Statistics

Visualising data with ggplot2

Speaker: 
Hadley Wickham
Date: 
Fri, May 10, 2013
Location: 
PIMS, University of British Columbia
Conference: 
UBC Statistics Departmental Seminar
Abstract: 

This tutorial will introduce you to the theory and practice of ggplot2. I'll introduce you to the rich theory that underlies ggplot2, and then we'll get our hands dirty making graphics to help understand data. I'll also point you towards resources where you can learn more, and highlight some of the other packages that work hand in hand with ggplot2 to make data analysis easy.

You will have the opportunity to practice what you learn, so please bring along your laptop, with the latest version of R installed. Make sure that your version of ggplot2 is up-to-date by running install.packages("ggplot2").

To get the most out of the course, I'd recommend that you're already comfortable with R: you know how to get your data into R, you've done some graphics (base or lattice) in the past, and you've written an R function.

Class: 

Epidemiologic methods are useless. They can only give you answers

Speaker: 
Miguel Hernán
Date: 
Fri, Mar 1, 2013
Location: 
Michael Smith Laboratories, UBC
Conference: 
Constance Van Eeden Speaker
Abstract: 

The first duty of any epidemiologist is to ask a relevant
question. Learning and applying sophisticated epidemiologic methods is
of little help if the methods are used to answer irrelevant questions.
This talk will discuss the formulation of research questions in the
presence of time-varying treatments and treatments with multiple
versions, including pharmacological treatments and lifestyle
exposures. Several examples will show that discrepancies between
observational studies and randomized trials are often not due to
confounding, but to the different questions asked.

Brief Biography

Miguel Hernán is Professor of Department of Epidemiology and Department of Biostatistics at the Harvard School of Public Health (HSPH). His research is focused on the development and application of causal inference methods to guide policy and clinical interventions. He and his collaborators apply statistical methods to observational studies under suitable conditions to emulate hypothetical randomized experiments so that well-formulated causal questions can be investigated properly. His research applied to many areas, including investigation of the optimal use of antiretroviral therapy in patients infected with HIV, assessment of various interventions of kidney disease, cardiovascular disease, cancer and central nervous system diseases. He is Associate Director of HSPH Program on Causal Inference in Epidemiology and Allied Sciences, member of the Affiliated Faculty of the Harvard-MIT Division of Health Sciences and Technology, and an Editor of the journal EPIDEMIOLOGY. He is the author of upcoming highly anticipated textbook "Causal Inference" (Chapman & Hall/CRC, 2013), drafts of selected chapters are available on his website.

Class: 

Pumps, Maps and Pea Soup: Spatio-temporal methods in environmental epidemiology

Speaker: 
Gavin Shaddick
Date: 
Fri, Jan 4, 2013
Location: 
Room 2012 Earth Sciences Building
Conference: 
Constance van Eeden Invited Speaker, UBC Statistics Department
Abstract: 

Further information about the Constance van Eeden Invited Speaker Program

This talk provides an introduction to epidemiological analysis where the distribution of health outcomes and related exposures are measured over both space and time. Developments in this field have been driven by public interest in the effects of environmental pollution, increased availability of data and increases in computing power. These factors, together with recent advances in the field of spatio-temporal statistics, have led to the development of models which can consider relationships between adverse health outcomes and environmental exposures over both time and space simultaneously.

Using illustrative examples, from outbreaks of cholera in London in the 1850s, episodes of smog in the 1950s to present day epidemiological studies, we discuss a variety of issues commonly associated with analyses of this type including modelling auto-correlation, preferential sampling of exposures and ecological bias. The precise choice of statistical model may be based on whether we are explicitly interested in the spatio-temporal pattern of disease incidence, e.g. disease mapping and cluster detection, or whether clustering is a nuisance quantity that we need to acknowledge, e.g. spatio-temporal regression. Throughout we consider the practical implementation of models with specific focus on inference within a Bayesian framework using computational methods such as Markov Chain Monte Carlo and Integrated Nested Laplace Approximations.

The talk also serves as a precursor to a graduate level course on spatio-temporal methods in epidemiology. This course will cover the basic concepts of epidemiology, methods for temporal and spatial analysis and the practical application of such methods using commonly available computer packages. It will have an applied focus with both lectures and practical computer sessions in which participants will be guided through analyses of epidemiological data.

BACKGROUND INFORMATION: The Statistics Department, with the support of the Constance van Eeden Fund, is honoured to host Dr Gavin Shaddick during term 2 2012-13. Dr Shaddick, a Reader in Statistics in the Department of Mathematical Sciences at the University of Bath, has achieved international prominence for his contributions to the theory and application of Bayesian statistics to the areas of spatial epidemiology, environmental health risk and the modelling of spatio-temporal fields of environmental hazards.

Dr Shaddick will begin his visit to the Department, by giving the 2012-13 van Eeden lecture. That lecture will inaugurate a one term special topics graduate course in statistics, which the Department of Statistics is offering next term. It will be given by Dr Shaddick and Dr James Zidek (Statistics, UBC) on the subject of spatial epidemiology. This course, which is aimed primarily at a statistical audience, will provide an introduction to environmental epidemiology and spatio-temporal process modeling, as it applies to the assessment of risk to human health and welfare due to random fields of hazards such as air pollution. Please see the course outline for more information.

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