Joint RSS Highland Group - St Andrews meeting

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RSS local group meetings

Wednesday 18 April 2018, 02:00pm - 04:20pm

Location Lecture Theatre C, Physics Building, North Haugh, St Andrews

2.00pm -2.05pm                      Welcome
2.05pm – 2.55pm                    Marta Blangiardo (Imperial College London)
2.55pm – 3.30pm                    Coffee Break
3.30pm - 4.20pm                     Thordis L. Thorarinsdottir (Norwegian Computing Centre, Oslo)
Marta Blangiardo (Imperial College London)
A data integration approach for improving inference in area-referenced environmental health studies (joint work with Monica Pirani, Alexina Mason, Anna Hansell, and Sylvia Richardson) 
Study designs where data are aggregated into geographical areas are extremely popular in environmental epidemiology. These studies are commonly based on administrative databases and, providing a complete spatial coverage, are particularly appealing to make inference on the entire population. The ecological nature of these studies, however, does not allow the direct inclusion of individual-level risk factor data. In the presence of unmeasured potential confounding factors, risk effect estimates are prone to bias. Here, we show how to improve inference drawn from area-referenced environmental health-effect studies, proposing a Bayesian approach that augments measured area-level covariates with an ecological propensity score estimated upon individual-level data from sample surveys. This scalar index acts as a proxy for the unmeasured ecological confounders and represents a useful tool for overcoming the problem due to the incomplete spatial coverage of the individual-level data. In contrast to the main literature on propensity score for confounding adjustment where the exposure of interest is confined to a binary domain, we generalize its use to cope with ecological studies characterised by a continuous exposure. The approach is illustrated using simulated examples and a real application investigating the risk of lung cancer mortality associated to nitrogen dioxide in England (UK).
Thordis L. Thorarinsdottir (Norwegian Computing Centre, Oslo)
Does Bayes beat squinting? Bayesian modelling of cluster point process models
Point process data arises naturally in various fields of science such as biology, ecology, epidemiology, and environmental sciences. However, the point process modelling framework is very involved and inference can often only be performed approximately and with great care. At the same time, a great number of different models are available where the subtle differences between the individual models can be hard to detect. In this talk, we discuss to which extend Bayesian modelling approaches may be applied to the class of cluster process models.  Cluster point processes have the following general structure. There is a point process of cluster centers and to each cluster center is associated a random number of points forming a subsidiary process, where the points in the subsidiary process are distributed about the cluster center in some specific way. A model for a cluster point process thus consists of three components; a component describing the cluster center process, a component describing the cluster sizes, and a component describing the distribution of the subsidiary points around the cluster center, the dispersion process. We consider how Bayesian approaches may be used to perform inferences for all three components, a feat which is often not possible using other inference methods

Organiser Name Lorna Aucott

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Organising Group(s) RSS Highlands Local Group





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