Using quantitative bias analysis with epidemiologic data: Short course at SACEMA, 13-15 May 2019. Register now!

Posted on Wed, Mar 13 2019 15:42:00

Introduction

Dr. Matthew Fox of the Department of Epidemiology and the Center for Global Health and Development at Boston University will be presenting an intensive three-day course on using quantitative bias methods with epidemiological data, at Stellenbosch University under the auspices of the South African DST/NRF Centre for Epidemiological Modeling and Analysis (SACEMA). The course will take place from 9 am to 4 pm daily, 13 – 15 May 2019, in the STIAS Library, adjacent to SACEMA.

The course fee, including refreshments, lunches and social events, is R5500 for early bird registration by 31 March 2019, and R6500 for later registration. Accommodation, breakfast and dinner is not included in the course fee, but information about accommodation packages may be obtained from SACEMA.

Click here for the Flyer.

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Course overview:

Students of epidemiology are well versed in ways to reduce systematic error (bias) in the design of their studies and to describe random error in the analysis of their studies through confidence intervals and p values. However, students are rarely taught methodologies for quantifying systematic error in their studies.

Quantitative bias analysis (QBA) provides a methodology for assessing the impact of bias on study results by making assumptions about the bias parameters. QBA allows for assessment of both the direction and magnitude of systematic error and gives an estimate of effect (or a series of estimates of effect) that would have occurred had the bias been absent, assuming the bias parameters are correct. Such analyses allow investigators to go beyond speculation about bias in discussion section and can be a powerful tool for quantifying the impact of such biases.

 

Course details:

The emphasis of this course will be on the basic methodologies and on applying the methods to data. We will not emphasize statistical theory, but rather the simple mathematics required to correct for study errors. All aspects of quantitative bias analysis, including probabilistic bias analysis, will be demonstrated using freely available software in Microsoft Excel. A few examples in later sessions will be demonstrated in SAS only to allow participants to understand ways to continue with their training in QBA. However we do not expect students to be proficient in SAS. Examples can be translated to STATA by participants after the course. All participants are expected to bring a laptop with Microsoft Excel. It is preferable if students bring a dataset with them that has sources of random error.

Enquiries may be directed to the SACEMA Research Manager, Ms Lynnemore Scheepers at: scheepersl@sun.ac.za, or SACEMA Training Coordinator, Mr Masimba Paradza at: mwparadza@sun.ac.za (and copy to Matthew Fox at: mfox@bu.edu ).