (i.e. To Be Determined).

1. PhD students must obtain a specified number of CFUs each year by attending teaching activities offered by the PhD School.
First and second year students must obtain 8 CFUs. Teaching activities ex DM 226/2021 provide 5 CFUs; free choice activities provide 3 CFUs.
Third year students must obtain 4 CFUs. Teaching activities ex DM 226/2021 provide 2 CFUs; free choice activities provide 2 CFUs.
More information regarding CFUs is found in the Handbook for PhD Students: https://www.univr.it/phd-vademecum

2. Registering for the courses is not required unless explicitly indicated; please consult the course information to verify whether registration is required or not. When registration is actually required, instructions will be sent well in advance. No confirmation e-mail will be sent after signing up. Please do not enquiry: if you entered the requested information, then registration was silently successful.

3. When Zoom links are not explicitly indicated, courses are delivered in presence only.

4. All information we have is published here. Please do not enquiry for missing information or Zoom links: if the information you need is not there, then it means that we don't have it yet. As soon as we get new information, we will promptly publish it on this page.

Summary of training activities

Teaching Activities ex DM 226/2021: Linguistic Activities

Teaching Activities ex DM 226/2021: Research management and Enhancement

Teaching Activities ex DM 226/2021: Statistics and Computer Sciences

Teaching Activities: Free choice

Credits

1.5

Language

English - Inglese

Class attendance

Free Choice

Location

VERONA

Learning objectives

The course aims to provide the theoretical and practical tools for the analysis of survival data in human populations and the associated risk factors, i.e. skills in the field of epidemiology, biostatistics and computer science applied to the analysis of biomedical data (programming syntax of a statistical software).
The course is structured in theoretical and practical lessons in the computer lab (12h) on the use of a statistical software (STATA) for the analysis of survival data. The teaching material (slides of the theoretical lessons, STATA files used for the practical lessons) is made available to the students on the e-learning web page of the course (Moodle platform).

Prerequisites and basic notions

The course requires basic knowledge of descriptive statistics, inferential statistics and probability theory. There are no preparatory courses.

Program

- General concepts of survival analysis: endpoint, survival time, survival function, hazard function.
- Univariable analysis: Kaplan-Meier estimator, median survival time, Mantel-Haenszel test.
- Multivariable analysis: Cox regression model.

Bibliography

Visualizza la bibliografia con Leganto, strumento che il Sistema Bibliotecario mette a disposizione per recuperare i testi in programma d'esame in modo semplice e innovativo.

Didactic methods

The course is structured in theoretical and practical lessons in the computer lab (12 hours) on the use of a statistical software (STATA) for the analysis of survival data. The teaching material (slides of the theoretical lessons, STATA files used for the practical lessons) is made available to the students on the e-learning web page of the course (Moodle platform).

Learning assessment procedures

There is no exam test. Attendance at all lessons is required to acquire course credits.

Students with disabilities or specific learning disorders (SLD), who intend to request the adaptation of the exam, must follow the instructions given HERE

Assessment

There is no exam test.

Criteria for the composition of the final grade

There is no exam test.

Course lessons
PhD Schools lessons

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Guidelines for PhD students

Below you will find the files that contain the Guidelines for PhD students and rules for the acquisition of ECTS credits (in Italian: "CFU") for the Academic Year 2023/2024.

Documents

Title Info File
File octet-stream Annual credit sheet octet-stream, it, 20 KB, 05/04/24
File pdf Basic expected outcomes pdf, en, 131 KB, 05/04/24
File pdf Competenze attese pdf, it, 129 KB, 05/04/24
File pdf Prodotti minimi attesi pdf, it, 126 KB, 05/04/24
File pdf Specific learning outcomes pdf, en, 133 KB, 05/04/24