| Title: |
Advanced Statistical Methods in Epidemiology (ASME) |
| Keywords: |
Statistics
Research
Quantitative methods
Epidemiology
|
| Country: |
Germany
|
| Institution: |
Germany - Institute of International Health, Berlin
|
| Course coordinator: |
Dr. Matthias Borchert
|
| Date start: |
2025-03-17 |
| Date end: |
2025-04-04 |
| About duration and dates: |
3 weeks |
| Classification: |
advanced optional
|
| Mode of delivery: |
Face to face
|
Course location:
Charité – Universitätsmedizin Berlin
Campus Virchow-Klinikum
Augustenburger Platz 1
Berlin-Wedding
Germany |
|
| ECTS credit points: |
4.5 ECTS credits
|
SIT:
135 hours
90 contact hours (42h interactive lectures, 37h guided exercises, 5h supervised data analysis exercise, 2h revision, 4h written exam incl. feedback)45 self-study hours (10 devoted to data analysis exercise, 35h independent). |
|
| Language: |
English
|
Description:
At the end of the module, students will be able to:
• Appraise the different alternative explanations to causality, and propose ways to address them; evaluate the presence of effect modification, and propose ways to interpret and report it.
• Appraise the role of multivariable regression techniques to predict an outcome depending on several exposure variables, to assess interaction and control confounding
• Appraise why data from matched case control studies and from cluster surveys require special analysis techniques and demonstrate how to use them
• Propose an appropriate modelling strategy to select variables, identify interaction and linear trends, and relate results from multivariable analysis to those from table-based techniques
• Appraise how results from regression analysis are presented and discussed in the scientific literature.
• Perform multivariable linear and (unconditional and conditional) logistic regression analyses using the statistical software package STATA, and interpret their results. |
|
Assessment Procedures:
● Three hour written closed-book examination (multiple choice and open-ended question) at the last day of the module. Exams are marked within four weeks and communicated anonymously (using the student ID as identifier) via the learning platform/Email to the students.
The overall pass mark is 60% (converts to a German mark of “4.0” or lower) Students who fail will be offered a re-sit examination, which
should take place by the beginning of the following semester (October). Students who fail with a mark between 50% and 59% (converts to a German mark between “4.1” and “5.0”) are offered to have their Data Analysis Report (DAR) (see below) evaluated instead of re-sitting the written closed-book examination. If students achieve a “pass” in the DAR, the final grade will be a “4.0 – Sufficient”.
A second re-sit is allowed but may be linked to conditions set by the Committee of Admissions and Degrees, such as attending the course the following year again (no additional fees).
● The Data Analysis Report (DAR) is a commented
(unmarked) self-assessment. The DAR is an individual written exercise students need to submit by the end of week three. For the DAR students receive an individual data set from the course coordinator together with a number of questions that need to be answered by the DAR. Students receive a commentary on the DAR by the lecturers of the course within four weeks after the course ends. |
|
Content:
• Review: Measures of disease frequency and strength of association; inference; study designs; causality and its alternatives: random error, bias, confounding (inflation and masking), reverse causality; interaction (synergistic and antagonistic); data management with STATA; stratified analysis with STATA.
• Analysis of cluster survey data
• Simple and multivariable linear regression
• Matching in case-control studies, analysis of matched data
• Unconditional and conditional logistic regression
• Model selection and variable selection
• Role of regression techniques in data analysis
• Role of regression techniques in scientific publications
• Outlook on further regression methods (regression models for count data, regression models for survival time data)
NB: The focus of the module is on linear, and even more on logistic regression. |
|
Methods:
The course uses participatory learning, based on interactive lectures (42 hrs), guided practical exercises with or without computers (37 hours), a supervised data analysis exercise with report writing (15 hours), self-directed independent learning (35 hrs), revision and Q&A (2 hours) and a written exam including discussion of the exam (4 hrs) |
|
Prerequisites:
● Participants are required to have solid knowledge in epidemiology and biostatistics (including confounding, interaction, and stratified analysis), and should be interested in theory and practice of epidemiology.
● If not a native speaker: Internationally recognised English proficiency certificate equivalent to a TOEFL score of 550 paper/213 or 79/80 interenet based, or IELTS score 6, or DAAD (A or B in all categories).
● tropEd students need to provide proof of registration as tropEd student at their home institution only.
● Students need to have a laptop. |
|
Attendance:
Max 24 students .
Students and participants must attend 80% of the teaching time. |
|
Selection:
Participants are selected on a first come first served basis.
Deadline for application: 8 weeks before module start.
Deadline for payment: 4 weeks before module start.
We shall confirm the module 6 weeks before the module starts which is subject to a sufficient number of applications (minimum 8).
Late applications will be considered as long as places are available.
Applications forms can be found here:
https://internationalhealth.charite.de/en/courses_modules/advanced_modules/ |
|
Fees:
1.575,00 EUR for tropEd MScIH students and alumni.(For Berlin students who started their studies in 2022 or earlier the old scale of fees applies. Please see here)
1.968,75 EUR for others. Consider 50 to 200 EUR for a STATA licence. |
|
|
|
Major changes since initial accreditation:
● Given the complexity of the issues addressed in this module we continue to devote a substantial amount of time (2.5 days) to the revision of content covered for a first time in ITMIH’s core course.This revision has been found to be helpful, even necessary for many students.
● The content of the module has been revised with the view to emphasise content that many students tend to need for epidemiological MScIH thesis projects, and to deemphasize other advanced content. Therefore, the analysis techniques
for cluster survey data and data from matched case control studies have been added, while standardisation has been eliminated. Linear and logistic regression continue to be
taught, the latter quite extensively given its importance in epidemiological research, while Poisson regression has been eliminated, as few students will analyse data from open cohort studies as MScIH thesis project.
● Overall, the emphasis has moved away from mathematical background and formulae, in favour of understanding when to use which technique, how to use it, and how to interpret its result. This shift means to take into account the
background and interests of the majority of students.
● In response to student feedback about the high density of the two-week module and the lack of time to practice new skills, we have decided to add a third week, during which students analyse data sets under supervision. Little new
content is being offered in the third week, which is predominantly devoted to consolidate what has been learned in week one and two.
● Dr Matthias Borchert (formerly ITMIH and Robert Koch
Institute) took over as course coordinator in 2012. Since then, co-lecturers have come from the Antwerp Institute of Tropical Medicine and the University of Liverpool. |
|
Student evaluation:
Overall student evaluation has been very good to excellent. Students were especially pleased with the combination of theory and extensive practical exercises. Students demanded more time to practice data analysis under supervision, and a revision session at the end of the module. |
|
Lessons learned:
The course has in most years experienced high demand and interest. Most students have limited interest in statistical theory but are keen to acquire knowledge and skills they can use to
analyse epidemiological data and to critically read scientific
literature where results from multivariable analyses are presented. In the context of ITMIH’s MScIH this advanced module should prepare students to conduct an epidemiological thesis project with competence and confidence. |
|
tropEd accreditation:
Accredited in Marseille in 2004, re-accredited in Mexico in May 2010, in Uppsala in September 2012 and in December 2017, in Hamburg in September 2022. Accreditation is valid until September 2027. |
|
Remarks:
Students are expected to bring a calculator without internet connectivity (i.e. not a smartphone etc.)to the teaching sessions and the written exam. We recommend the following
calculator: Casio fx-115MS or equivalent.
Recommended textbooks:
For this advanced module: Campbell MJ, 2006. Statistics at Square Two. 2 nd edition. London: BMJ Books. ISBN-13: 978-1405134903
For reviewing relevant core course content: Campbell MJ 2021. Statistics at Square One. 12 th edition. London: BMJ Books. ISBN: 9781119401308 (paperback) |
|
| Email Address: |
mscih-student@charite.de |
| Date Of Record Creation: |
2011-11-16 04:14:39 (W3C-DTF) |
| Date Of Record Release: |
2011-11-16 05:20:47 (W3C-DTF) |
| Date Record Checked: |
2018-06-27 (W3C-DTF) |
| Date Last Modified: |
2024-09-24 09:41:28 (W3C-DTF) |
