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 Modelling and Analysis (SACEMA). The course will take place in Stellenbosch from 21-23 May 2014.
The course fee includes a copy of the recent book on this subject by Lash, Fox and Fink. The fee, including refreshments, lunches and social events, is R3500 for early bird registration by 28 February 2014 and R4400 for later registration. Of this, R500 is a non-returnable registration fee. For international participants, the course fee is 400 euros for early bird registration, and 450 euros for late registration. Of this, 50 euros is a non-returnable registration fee.
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 the bias in discussion section of manuscripts and can be a powerful tool for quantifying the impact of such biases.
Based on the book co-authored by Dr Fox*, this 3 day workshop will cover simple and multidimensional bias analysis methods that can be used to gain a better understanding of the impact of unmeasured confounding, selection bias and misclassification (measurement error) on study results. These methods can be applied to nearly any dataset, even summary data presented in the literature. Such approaches lay the foundation for more complicated methods, but by themselves, they act as if the bias parameters are known with certainty. We will then continue with probabilistic bias analysis which requires specification of probability distributions about the bias parameters and then uses Monte Carlo simulations methods to create intervals accounting for the uncertainty in the systematic error. Finally we will finish with methods for combining the systematic error to create simulation intervals that account for the total error (systematic and random) in the study results.
* Lash TL, Fox MP, Fink A. Applying Quantitative Bias Analysis to Epidemiologic Data Springer 2009.
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