The course covers the scope of epidemiology, basic concepts and principles, and major study types.

The aim of the course is to write good abstracts of study plans and results, and also press releases. The maximum number of students is 12. In order to obtain 1 ect there is a written pre-assigment and active participation in a course. Pre-assigments are discussed during the course.

Intermediate course in practical aspects of study design and application of epidemiologic methods.

The course covers fundamental concepts and methods in epidemiologic research, including planning and conducting epidemiologic studies, design strategies, disease occurrence and exposure measurement, statistical associations, bias, confounding, effect modification, causality, and different epidemiological study designs.

Prerequisites Previous knowledge about basic epidemiological concepts and methods.

Participation in the lectures, completion of learning diary

:This course covers the concepts and methods of vaccine-preventable disease epidemiology and the science of vaccinology. The course can be taken independently or in combination with the Essentials of Infectious disease epidemiology course ( 2 ECTS).

If you are interested in taking this course, please contact Kirsi Lumme-Sandt, kirsi.lumme-sandt@uta.fi. 

An introduction to epidemiologic data analysis using the statistical programme R. Course will cover basic statistical inference of the fundamental epidemiologic study designs. An introduction to the some more advanced methods concerning statistical analysis of dependent observations and long-term survival analysis are also presented.

The cancer epidemiology course consists of several themes such as: cancer registration and coding, prevention, differences in incidence and mortality, prediction and survival, radiation and cancer, work and cancer, screening of cancer.

The aim of the course is to be able to have basic knowledge of cancer as a disease, how it is registered and coded, what kind of preventive and early diagnostics are in use, and how to use register data in research and in policy making.

If you are interested in taking this course, please contact Liina-Kaisa Tynkkynen, liina-kaisa.tynkkynen@uta.fi. 

An introduction to epidemiologic data analysis using the statistical programme R. Course will cover basic statistical inference of the fundamental epidemiologic study designs. An introduction to the some more advanced methods concerning statistical analysis of dependent observations and long-term survival analysis are also presented.

The first day offers a series of exercises and points to reflect on, whereas the second day offers the students an opportunity to apply these tips and strategies in an "Editing Clinic". During the second day, the students will be taught a ten-step editing process that they will apply to their own texts in class.

For the second day of the workshop, students are asked to bring one copy of a well-written article in their fields and 5 copies of a 2-3 page sample of their own writing (unedited, unpublished). Please make sure that the writing sample is double spaced.

The cancer epidemiology course consists of several themes such as: cancer registration and coding, prevention, differences in incidence and mortality, prediction and survival, radiation and cancer, work and cancer, screening of cancer.

The aim of the course is to be able to have basic knowledge of cancer as a disease, how it is registered and coded, what kind of preventive and early diagnostics are in use, and how to use register data in research and in policy making.

Epidemiology, prevention and control of chronic, non-communicable diseases such as diabetes and cardiovascular diseases; nutritional epidemiology (e.g. measurement of dietary intake in epidemiologic studies).

Students who will complete all parts of the course will earn 4 ECTS. Students who complete other parts of the course except the seminar in Helsinki (Part IA) will earn 3 ECTS.

There will be some reading or other homework before a part of the lectures and practicals. The students will keep a 2-day dietary record before the last practical.

If you are interested in taking this course, please contact Liina-Kaisa Tynkkynen, liina-kaisa.tynkkynen@uta.fi. 

Decisions in healthcare and policy depend on the highest available evidence. Systematic reviews and meta-analyses rank highest in the evidence hierarchy in informing such important decisions. Over the last decade, their use in different fields of inquiry has rapidly grown in length and breadth. In this course, participants will be taken through the rudiments of conducting systematic reviews and meta-analyses, covering all stages of the review process, including question formulation, study identification, data extraction, quality appraisal, meta-analysis, and many more. Tutoring is participatory, with hands-on practical sessions that allow participants to implement the skills learned. The course is suitable for postgraduate students and researchers from any field of research.

Contents:
- Formulating a research question
- Defining inclusion and exclusion criteria
- Developing a search strategy and locating primary studies
- Selecting studies and assessing their quality
- Performing fixed-effect and random-effects meta-analyses
- Exploring heterogeneity across studies
- Assessing publications bias
- Performing sensitivity analyses

Course outline

7th May 2018

8.30-16.30

Introductions and principles of systematic reviews

8:30-9:00       Introductions and general overviews

9:00-10:00     Rationale for systematic reviews

10:00-10:15   Coffee break

10:15-11:00   Question formulation for a systematic review

11:00-12:00   Lunch break

12:00-13:30   Planning and performing searches: search strategies and databases

13:30-15:00   Study screening, data extraction, and critical appraisal

15:00-15:15   Coffee break

15:15-16:30   Practical session: protocol development and group work

8th May 2018

8.30-16.30

Meta-analysis

8:30-9:30       Practical session: group work

9:30-10:00     Review of Day 1

10:00-10:15   Coffee break

10:15-11:30   Principles of meta-analysis

11:30-12:30   Lunch break

12:30-13:30   Fixed-effect versus random-effects models in meta-analysis

13:30-14:30   Heterogeneity and publication bias in meta-analysis

14:30-14:45   Coffee break

14:45-15.30   Sub-group analysis, meta-regression, and sensitivity analysis

15:30-16:30   Practical session: planning and conducting a meta-analysis

9th May 2018

8.30-15.00

Continuation of meta-analysis and group presentations

8:30-10:30     Practical session: group work

10:30-10:45   Coffee break

10:45-11:15   Review of Day 2

11:15-12:15   Lunch break

12:15-13:00  

Programme

Thursday 26.4.

8.00                     Departure from Tampere

9.00                     Breakfast

9.45                    Welcome, orientation and student presentations
Pekka Nuorti and Kirsi Lumme-Sandt

12.15                  Lunch

13.00                  “Writing Scientific Journal Articles” / Dr Pekka Belt

Dr Pekka Belt currently works in the group for improving the efficiency of postgraduate studies (GIEPS).

GIEPS serves the needs of postgraduate students guiding them in writing doctoral theses and scientific Journal articles in the University of Oulu.

15.00                  coffee and tea break

15.15                  “Writing a thesis” / Dr Pekka Belt

17.30                  Activities

18.30                  Sauna

20.00                  Dinner

Friday 27.4.

7.00 – 9.00       Breakfast

9.00 – 10.15     From health promoting interventions to ‘eco-chic’ veganism

– Sociological analysis in action
Professor Piia Jallinoja, University of Tampere

10.30 – 11.30  Science & media
                             Professor Piia Jallinoja

11.30 -12.30    Group work on science & media – seminar participants’ perspectives and ideas

12.30                  Lunch

13.30                  Group work wrap up

14.30                  Coffee / tea, Feedback and closing words

15.00                  Departure to Tampere

This intensive, 4-day seminar course will introduce a variety of practical analytical approaches for addressing threats to validity in observational studies. Specific topics will include the limitations of traditional hypothesis testing in observational epidemiology, modern approaches to detecting and attenuating confounding, probabilistic correction for variable misclassification, and handling of missing data. These topics will be taught as non-technically as possible, but references and programming resources will be provided to allow for rigorous application of the methods. Throughout the course, emphasis will be placed on how to successfully incorporate these methods into real-world studies and subsequent publications. Examples using SAS R and Microsoft Excel will accompany many of the lectures. Students are expected to have a solid foundation in epidemiologic study design and biostatistics, including the interpretation of linear, logistic, and proportional hazards regression models, and will be asked to read two to three papers each night in preparation for the next day’s class. Examples using SAS R will focus on demonstrating the mechanics of implementing the methods, and will not assume familiarity with SAS R syntaxprogramming. Equivalent functions in Stata and R SAS will be pointed outdiscussed whenever applicable. Lecture content will be reinforced by afternoon laboratory sessions, in which students can apply the methods taught learned in lecture to the analysis of simulated data sets.

Instructor: Thomas Ahern, PhD, MPH, University of Vermont, U.S.A.

Dr. Ahern studies the pharmacoepidemiology of breast cancer using the U.S. Nurses’ Health Study cohorts and the population-based medical registries of Denmark. He has lectured extensively in beginner, intermediate, and advanced courses in epidemiologic methods, and is experienced in the application of modern analytical concepts to practical research. Dr. Ahern received his PhD in epidemiology from the Boston University School of Public Health, completed a post-doctoral fellowship in cancer epidemiology at the Harvard School of Public Health, and is currently an assistant professor at the University of Vermont College of Medicine.