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Statistical Methods for Epidemiological & Clinical Research


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Contact Provider
Liverpool
2 weeks
Short Course
50
10
£1,150.00
Anyone working in the fields of statistics or who requires a good knowledge of statistics would find this course beneficial.
Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA, Merseyside, United Kingdom

Programme Introduction

This course aims to provide students with knowledge and critical understanding of standard quantitative statistical methods used to evaluate epidemiological studies and clinical trials involving humans.  It develops the ability of students to design, critically analyse, interpret and report the findings of a research project in a health related topic, primarily using methodologies appropriate for health surveys and observational cohort/case-control studies, but will also examine methodologies relevant for (randomised controlled) clinical trials.

Learning Outcomes

Upon successful completion of the course, you will be able to:

  1. Identify appropriate experimental design methodologies for carrying out health surveys, observational epidemiological studies (primarily cohort and case-control design studies) and interventional clinical trials involving human participants
  2. Identify and execute appropriate statistical methods for summarising data collected in health surveys, epidemiological studies and interventional clinical trials, using both conventional significance testing approaches and more contemporary effect size/confidence intervals concepts
  3. Critically evaluate and interpret the statistical analyses of data from health surveys, epidemiological studies and interventional clinical trials
  4. Summarise the results of statistical analyses in a report format suitable for a non-mathematical readership

 

Training Methodology

We will concentrate on the practical application of different experimental design strategies and on the interpretation of the results of statistical analysis methodologies, rather than on any detailed description of the mathematical derivations of these methods. Most emphasis will be given to methodologies relevant to epidemiological research.


You will be given access to a large data set, mimicing the inhabitants of a large (fictitious) community in a tropical country. Students will be required (either individually or in pairs depending on the number and background of the students who attend) to:

(a)   design projects to address a series of research questions relating to (tropical) health issues in humans

(b)   select an appropriate number of individuals from the data set to mimic the conduct of each study design

(c)   apply appropriate statistical methods to evaluate the data sets drawn using standard statistical computer packages such as EpiInfo, SPSS and R.


The course will be taught intensively over 10 teaching sessions. The number of contact hours is high relative to other LSTM modules so that students have access to staff support whilst completing the exercises.

A session will typically last for 8 hours and consist of:

  • an initial formal presentation in the form of a lecture, seminar or audiovisual display describing a specific type of study design and/or statistical methodology (1 hour)
  • a linked period of student-directed learning with staff available for consultation - this will usually be a practical exercise involving the analysis of data but may involve other tasks including appraising a research paper, considering an appropriate research design to address a hypothetical tropical health-related research question, or writing a short report summarising the results of a statistical analysis (these reports will be formative in nature and feedback will be given to assist students in the preparation of their summatively assessed assignments) (4.5 hours)
  • a feedback session and/or group discussion (0.5 hours)
  • a period of directed reflective thinking based on the material covered in the session (2 hours).

Students are directed to and are expected to have read relevant material from the course reading list in advance of each session.

Because of the nature of the topics covered by this course, you will be encouraged to work on the set exercises individually. However, students with previous experience of analysing data from epidemiological and/or clinical research projects may be encouraged to "pair-up" with inexperienced students to provide peer support if this is considered to be appropriate and feasible - in which case, the Module Organiser will arrange for students to work with different partners for different exercises in order to ensure equity of experience.  Since the teaching staff interact closely with the students whilst they are completing the exercises, they are able to monitor the quality of the peer support being given and provide extra input if necessary.

 

BRING YOUR LAPTOP - The course will be very practically orientated; an integral element will be the analysis of data within both the taught and practical sessions. A limited number of PCs will be available in the LSTM Computer Room but ideally students should have a laptop computer onto which they have loaded the SPSS, Amos and R software packages, all of which are freeware and/or available from the UoL CSD Helpdesk for a nominal cost. If necessary, laptops can be be borrowed from LSTM Library and Computing Services (subject to availability).

Please be aware that Apple Mac computers do not support the software used on the course.

Who Should Attend

Anyone working in the fields of statistics would find this course beneficial.  Of particular interest to people working in epidemiological and clinical research.

Pre-requisites – include:

Open to all related disciplines.

Non-graduates who are suitably qualified by experience.

Proof of English to IELTS 6.5 or TOEFL iBT 88 / paper based 570

 

Course Outline

Day 1

  • Overview of design issues for the conduct of health surveys, including the development of sampling frames, selection of (random / representative) samples, stratification, bias, confounding, data management and survery size estimation.
  • You will then be required to design a survey to address a set health-related research question, and then to select a (unique to them) sample of individuals from the large (imaginary community) data set mimicking the conduct of the survey they have designed.

Day 2

  • Overview of the basic statistical methods commonly used to evaluate health surveys, concentrating on data summary methods and evaluation of error / uncertainty; statistical methods used to adjust paramater estimates for confounding factors and clustering / stratification effects will be considered.
  • You will then be required to carry out a simple statistical analysis of the data selected - teaching staff will be available at all times to help with practical problems encountered. The session will end with a feedback session to compare the results obtained from each student's sample - this feedback will concentrate primarily on the issues relating to the fact that each student will obtain a (slightly) different result even though they will all be addressing the same question, and how this apparent paradox can be resolved.

Day 3

  • Overview of design issues for the conduct of observational epidemiological studies, concentrating primarily on cohort and case-control study designs. Students will be provided with a research question unrelated to the module data set and, as a class exercise, asked to discuss the advantages and disadvantages of addressing this question using a cohort design and then using a case-control design. This discussion will be directed at practical issues such as subject selection, bias, confounding and data management.
  • You will then be given a second research question linked to the module data set and will be required to write a short essay (not assessed) outlining how they would use the data set to address this research question using (a) a cohort study design and (b) a case-control study design, again outlining the advantages and disadvantages of each approach.

Day 4

  • Overview of the statistical methods used to estimate the sample size / statistical power of cohort and case-control studies.
  • You will then be required to calculate the sample sizes needed for the cohort and case-control studies they designed based on the module data set in session 3 and to draw appropriate samples from the data set. The session will conclude with an overview of the statistical methods used to analyse the data collected in observational cohort and case-control studies.

Days 5 & 6

  • You will perform a statistical analysis of your cohort study data using the methods discussed in Day 4. There will be an extended feedback at the end of Day 6 in which the results obtained by each student will be collated as a class exercise and the range of findings explored to identify reasons for the different results obtained.

Days 7 & 8

  • You will perform a statistical analysis of your case-control study data using the methods discussed in Day 4. There will be an extended feedback at the end of Day 8 in which the results obtained by each student will be collated as a class exercise and the range of findings explored to identify reasons for the different results obtained.

Day 9

  • Overview of design issues for the conduct of interventional randomised controlled trials (RCTs), covering selection of subjects, randomisation, blinding, ethics, sample size / power estimation, etc.
  • You will then be given a research question and will be required to design a RCT to address this question. You will then draw an appropriate sized sample of individuals from the module data set and to appropriately allocate each person selected to receive either a new or a standard intervention. The session will close with a feedback session around the practical issues that would be encountered conducting this study in a real-life tropical situation.

Day 10

  • Overview of the statistical methods used to analyse the data collected in a RCT. Particular attention will be given to concepts such as intention-to-treat and per-protocol analysis methodologies.
  • You will then analyse the primary outcome measure from the data set generated in Day 9. The session will end with a feedback in which the results obtained by each student will be collated as a class exercise and the range of findings explored.

10 Academic Credits - Assessment

For an additional fee of £95, you can be entered for full Academic Credits for this course (awarded by the University of Liverpool).  The assessment is related to the learning outcomes :

  • 100% 2,000 word Written Assignment meeting all Learning Outcomes.  You will be given a data set from an observational epidemiological study to analyse and write up as a short formal report.  The word count excludes tables and output material from statistical packages, which will be included as an Appendix.

Please note that assessments may change at the discretion of the Directors of Study.  All students will be made aware of new assessment details.  The academic credits are offered to provide choice and flexibility to all our students, and students who do not wish to be entered for academic credits will be awarded a Certificate of Attendance.

Course Lecturers include:

Please click on the name of the Academic to view their full profile.

Dr Brian Faragher (Senior Lecturer in Medical Statistics)

Dr. Brian Faragher is a (medical) statistician/epidemiologist.  He has a BSc in Computational and Statistical Sciences from the University of Liverpool and an MSc in Statistics from the University College of Wales (Aberystwyth).  His PhD, on the relationship between psychosocial factors and breast cancer, was completed at the University of Manchester.    Before taking up his current post, Dr. Faragher worked with a group of health psychologists in Manchester Business School, having previously worked for over 20 years providing statistical and computing support to a wide range of clinical and laboratory research projects in the University of Manchester Medical School and in NHS Trusts across Greater Manchester.  Prior to that, he worked briefly in an agricultural research centre and the pharmaceutical industry.

In his current post, Dr. Faragher teaches postgraduate courses in research methods and medical statistics.  His main research interest is in the application of (structural equation) models to data from clinical trials and surveys, as well as the application of statistical methods in general to tropical health research data.  He also provides statistical and data management/computing support and advice to researchers working on LSTM sponsored research projects and to postgraduate students within LSTM.  Dr. Faragher continues to have an additional interest in research ethics, having served on NHS Research Ethics Committees for over 20 years.

Professor Joe Valadez (Professor of International Health)

Joe Valadez joined LSTM’s International Health group in July 2008 from the World Bank, where he was the Senior Monitoring and Evaluation Specialist on the Global HIV/AIDS Programme and the Malaria Booster Program for Africa. A community epidemiologist who has worked in more than 45 countries, he earned his Doctorate in Science at Harvard, joining the Harvard faculty in 1986. He has since worked in more than 45 countries, including Kenya, where he was Director of Projects for the African Medical and Research Foundation and in Rwanda where he served as Senior Health Officer for UNICEF immediately after the genocide, helping the new Rwandan government develop a Ministry of Health. Much of his research has focused on the development of rapid and practical programme monitoring and evaluation techniques that adapt quality control statistics as used in industry for application in community health programmes. He developed a state of the art Lot Quality Assurance Sampling method during the mid-1980s which is used internationally and recently integrated this approach with cluster sampling for applications in large countries. Currently, he is developing new approaches for rapid mapping of disease prevalence to support community treatment programmes of schistosomiasis and school-based intermittent preventive treatment of malaria. He is also working to assess the relative effectiveness of diverse service delivery strategies on covering community populations with public health programmes.

Professor Paul Garner(Professor in Community Health)

Research to inform global health policies in international health: I have led a programme of research coordinating a network of over 150 people synthesising research to inform global, regional and national policies in tropical infections and conditions relevant to middle and low income countries, and been directly responsible for obtaining and managing over 10 million pounds since 1992 in this area of research and development. This has had substantive effect on global and national policies, particularly in diarrhoea, malaria and tuberculosis.

Work experience includes UK NHS; Papua New Guinea as a District Medical Officer, then epidemiologist at the PNG Institute of Medical Research; researcher at the London School of Hygiene and Tropical Medicine from 1988, then moved to the Liverpool School of Tropical Medicine in 1994.

Recommended Texts

There are many books in the Donald Mason Library in LSTM that can be used to supplement the material taught on this course. Anything with "epidemiology" in the title is likely to offer something, although probably more than most students will want or need.  The best are (in order of personal preference):

  • Silman AJ, Macfarlane GJ (2002). Epidemiological Studies: A Practical Guide (2nd edition). Cambridge University Press.
  • Stewart A (2010). Basic Statistics and Epidemiology (3rd edition). Radcliffe Publishing Ltd.
  • Giesecke J (2002). Modern Infectious Disease Epidemiology (2nd edition). Arnold.
  • The following is not yet in the library, but is excellent and very contemporary (there is a parallel website to go with the book):
  • Gordis L (2008). Epidemiology (4th edition). Saunders.
  • After that, anything with "medical statistics" in the title will be useful. The two seminal texts are (in order of personal preference):
  • Altman D (1990). Practical Statistics for Medical Research. Chapman & Hall.
  • Bland JM (2000). An Introduction to Medical Statistics (3rd edition). OUP Oxford.
  • After that, there are two good "old favourites":
  • Kirkwood B, Sterne J (2003). Essential Medical Statistics (2nd edition). Wiley-Blackwell.
  • Altman D, Machin D, Bryant T, Gardner S (2000). Statistics with Confidence (2nd edition). Wiley-Blackwell.
  • If you are looking for a good read around the subject of epidemiology, two books stand out (and both are very cheap to buy):
  • Beaglehole R., Bonita R, Kjellstrom T (2007) Basic Epidemiology (2nd edition). World Health Organization
  • Elizabeth Pisani (2009) The Wisdom of Whores Granta Books



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