Master Class: Advances in Epidemiologic Analysis

In this Master Class timely topics in study design of epidemiologic and clinical studies will be addressed. Four renowned faculty members will address advanced study design issues in a seminar format.

 

Moderator Prof. Albert Hofman

  • Kenneth-Rothman

    Monday August 11, 2014

    The Growing Rift Between Epidemiologists and Their Data

    In former times, epidemiologists customarily published their data in a way that showed the distributions of key variables and their associations.  Those days are mostly gone.  From earlier papers, readers could see enough detail in the data to conduct their own analyses.  This approach kept everyone informed and careful.  Nowadays, it is usual for epidemiologists to present only derived results, typically from regression models, that cannot be checked from the published data.  This gulf between epidemiologists and their data is conducive to publishing mistakes and obscures important information that readers should have.

    Prof. Kenneth J. Rothman
    Distinguished Fellow and VP for Epidemiology Research, RTI Health Solutions, Research Triangle Park, North Carolina, USA and Professor of Epidemiology Boston University School of Public Health, Boston, MA, USA

  • Matthias-Egger

    Tuesday August 12, 2014

    Big epidemiology: too big to fail?

    Prof. Matthias Egger
    Professor of Epidemiology and Public Health and Director, Inst. Social and Preventive Medicine (ISPM), Univ. Bern, Switzerland; visiting Professor of Clinical Epidemiology, Dept. of Social Medicine, Univ. Bristol, UK; visiting Professor of Epidemiology, School of Public Health, Univ. Cape Town, South Africa

  • Zoltan-Voko

    Wednesday August 13, 2014

    Change as a determinant, determinants of change.

    Studying whether change depends on the initial value and the effect of change is of general interest in epidemiological studies with repeated measurements. Simple analyses of these data are often biased because of regression-to-the-mean.

    The phenomenon will be presented together with typical examples from the literature. Simple and more advanced methods to correct for the effect of regression-to-the-mean will be proposed.”

    Dr. Zoltán Vokó
    Associate professor, deputy head of institute
    Department of Health Policy & Health Economics
    Institute of Economics, Faculty of Social Sciences
    Eötvös Loránd University, Budapest, Hungary

  • MichelleWilliams

    Thursday August 14, 2014

    The Case-Crossover Design: Applications in Perinatal Epidemiology Research

    The case-crossover study is designed to answer the question “Was event X triggered by something unusual that happened just before?” The key feature of the design is that each case serves as its own control. The method is analogous to a crossover experiment viewed retrospectively, except that the investigator does not control when a patient starts being exposed to a potential trigger. The case-crossover study can also be compared with a traditional matched-pair case-control study. In both types of studies, each case has a matched control. In a traditional matched-pair case-control study, the control is a different individual at a similar time. In the matched-pair case-crossover design, the control is the same person at a different time. Decades of applied research have shown that the study design applies best when the exposure is intermittent, the effect of risk is immediate and transient, and the outcome is abrupt. Using the case-crossover design, investigators have been able to assess the extent to which episodes of physical exertion, anger, sexual activity, cocaine use, and alcohol consumption are triggers of myocardial infarctions. Others have applied the method to study triggers of other acute events including car crashes, injury, and adverse drug reactions. A placental abruption, for the most part, is an abrupt event that typically occurs in mid-to-late pregnancy; and is amenable for study using the case-crossover design.

    Prof. Michelle A. Williams
    Professor and Chair of Epidemiology
    Harvard School of Public Health
    Boston, MA, USA

Admission is free, without registration for:

  • Participants ESP 2014
  • Students NIHES programmes
  • Employees Erasmus MC
  • Public at large


Course code:
ESP64

Date: August 11 – 14, 2014

Time:
16:00 – 17:00

Course days:
Monday to Thursday

Location: Erasmus MC, Education Center

No course material