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2019/20 Taught Postgraduate Module Catalogue

EPIB5043M Further techniques in Health Data Analytics

15 creditsClass Size: 25

Module manager: Dr Richard Feltbower
Email: r.g.feltbower@leeds.ac.uk

Taught: Semester 1 View Timetable

Year running 2019/20

Pre-requisite qualifications

Academic entry requirements

A 1st degree in a quantitative or scientific subject area with substantial mathematical, statistical or numeracy components (at least 2:1). We also consider working experience (two years or more) of research in a quantitative subject area. Non-graduates who: have successfully completed three years of a UK medical degree; are normally ranked in the top 50% of the year 3 cohort; and wish to take the Health Data Analytics MSc as an intercalated programme, will also be accepted.

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.

This module is mutually exclusive with

EPIB5038MAdvanced epidemiological techniques

Module replaces

EPIB5038M Advanced Epidemiological Techniques

This module is approved as an Elective

Module summary

The module is designed to give students an understanding of advanced techniques which underpin the study of diseases in a population, including genetic and geographical epidemiology. By the end of the module students will be able to undertake survival analyses, analysis of diagnostic tests, meta-analyses and conditional logistic regressions.

Objectives

The objectives of this module are to:

- Develop an understanding of advanced observational data analytic techniques which underpin the study of diseases in a population.
- Enable the student to apply appropriate models to describe disease incidence and temporal changes over time.
- Introduce diagnostic testing and inter-rater agreement.
- Introduce survival analyses, with a particular focus on Cox regression models
- Explore how to develop and assess the validity of statistical models to test research hypotheses
- Gain knowledge of logistic regression techniques relevant to case-control data
- Introduce meta-analysis in randomised controlled trials and observational studies; assessment of publication bias.
- Gain an understanding of genetic epidemiology and data analytical approaches used in this field
- Introduce geographical epidemiology techniques and gain an understanding of using census data in observational research
- Enable the student to critically evaluate observational research evidence

Learning outcomes
By the end of this module the student should be able to:

- Demonstrate a critical awareness of how to examine temporal changes in incidence rates using age-period-cohort models;
- Create robust statistical models to test aetiological hypotheses which minimise common problems such as bias and confounding;
- Formulate statistical models to perform analyses where survival time is the main outcome;
- Construct regression models for ecological data to overcome problems with sparse data;
- Understand how to measure agreement and evaluate diagnostic testing using robust statistical methods.
- Understand how to synthesise evidence from a variety of sources, undertake a meta-analysis and assess publication bias;
- Generate population disease burden using standardisation techniques;
- Evaluate modelling strategies which will avoid common pitfalls in observational research;
- Assimilate the principles which underpin logistic regression for use in case-control analyses;
- Assimilate knowledge in appropriate methods to undertake genetic epidemiological analyses.

Skills outcomes
Introduction to spatial and genetic epidemiological methods, age-period-cohort analysis, screening and agreement, survival analysis, meta-analysis and conditional logistic regression.


Syllabus

The module will be delivered by Dr Richard Feltbower and other colleagues over 6 weeks, as a blend of face-to-face small group work and lectures, online written material, and PC-based practical sessions.

The course will cover the following subjects:

Distribution of disease:

Modelling trends in incidence rates; age-period-cohort models; Introduction to spatial methods; Use of census data; ecological regression methods

Screening:

Methods in screening; Agreement; predictive values; sensitivity/specificity; ROC curves;

Survival analyses:

Survival analysis using non-parametric approaches (actuarial/Kaplan-Meier survival estimates)log-rank/ Wilcoxon tests), semi-parametric (Cox proportional hazards model) and an awareness of parametric models (accelerated failure time model, relative survival analysis),

Modelling and bias:

Model building, model diagnostics, fitting time-dependent variables, Meta-analysis methods; assessing publication bias; Binary outcome methods; conditional and unconditional logistic regression with a particular focus on case-control analyses; Adjustment for confounding; Identifying sufficient sets of confounders; Identification of, and adjustment for, participation bias, matching.

Teaching methods

Delivery typeNumberLength hoursStudent hours
Lecture92.0018.00
Practical62.0012.00
Independent online learning hours50.00
Private study hours70.00
Total Contact hours30.00
Total hours (100hr per 10 credits)150.00

Private study

Students will be expected to continue questions started during the practicals, where they will be expected to read through and reflect upon material covered in the previous lecture and practical session, and to read relevant material from the recommended reading list provided (a mix of paper-based and VLE materials). Students will be set a weekly reading task covering further information on meta analysis, Bayesian spatial smoothing and direct vs indirect standardisation.

Opportunities for Formative Feedback

This will be done in a number of ways:

Completion of practical questions during contact time.
Participation in weekly reading tasks as discussed in face-to-face small group work sessions.

Methods of assessment


Coursework
Assessment typeNotes% of formal assessment
Essay or DissertationShort report (1000 words) based on age-period-cohort and logistic (case-control) regression modelling50.00
Essay or DissertationShort report (1000 words) based on survival analyses50.00
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 website

Last updated: 10/05/2018

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