This module is discontinued in the selected year. The information shown below is for the academic year that the module was last running in, prior to the year selected.
2017/18 Taught Postgraduate Module Catalogue
EPIB5037M Advanced Modelling Strategies
15 creditsClass Size: 25
Module manager: Professor M Gilthorpe
Email: M.S.Gilthorpe@leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2017/18
Pre-requisite qualifications
Academic entry requirements:Normally a first degree in a science allied with medicine, including biology, ecology, biochemistry, statistics, mathematics, computing, psychology, economics or biomedical science (at least 2:2). We will also consider working experience (two years or more) of research in a quantitative subject area.
English language requirements:
An overall score of 7.0 on IELTS (International English Language Testing System) with at least 6.0 in writing and no other skill below 6.5; from a TOEFL paper based test the requirement is a minimum score of 600, with 4.5 in the Test of Written English (TWE); from a TOEFL computer based test the requirement is a minimum score of 250, with 4.5 TWE; from a TOEFL Internet based test the requirement is a minimum score of 100, with 25 in the "Writing Skills" score.
Pre-requisites
| EPIB5022M | Core Epidemiology |
| EPIB5023M | Introduction to Modelling |
| EPIB5024M | Statistical Inference |
This module is not approved as an Elective
Module summary
The module is designed to give students an introduction to the common pitfalls and challenges in the statistical modelling of epidemiological data. By the end of the module students will be able to critically appraise statistical models as presented in the epidemiological literature, identifying modelling strategies that are potentially erroneous and understanding alternative strategies (if they exist) particularly avoiding mathematical coupling, reversal paradox and inappropriate modelling of compositional data.Objectives
The objectives of this module are to:- Describe common pitfalls and challenges in the statistical modelling of epidemiological data;
- Explain a range of scenarios where standard modelling strategies present challenges; outline solutions to these challenges;
- Develop an understanding of underlying assumptions to various modelling strategies, leading to student comprehension of the various pitfalls in statistical modelling;
- Describe situations where statistical modelling strategies might be erroneous or misleading;
- Evaluate alternative modelling strategies based on a comprehensive appreciation of the common pitfalls;
- Promote an inherently questioning stance to modelling strategies of epidemiological data;
- Formulate a sound rationale, with respect to undertaking the modelling of epidemiological data, appropriate for the student’s future responsibilities in employment;
- Apply professional critical evaluation of evidence based on statistical modelling of epidemiological data;
- Provide sign-posted links to skills introduced or developed by related modules.
Learning outcomes
Critical evaluation of modelling strategies
By the end of this module the student should be able to:
- Understand common pitfalls in the standard regression modelling of epidemiological data;
- Identify appropriately directly observed confounders, unobserved confounders, proxy confounders, mediators and competing exposures;
- Identify modelling strategies that are potentially erroneous or misleading in specific contexts;
- Formulate alternative strategies, if they exist, particularly avoiding mathematical coupling, reversal paradox and inappropriate modelling of compositional data;
- Describe how to phrase epidemiological research questions to ensure valid statistical modelling strategies may be applied;
- Contrast the limitations of viable modelling strategies for various epidemiological scenarios and complex epidemiological data structures;
- Critically appraise statistical models as often presented in the epidemiological literature.
Modelling skills
By the end of this module the student should be able to:
- Explain the definition and implications of causality for epidemiological data;
- Describe how to establish which confounders and competing exposures are appropriate for different research questions in a modelling framework;
- Assimilate the pitfalls, strategies and caveats of dealing with complex data structures (e.g. hierarchical data, mathematically coupled data, ratio variable data, compositional data);
- Formulate under what circumstances the adverse impacts of mathematical coupling in regression modelling can be overcome;
- Explain the challenges and limitations of ratio variables for frequently encountered epidemiological scenarios;
- Recognise that biological and statistical interactions are not the same and formulate when and how to use statistical interaction to infer underlying biological processes;
- Generate mixture models to deal with data complexity, particularly sparse data;
- Appraise how an a priori understanding of underlying data generation processes informs model selection and interpretation.
Transferable skills
By the end of this module the student should be able to:
- Create appropriate statistical models of observational data for a number of contexts, epidemiological or otherwise, where causal inference is required;
- Undertake critical appraisal of the statistical modelling strategies of epidemiological data by other researchers.
Skills outcomes
Fundamental knowledge and understanding of a range of statistical modelling strategies appropriate to epidemiological data. This includes use of directed acyclic graphs, avoidance of regression to the mean, mathematical coupling, reversal paradox and the limitations of statistical interaction.
Syllabus
The module will be delivered by Professor Mark S Gilthorpe over 12 weeks, as a blend of face-to-face lectures, small group workshops, tutorials (especially for face-to-face feedback on assignments), online written material, online formative questions, answers and feedback, and online further reading materials.
The course will cover the following subjects:
Causality, directed acyclic graphs (DAGs) and their use within epidemiological studies, adjustment for confounding, competing exposures and mediation; regression to the mean; analysis of change, appropriate use of ANCOVA and baseline effects; the reversal paradox (Simpsons paradox, Lord's paradox, and suppression); mathematical coupling; ratio variables; statistical interaction; mixture modelling; and zero-inflated models.
Teaching methods
| Delivery type | Number | Length hours | Student hours |
| Workshop | 7 | 2.50 | 17.50 |
| Lectures | 11 | 1.00 | 11.00 |
| Tutorial | 3 | 2.00 | 6.00 |
| Independent online learning hours | 22.50 | ||
| Private study hours | 93.00 | ||
| Total Contact hours | 34.50 | ||
| Total hours (100hr per 10 credits) | 150.00 | ||
Private study
- Lectures deliver the theoretical basis of the issues covered; comprehensive, detailed lecture notes, along with multiple references, are provided ahead of each lecture online;- Students will be guided and encouraged to search for additional associated literature using material provided by Skills@Library (http://library.leeds.ac.uk/skills).
- Additional resources are uploaded to VLE, including podcasts and animated illustrations of the concepts introduced in lectures / workshops.
Opportunities for Formative Feedback
This will be done in a number of ways:- Student attendance and contribution to lectures and tutorials;
- Individual online formative assessments, delivered using the VLE, will help students assess their learning throughout;
- Students will be set formative group problems to work through, the outcome of which will be presented in the workshops and peer-assessed under tutor guidance.
Methods of assessment
Coursework
| Assessment type | Notes | % of formal assessment |
| Essay or Dissertation | Short reports - max 800 words | 30.00 |
| Essay or Dissertation | Short reports - max 800 words | 40.00 |
| Essay or Dissertation | Short reports - max 800 words | 30.00 |
| Total percentage (Assessment Coursework) | 100.00 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
Reading list
The reading list is available from the Library websiteLast updated: 22/04/2015
Browse Other Catalogues
- Undergraduate module catalogue
- Taught Postgraduate module catalogue
- Undergraduate programme catalogue
- Taught Postgraduate programme catalogue
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