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2018/19 Taught Postgraduate Module Catalogue
EPIB5041M Statistical Inference for Health Data
15 creditsClass Size: 45
Module manager: Dr Paul Baxter
Email: p.d.baxter@leeds.ac.uk
Taught: Semester 1 (Sep to Jan) View Timetable
Year running 2018/19
Pre-requisite qualifications
Academic entry requirementsA 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
| EPIB5024M | Statistical Inference |
Module replaces
EPIB5024M Statistical InferenceThis module is not approved as an Elective
Objectives
The objectives of this module are to:- introduce basic methods of statistical inference as the key tools for investigating diseases in a population;
- introduce the concept of likelihood given a parametric model;
- introduce the general framework of likelihood-ratio testing and frequentist methods, including a range of specific hypothesis tests used when studying health and disease in populations;
- discuss the use of prior distributions and Bayes' theorem and to introduce the Bayes' solution to some basic decision problems relevant to studying health and disease in populations;
- develop understanding of how to properly conduct, interpret and present methods of statistical inference appropriate to their future responsibilities to colleagues and society in general. As such, we aim to develop a professional attitude towards statistical inference;
- provide signposted links to skills introduced or developed by related modules.
Learning outcomes
Critical evaluation
By the end of this module participants should be able to:
- contrast and choose appropriate (Bayesian and / or frequentist) methods of inference to analyse data in a variety of situations;
- check the validity of the assumptions behind these methods.
- Skills base
By the end of this module participants should be able to:
- conduct basic methods of statistical inference:
(i) analysis of variance and non-parametric equivalents
(ii) chi-squared tests of association and related methods
(iii) simple linear regression and correlation
- recognise the likelihood given a parametric model and produce different estimators
- perform likelihood-ratio tests for hypotheses on several parameters
- discuss the use of prior distributions and Bayes' theorem
- obtain the Bayes' solution to some basic decision problems
Transferable skills
By the end of this module participants should be able to:
- use a statistical computing package
- interpret and present the results of their analyses appropriately
- undertake on-line tasks.
Syllabus
The module will be delivered by Paul Baxter over 11 weeks, as a blend of interactive lecture/seminars, online material and computer practicals.
The module will cover the following subjects:
- Review of probability theory: discrete and continuous random variables, univariate transformations, probability and moment generating functions, hierarchical models and mixture distributions, multiple random variables, multivariate transformations.
- Basic methods of inference: confidence intervals and t-tests, analysis of variance, chi-squared tests, McNemar's test, correlation, introduction to linear regression, Wilcoxon and Mann-Whitney tests, Kruskal-Wallis test and Spearman's correlation.
- Frequentist inference: properties of estimators, point estimation techniques (method of moments, maximum likelihood), hypothesis testing (philosophy, type I and II errors, simple and composite hypotheses, multiple testing), likelihood ratio tests (Neyman-Pearson lemma, chisquare approximation), confidence intervals (coverage probability and confidence coefficient, inversion of a test statistic, asymptotic methods).
- Bayesian inference: Frequentist vs Bayesian perspective (posterior and prior distributions, non-informative, and conjugate priors), point estimation (posterior mean, mode and maximum a posterior estimators), Bayesian hypothesis testing, Bayes' credible intervals, Bayes' decision theory (risk functions, posterior loss).
- Sample size calculation.
Teaching methods
| Delivery type | Number | Length hours | Student hours |
| Lecture | 22 | 1.00 | 22.00 |
| Practical | 11 | 1.00 | 11.00 |
| Independent online learning hours | 30.00 | ||
| Private study hours | 87.00 | ||
| Total Contact hours | 33.00 | ||
| Total hours (100hr per 10 credits) | 150.00 | ||
Private study
Module participants will be expected to continue questions started during the practicals and work on self study exercises.Each week participants will be expected to read through and reflect upon material covered in the previous lecture and practical session, to write up an online reflective log of their learning, and to read relevant material from the recommended reading list provided (a mix of paper-based and VLE materials).
Opportunities for Formative Feedback
This will be done in a number of ways:- Participation in and completion of online reflective logs
- Completion of practical questions during contact time.
Methods of assessment
Coursework
| Assessment type | Notes | % of formal assessment |
| Self/Peer Assessment | 3 batches of 5 questions each | 45.00 |
| Practical | 3 batches of 6 questions each started during weekly practicals | 45.00 |
| Reflective log | 100 word reflection with each of the 6 above submissions | 10.00 |
| 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: 10/05/2018
Browse Other Catalogues
- Undergraduate module catalogue
- Taught Postgraduate module catalogue
- Undergraduate programme catalogue
- Taught Postgraduate programme catalogue
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