Stat547L: Spatiotemporal methods in environmental epidemiology,
2012/13 – Term 2
Instructors: Dr Gavin Shaddick and Prof. Jim Zidek
Dr Gavin Shaddick
Department of Mathematical Sciences,
University of Bath
Email: [email protected]
Dr Jim Zidek
Department of Statistics,
University of British Columbia
Email: [email protected]
Description: This graduate level course covers methods used in environmental
epidemiology where the distribution of health outcomes and related exposures are
measured over both space and time. The course starts with an introduction to the basic
concepts of epidemiology and covers cases where risks vary over time and space.
Consideration will be given to both temporal methods, where the investigation is of
risks to health over time in a single area, and spatial methods, where the emphasis is
on changes in risk over space within a given time period. However, the scale and
complexity of modern investigations means there is a need to consider relationships
over both time and space simultaneously and therefore a requirement for sophisticated
methods whose development is an active area of risk in statistical science.
Outline: The main topics to be covered include:
 Types of epidemiological studies; cohort, case-control, ecological
 Measures of risk; relative risks, odds ratios, absolute risk, sources of bias,
assessing uncertainty.
 Bayesian statistics and computational techniques; Markov chain Monte Carlo
(MCMC), Integrated nested Laplace approximations (INLA)
 Regression models in epidemiology; Logistic and Poisson generalised linear
models, generalised additive models, hierarchical models, measurement error
 Temporal models; time series models, temporal auto-correlation, smoothing
 Spatial models; point processes, area and point referenced methods, mapping,
geostatistical methods, spatial regression, non-stationary models, preferential
 Spatial-temporal models; separable models, non-separable models, modelling
exposures in space and time, correction for ecological bias.
Throughout the course, there will be a strong emphasis on the implementation of
models in practice and the application of the methods will be demonstrated by
example using the commonly available computer packages, including R, OpenBUGS
geoR, gstat and INLA.
Prerequisites: The course assumes a solid background in statistics, although the
required material will be briefly reviewed and graduate students in Statistics would
qualify. There will be no assumption of a background in epidemiology. If you are
unsure, contact an instructor by email with your (unofficial) graduate and
undergraduate transcripts attached.
Lab: Subject to the availability of space and the necessary support, we will have a
laboratory program of hands-on study. This will include an introduction to the
statistical packages with lab assignments based on them.
Evaluation: Final grades will be based on assignments, laboratory exercises, and
Textbook:Various options are under consideration, but none has been selected as the
outline was being prepared.
Lawson A. (2006) Statistical Methods in Spatial Epidemiology (2nd edition). Wiley
Thomas, D. (2009) Statistical Methods in Environmental Epidemiology. Oxford
University Press
Rothman K.(2012) Epidemiology: An Introduction (2nd edition). Oxford University
Rothman K, Greenland S, Lash T. (2008) Modern epidemiology (3rd edition).
Lippencott, Williams & Wilkins
Ward H, Toledano M, Shaddick G, Davies B, Elliott P. (2012) Oxford Handbook of
Epidemiology for Clinicians. Oxford University Press.
Spatial and temporal statistics:
Banerjee S, Gelfand A, Carlin P. (2005) Hierarchical Modeling and Analysis for
Spatial Data. Chapman & Hall/CRC.
Cressie N, Wilke C. (2011) Statistics for spatio-temporal data. Wiley.
Diggle P. (2002) Statistical analysis of spatial point pattern (2nd edition) Hodder
Diggle P, Ribeiro P. (2007) Model-based geostatistics. Springer.
Le N, Zidek J. (2006) Statistical Analysis of Environmental Space-Time Processes.
Schabenberger O, Gotway C. (2004) Statistical Methods for Spatial Data Analysis .
Chapman & Hall/CRC.