Royal Statistical Society
Highlands Local Group

Local organising Committee


Dr Claus Mayer (Chair)
Biomathematics & Statistics Scotland
Aberdeen AB25 2ZD
Dr David McLernon (Hon Secretary)
Polwarth Building,
Aberdeen AB25 2ZD 
Phone: +44 (0) 1224 438652 Phone: +44 (0) 1224 437152  

Dear All

Here are details about the talks planned for our next Joint Meeting with St Andrews


Wednesday, 18th of April 2018 - Joint RSS Highland group- St Andrews meeting.


Venue: Lecture Theatre C, Physics building, North Haugh.




2.00pm -2.05pm                      Welcome


2.05pm – 2.55pm                    Marta Blangiardo (Imperial)

2.55pm – 3.30pm                    Coffee Break


3.30pm - 4.20pm                     Thordis L. Thorarinsdottir (Norwegian Computing Centre, Oslo)




Speaker’s biographies


Marta Blangiardo is a senior lecturer in Biostatistics at the Department of Epidemiology and Biostatistics, Imperial College London (UK) affiliated with the MRC-PHE Centre for Environment and Health (  Her main research interest is the development of Bayesian hierarchical models, especially in relation to the use of multiple data sources, with application in a wide range of areas, including genetics, environmental science, and epidemiology. Recently she has been working on spatio-temporal epidemiological surveillance for chronic diseases, confounder adjustment in ecological studies of environmental epidemiology and models for multi-pollutant exposure in a time series framework.



Title: 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)

Abstract: 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 is a statistician working within the fields of spatial and space-time statistics, Bayesian methods and forecasting. She is a Chief Research Scientist in the Statistical Analysis, Machine Learning, and Image Analysis (SAMBA) group at the Norwegian Computing Center in Oslo, Norway. Her current work focuses on environmental applications to solve problems on uncertainty quantification, probabilistic prediction and model evaluation in collaborations with climate ecologists, atmospheric scientists and hydrologists.


Title: Does Bayes beat squinting? Bayesian modelling of cluster point process models

Abstract: 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

We are delighted to announce that Professor Deborah Ashby, Chair in Medical Statistics and Clinical Trials at Imperial College London (and RSS President in waiting), will be visiting us at the University of Aberdeen in April. Deborah has kindly agreed to deliver a talk for the RSS Highlands Local Group. The details are below and the title will be announced in due course.

Date: Wednesday 11th April 2018

Speaker: Deborah Ashby, Chair in Medical Statistics and Clinical Trials at Imperial College London

Time: 3pm for 3.30pm start

Venue: Room 115, Health Sciences Building, Foresterhill, UoA, Aberdeen

Title: Better benefit-risk decision-making in the regulation of medicines: new opportunities for statistical and data sciences

Abstract: Until recently, assessment of the benefit risk balance for a medicine has been entirely informal, but there is now growing interest among drug regulators and pharmaceutical companies in the possibilities of more formal approaches to benefit-risk decision-making, including those which explicitly take patient perspectives into account. Pharmacoepidemiological Research on Outcomes of Therapeutics by a European Consortium (PROTECT) was a project funded under the Innovative Medicines Initiative as a collaboration between academic, pharmaceutical, regulatory and patient organizations. Based on work from PROTECT we review current methodological approaches, and illustrate them with the case-studies where benefit-risk is finely balanced. We will introduce the PROTECT Benefit-Risk Roadmap, designed to help those wishing to find their way through this evolving arena (, and highlight recent statistical developments and current challenges.

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