Advanced Statistical Methods in Epidemiology - 3 day course Event
For more information regarding this event, please telephone Nicole Thompson - Vassel on +44 (0) 023 8059 9036 or email ProfessionalTraining.FSHS@soton.ac.uk .
Event details
This course will focus on the application of statistical methods specially developed for epidemiological study data. Topics will include the basic disease occurrence measures of prevalence and incidence with their role in surveillance including standardization, Mantel-Haenszel estimation of various effect measures including the risk ratio and risk difference for cohort studies and the odds ratio for case-control studies as well as Poisson and logistic regression to adjust for potential confounders simultaneously. The course will also include elements of time-to-event analysis including Kaplan-Meier estimation and Cox' proportional hazards model for confounder adjustment. The course will include a mixture of lectures and practical workshops using the software STATA.
This course is run by Southampton Statistical Sciences Research (S3RI). For more information about S3RI and their short courses please visit their courses pages here.
Course Objectives:
By the end of the course participants should:
- Have a practical understanding of the ideas and methods of modelling analyzing epidemiological data arising from different epidemiologic designs, and know when their use is appropriate
- Have a detailed understanding of how to critically interpret results from epidemiological data analysis
- Gain a working knowledge of the epidemiological parts of the package STATA
- Be able to apply these methods to epidemiologic data arising from diverse epidemiological designs
Course Content:
- Measures of disease occurrence including standardization (day 1)
- Epidemiological study types and estimable effect measures (day 1)
- Effect measures including risk ratio, risk difference and odds ratio including their nonparametric estimation under confounding using Mantel-Haenszel techniques (day 1 - day 2)
- Poisson and logistic regression modelling for dealing with several confounders (day 2)
- Kaplan-Meier estimation for time-to-event data (day 3)
- Cox' proportional hazards model (day 3)
The course will have a practical emphasis with computer workshops allowing participants to work through examples using the STATA software.
Target Audience:
The course is aimed at researchers who want to perform statistical modeling and analysis of epidemiological study data. Participants may be academic researchers in the medical and health or social sciences or may work in government, pharmaceutical industry, or other parts of the private sector.
Pre-requisites:
Participants are expected to have a good working knowledge of simple statistical methods, including a good understanding of estimation of parameters including confidence intervals, hypothesis testing and p-values. No familiarity with the software STATA is required.
Preparatory Reading:
There are an increasing number of good introductory books of statistical methodology for epidemiology. Below are a few that participants may find useful before attending the course, but may also be valuable to consult during and after the course:
- Wassertheil-Smoller, S (2004) Biostatistics and Epidemiology. Springer, New York.
- Selvin, S (2004) Statistical Analysis of Epidemiologic Data, 3rd Ed. Oxford University Press, Oxford.
- Jewell, N. P. (2004) Statistics of Epidemiology. Chapman & Hall /CRC, Boca Raton.
- Woodward M (2005) Epidemiology. Study Design and Data Analysis, 2nd Ed. Chapman & Hall /CRC, Boca Raton.
Course Materials:
Participants will receive written course notes.
Please bring a calculator for the workshops as well as a USB memory stick for saving your computer workshop output.
Course Fee:
£150 per day for registered students. £200 per day for staff from academic institutions (including research centres). £250 per day for all other participants. The course fee includes course materials, lunches and morning and afternoon refreshments. Travel and accommodation are to be arranged and paid for by the participant.
Duration:
On the first day, the course will start with registration and coffee at 9.30 with formal teaching starting at 10.00 a.m. On the last day, formal teaching will end at about 3.00p.m. Afterwards there will be an opportunity for participants to ask questions about the course and to discuss with the instructor how to analyse their own data (until about 4pm). Participants are welcome to bring their own data if they wish.
How to book:
To book a place on the course please visit the University of Southampton's online store here
Speaker information
Professor Dankmar Boehning,is the Chair in Medical Statistics in the Southampton Statistical Sciences Research Institute and is also jointly appointed by the academic units of mathematics and medicine at the University of Southampton. He has research interests in mixture models, capture-recapture methods and their applications in public health, nonparametric estimation of effect measures under confounding, statistical methods for meta-analysis and methods for diagnostic testing. He is associate editor of the Biometrical Journal, Statistical Modelling, Statistical Methods in Medical Research and CSDA. He had previous professorial positions in Reading (UK) and Berlin (Germany) and held visiting positions in Penn State (USA), Vienna (Austria), Munich (Germany) and Bangkok (Thailand).
Dr Antonello Maruotti ,is a Lecturer in Medical Statistics at the Southampton Statistical Sciences Research Institute (S3RI) at the University of Southampton. He is also appointed by the academic unit of mathematics at the University of Southampton. He has research interests in repeated measurements and longitudinal data modeling, hidden Markov models and their application in medicine and epidemiology, statistical methods to deal with heterogeneiy and missingness. He has taught a wide range of statistical courses, with a special focus on statistical methods in medical research. He had previously a professional position in Rome (Italy) and held visiting positions in Lund (Sweden), Gottingen (Germany) and Caen (France).