EPI 271, propensity score methods
Jan
22
to Jan 25

EPI 271, propensity score methods

  • Harvard T.H. Chan School of Public Health (map)
  • Google Calendar ICS

Description
This course introduces basic and advanced theory underlying propensity score analyses and provides practical insights into the conduct of studies employing the method. Course readings will include propensity score theory as well as applications. Lectures are complemented by computer lab sessions devoted to the mechanics of estimating and using the propensity score as a tool to control for confounding in observational research. Students should have knowledge in multivariable modeling approaches. A course project will involve the application of propensity scores to a data set or the review of a related, published paper.Course Activities: Lectures, readings, homeworks, computer labs, participation, project. HSPH Course Prerequisite(s): EPI204 or EPI236 or EPI 522 or BST210 or BST213; may not be taken concurrently.

Prerequisite(s)
HSPH Prerequisite: EPI204 or EPI236 or EPI 522 or BST210 or BST213; may not be taken concurrently. Students outside of HSPH must request instructor permission to enroll in this course

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Advanced Epidemiologic Methods – causal research and prediction modeling
Aug
20
to Aug 24

Advanced Epidemiologic Methods – causal research and prediction modeling

Lecturers: Rolf H.H. Groenwold, MD, PhD, Leiden University Medical Center (NL) & 
Maarten van Smeden, PhD, Leiden University Medical Center (NL)

By the end of this week, participants should be able to:

  • Critically assess the results of epidemiological studies on causal relationships or prediction models
  • Correctly define exposures and learn how to best represent them in models
  • Understand difference between various sources of bias (confounding, measurement error and missing data) and the way these biases may differentially affect studies on causal relationships and prediction models.
  • Describe key assumptions of methods used to control for (time-varying) confounding.
  • Describe key assumptions of methods used to handle missing observations.
  • Understand the reasons for and consequences of overfitting prediction models
  • Describe recent developments in the fields of causal research and prediction modelling

Prerequisites:

  • Basic knowledge of epidemiology
  • Familiarity with R statistical software (for a short introduction see http://www.r-tutorial.nl/)

Fees:

  • 750 €
  • 510 € for enrolled students (proof required)

3 ECTS

Registration Information:
Tanja Te Gude
Tel. +49 30 450 570 812

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Jahresabschlussfeier der Berlin School of Public Health
Jun
8
5:00 PM17:00

Jahresabschlussfeier der Berlin School of Public Health

  • Charité Campus Mitte, Hörsaal Innere Medizin Südflügel (map)
  • Google Calendar ICS

PROGRAMM

17:00
Begrüßung
Prof. Dr. Reinhard Busse Technische Universität Berlin

17:15
Keynote-Lecture
Prof. Dr. Eva Inés Obergfell
Vizepräsidentin für Lehre und Studium der Humboldt-Universität zu Berlin

17:45
Rückblick, Ausblick, Würdigung
Dr. Nina Adelberger
Charité – Universitätsmedizin Berlin

18:00
Laudatio und Verleihung Berlin School of Public Health-Preis
Prof. Dr. Dr. Tobias Kurth
Charité – Universitätsmedizin Berlin

18:30
Get-together

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"The Complexity of Clinical Trials in the Prevention of Cardiovascular Disease: An Investigator's Perspective"
Apr
17
4:00 PM16:00

"The Complexity of Clinical Trials in the Prevention of Cardiovascular Disease: An Investigator's Perspective"

Prof Julie Buring, Prof. Dr. Dr. Tonias Kurth

Randomized clinical trials are more logistically difficult, more expensive, and need to consider more ethical issues than any other epidemiologic design strategy – yet if well designed and conducted, randomized trials can provide the highest level of assurance about the effect of the intervention itself. This lecture will address the complexity of randomized trials of prevention from an investigator’s standpoint, in the context of the evaluation of aspirin in the primary prevention of cardiovascular disease.

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Propensity Score Analysis: Theoretical & Practical Considerations
Jan
16
to Jan 19

Propensity Score Analysis: Theoretical & Practical Considerations

  • Harvard T.H. Chan School of Public Health (map)
  • Google Calendar ICS

This course introduces basic and advanced theory underlying propensity score analyses and provides practical insights into the conduct of studies employing the method. Course readings will include propensity score theory as well as applications. Lectures are complemented by computer lab sessions devoted to the mechanics of estimating and using the propensity score as a tool to control for confounding in observational research. Students should have knowledge in multivariable modeling approaches. A course project will involve the application of propensity scores to a data set or the review of a related, published paper.Course Activities: Lectures, readings, homeworks, computer labs, participation, project.

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