Module Specification
An online version of this specification is available to prospective students at
http://www.lshtm.ac.uk/study/currentstudents/studentinformation/msc_module_handbook/section3_moduledescript/in
dex.html
GENERAL INFORMATION
Module name
Survival Analysis and Bayesian Statistics
Module code
2463
Module Organisers
Dr Ruth Keogh (Survival), Dr David Prieto (Bayesian)
Contact email
Ruth.keogh@lshtm.ac.uk or David.Prieto@lshtm.ac.uk
Home Faculty
Faculty of Epidemiology & Population Health
Level
This module is at Level 7 (postgraduate Masters ‘M’ level) of the QAA
Framework for Higher Education Qualifications in England, Wales & Northern
Ireland (FHEQ).
Credit
LSHTM award 15 credits on successful completion of this module.
Accreditation
Not currently accredited by any other body.
Keywords
Research; Epidemiology; Statistics; Quantitative methods; Survival analysis;
Bayesian methods
AIMS, OBJECTIVES AND AUDIENCE
Overall aim
To equip students with the necessary skills to understand the principles and
apply the techniques of Survival Analysis and Bayesian Statistics.
Intended learning
outcomes
By the end of this module, students should be able to:
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Target audience
Demonstrate an understanding of the theoretical basis of Survival Analysis
and assumptions related to different Survival Analysis models;
Use Survival Analysis for analysis of data in STATA and R as appropriate;
Demonstrate an understanding of the theoretical basis of Bayesian
reasoning and Bayesian inference;
Understand the consequences of using different prior knowledge, including
uninformative priors;
Do simple Bayesian estimation by hand with conjugate priors;
Write and estimate Bayesian models with MCMC algorithms using the
statistical software package R.
This module is intended for people with both mathematical (up to first year
undergraduate level) and statistical backgrounds (undergraduate degree level
in joint mathematics/statistics for example) intending to pursue a career in
medical statistics.
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CONTENT
Session content
The module is expected to include sessions addressing the following topics
(though please note that these may be subject to change):
Survival Analysis:
 Non parametric and parametric estimation of survival curves;
 The hazard function and maximum likelihood estimation;
 The theory and use of proportional hazard models;
 Time dependent covariates;
 Accelerated failure time models;
 Competing risks;
 Survival analysis in a case-control setting.
Bayesian statistics:
 Bayesian reasoning and Bayesian inference;
 Prior and posterior distributions;
 Bayesian analysis with conjugate priors;
 Bayesian analysis with MCMC methods;
 Use of R statistical package for Bayesian analysis;
 Reporting the results of Bayesian analyses.
TEACHING, LEARNING AND ASSESSMENT
Study resources
provided or required
Survival Analysis lecture notes; recommended books: Collett D (2003):
“Modelling Survival Data in Medical Research”, Cox DR and Oakes D (1984):
“Analysis of survival data”, Marubini and Valescchi (1995): “Analysing Survival
Data from Clinical Trials and Observational Studies”, Machin D., Cheung Y.B.
and Parmar M.K.B: “Survival Analysis. A practical approach (2006).
Bayesian Statistics lecture notes and exercises; recommended books: George G.
Woodworth “Biostatistics, a Bayesian introduction”, Wiley (2004); Andrew
Gelman et al. “Bayesian data analysis” Chapman & Hall (2009); David
Spiegelhalter et al “Bayesian approaches to clinical trials and health-care
evaluations” Wiley (2004).
Teaching and learning
methods
Learning will generally be based on relevant practicals following lectures.
Some sections of each module will involve the use of computers and/or
groupwork. Assignments will also be given as part of the practical work.
Approximately half of the contact time will be spent in the form of practicals.
Time will be allocated for private study sessions.
Assessment details
Students will carry out two assessments, each consisting of an analysis of data
or a programming exercise together with submission of a short report.
Survival Analysis Assessment Exercise: 50% of the module grade. This involves
analysis of a dataset using survival analysis methods, and writing a 4 page
report on the methods and results in a style appropriate for a medical journal.
Bayesian Analysis Assessment Exercise: 50% of the module grade. This
involves analysis of a dataset using MCMC models with statistical package R,
programming a suitable code and writing a report on the results and
interpretation.
For students who are required to re-sit, or granted a deferral or new attempt,
the tasks will be similar to the original assessment although the data set to be
analysed will be different. To pass the module, in addition to obtaining a pass in
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the combined mark of the two assignments, the student needs to obtain at least
20% of the marks of each assignment separately. A student might need to re-sit
only one part of the module if they have passed the other part.
Assessment dates
Both assessments will be due in 2 weeks after the assignment has been handed
out, which happens during lecture 6
For students who are required to re-sit, or granted a deferral or new attempt,
the deadline will be the standard School-recommended date in mid/late
September 2016
Language of study and
assessment
English (please see ‘English language requirements’ below regarding the
standard required for entry).
TIMING AND MODE OF STUDY
Duration
The module runs for 5 weeks at 2.5 days per week, from Wednesday lunchtime
to Friday afternoon.
Dates
For 2015-16, the module will start on Wednesday 24 February 2016 and finish
on Thursday 24 March 2016.
Timetable slot
The module runs in LSHTM timetable slot D2.
Mode of Study
The module is taught face-to-face in London. Both full-time and part-time
students follow the same schedule. For full-time students, other LSHTM
modules are available in the other half of the week for the C and D slots.
Learning time
The notional learning time for the module totals 150 hours, consisting of:
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Contact time ≈ 50 hours
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Directed self-study ≈ 30 hours
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Self-directed learning ≈ 20 hours
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Assessment, review and revision ≈ 50 hours
Approximately half of the time is spent on Survival Analysis and the other half
on Bayesian Statistics, including the assessment time that should be about
50%-50% respectively.
APPLICATION, ADMISSION AND FEES
Pre-requisites
A knowledge of linear regression, analysis of variance, logistic regression, and
simple methods of analysing quantitative and categorical data is essential (ttest, RR, OR). No previous knowledge of Bayesian statistics is necessary but
knowledge of classical inference and the idea of likelihood is needed.
Familiarity with STATA and R is needed.
English language
requirements
A strong command of the English language is necessary to benefit from
studying the module. Applicants whose first language is not English or whose
prior university studies have not been conducted wholly in English must fulfil
LSHTM’s English language requirements, with an acceptable score in an
approved test taken in the two years prior to entry. Applicants may be asked to
take a test even if the standard conditions have been met.
Student numbers
Student numbers are typically between 25-40 per year; numbers may be
capped due to limitations in facilities or staffing.
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Student selection
Preference will be given to LSHTM MSc students, particularly those registered
on the Medical Statistics MSc for whom the course is compulsory, and LSHTM
research degree students. Other applicants meeting the entry criteria will
usually be offered a place in the order applications are received, until any cap
on numbers is reached. Applicants may be placed on a waiting list and given
priority the next time the module is run.
Partial Registration (partial participation) by LSHTM research degree students
is allowed for this module.
Fees
For registered LSHTM MSc students, fees for the module are included within
MSc fees (given on individual course prospectus pages).
If registering specifically for this module, as a stand-alone short course,
individual module fees will apply.
Tuition fees must be paid in full before commencing the module, or by any fee
deadline set by the Registry.
Scholarships
Scholarships are not available for individual modules. Some potential sources
of funding are detailed on the LSHTM website.
Admission deadlines
For 2015-16:
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For registered LSHTM MSc students, the module choice deadline (for Term
2 and 3 modules) is Friday 20 November 2015.
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If registering specifically for this module, applications may be made at any
time but, as places are limited, applications ahead of the MSc deadline are
strongly advised. All applications should be submitted at the latest 8 weeks
prior to the start of the module. Formal registration will take place on the
morning of the first day of the module.
ABOUT THIS DOCUMENT
This module specification applies for the academic year 2015-16
Last revised 10 July 2014 by David Prieto and Ruth Keogh; Minor amendments 20 July 15 SDB
London School of Hygiene & Tropical Medicine, Keppel St., London WC1E 7HT.
www.lshtm.ac.uk
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