University of Dundee

Biomedical Research Institute

Ninewells Hospital and Medical School

Job Description

Job title: Statistician/Epidemiologist/Statistical Programmer

Grade: Grade 7, Maximum starting salary, SP33 - £32,620 per annum

Location: Biomedical Research Institute, College of Medicine, Dentistry & Nursing, Ninewells Hospital & Medical School Campus, University of Dundee, DD1 9SY. Office location will be within the Clinical Research Centre building on the Ninewells Hospital & Medical School Campus on the Western side of Dundee.

Responsible to: Professor Helen Colhoun, Professor of Public Health, and Professor Andrew Morris, Director of Biomedical Research Institute.

Purpose of post: The purpose of this post is to provide biostatistical support for some of the epidemiological analyses in the diabetes research programme described in detail below. The post holder will be expected to be knowledgeable about methods commonly used in biostatistics, including survival analysis and multivariate modelling, and to have good data management, data cleaning and data manipulation skills. Our preferred analysis programme is R and the post holder will be expected to learn R, if not already proficient in its use. The post is suitable for biostatisticians, statisticians or highly numerate epidemiologists with good data skills. Experience of dealing with datasets involving linkage to routine health statistics would be an advantage. There is good opportunity for higher degree, if desired, and for extensive publication.

Background

We are seeking an experienced statistician or statistical programmer or highly numerate epidemiologist to join the Diabetes Epidemiology Research group within the School of Medicine. The team is well established and multidisciplinary, with a strong record in epidemiology, health informatics and genetic epidemiology. The team includes senior staff from across several Divisions within the School of Medicine including Helen Colhoun (Professor of Public Health, Biomedical Research Institute), Andrew Morris (Director of Biomedical Research Institute), Ewan Pearson (Consultant Diabetologist and Senior Lecturer in Medicine) and Colin Palmer (Professor in Population Genetics).

The research programmes ongoing include:

Epidemiology of diabetes, diabetic complications and treatment of diabetes in the Scottish and Tayside diabetic population: The databases underpinning this work include the SCI-DC clinical data system for diabetes anonymised and linked with other routine datasets including hospital discharge data, mortality data and prescription encashment data.
Pharmacoepidemiology: This work includes evaluating the evidence for adverse effects of commonly used prescription drugs in the local population using anonymised linked datasets.
 
These diabetes epidemiology and pharmacoepidemiology projects are funded by the Wellcome Trust Scottish Health Informatics Programme (SHIP) and will be the principal focus of the work of the postholder. The project involves
i) working with the programmers to specify appropriate data extracts for research purposes from a clinical diabetes system
ii) working with two other statisticians in the team to maintain and augment metadata on the dataset
iii) using the data, linked anonymously to other routine health data sources, to test specific hypotheses about the epidemiology of diabetes. This includes drafting statistical analysis plans, writing the analysis code and preparing publication ready tables. The hypotheses are specified by the epidemiologists working with the statisticians.

Additional work ongoing within the team includes;

The Wellcome Trust UK Type 2 Diabetes case Control Collection: Within Tayside with support from the Wellcome Trust we have established a powerful bioresource of data and samples from more than 15,000 patients with type 2 diabetes and general population controls. This project offers an exciting opportunity to use data from Genome Wide Association studies to establish gene variants associated with type 2 diabetes and its complications, such as heart disease and retinopathy, and response to diabetes medications. In addition, an extensive biomarker identification programme is underway.

Biomarker and genetic substudies on the Collaborative Atorvastatin Diabetes Study: This is a bioresource and database from an RCT of lipid lowering therapy, which is being used to characterise biomarkers for vascular disease in diabetes and genetic determinants of various subphenotypes in this diabetic cohort. It includes data from a genome wide genotyping study.

The Eurodiab Family Study: A case control study of the genetics of diabetic complications that includes a genome wide genotyping study.

In addition to the principal investigators, the team comprises three statisticians including this position, an epidemiologist and genetic epidemiologists.

Principal duties

. Develop statistical analysis plans in conjunction with the rest of the team
. Specify applications to the clinical database data extraction and anonymisation service for new data extracts as needed
. Be responsible for some aspects database management
. Conduct the data analyses, maintaining well documented analysis code and prepare analysis output ready for publication and for presentation using PowerPoint or equivalent
. Actively collaborate in drafting co-authored publications with the rest of the team

Person Specification

Essential:
. Degree in numerate discipline, i.e. statistics, mathematics, epidemiology or equivalent
. Experience of analysing large datasets, including survival analysis
. Experience of data manipulation, cleaning and data management
. Skill in using one of the following analysis programmes to a high level: R, STATA, S-PLUS, or SAS
. Preparedness to learn R, if not already experienced in its use
. Experience of presenting data analysis results to non-statisticians
. Experience of preparing data output for publication
. Preparedness to learn presentation skills, including PowerPoint

Desirable:
. Higher degree in statistics, mathematics, or epidemiology or equivalent
. Experience of working specifically with biomedical data
. Good statistical programming skills
. Previous publications or significant contributions to publications
. Knowledge of Bayesian statistics
. Experience of working with routine health datasets involving ICD coding
. Knowledge of statistical genetics

Potential applicants are encouraged to have an informal discussion about the post with Professor Helen Colhoun

Contact details:
Professor Helen Colhoun
E-mail: [log in to unmask]
Telephone: 01382 740506

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