MIXED MODELS ANALYSIS OF MEDICAL DATA USING SAS
7-9th October 2015
http://www.mixedmodels2015.efconference.co.uk/
COURSE CONTENT
This course will cover the statistical background to the mixed model and will emphasise its practical application in medical data with particular reference to clinical trials. All analyses will be illustrated using SAS and lectures will be combined with practical sessions in order to reinforce concepts. Topics covered include:
Day 1
. General concepts and underlying statistical theory
. Use and interpretation of PROC MIXED
. Multi-centre trials and meta-analysis
. Consideration of issues such as biased standard errors, significance testing and negative variance components.
Day 2
. Repeated measures trials
. Random coefficients models
. Crossover trials
. More complex trial designs
. Introduction to Bayesian methods and PROC MCMC
Day 3
. Generalised linear mixed models
. Mixed models for ordinal data
. Use of PROC GLIMMIX, PROC GENMOD and PROC MCMC
WHO SHOULD ATTEND
This course is directed at medical statisticians who wish to understand the statistical background to mixed models and to carry out analyses using SAS.
WHY ATTEND
Conventionally, clinical data is analysed using fixed effects models. However, benefits can often be gained by using a mixed model. For example: in repeated measures trials full allowance can be made for the correlation occurring between the repeated observations even if data are missing; in multicentre trials or meta analyses treatment standard errors are more appropriately based on between centre/trial variation (fixed effects standard errors are based on within centre/trial variation); in crossover trials more accurate treatment means are often achieved by combining within and between patient estimates. Previously the use of these models has been limited by heavy computational requirements and a lack of generally available software, but this is now much less of a restriction with the introduction of suitable procedures into well known packages such as SAS. As with any statistical technique a firm understanding of the theoretical background is essential to allow its effective application and to obtain a clear interpretation of results.
COURSE FEES
Standard rate £895
Academic institutions and registered charities. £650
Fees include daily morning coffee, lunch, afternoon tea, a course dinner, course notes and a copy of the text book "Applied Mixed Models in Medicine" (third edition) by the speakers.
THE SPEAKERS
Helen Brown is the Senior Statistician at The Roslin Institute, University of Edinburgh, and has a research interest in mixed models. She has over thirty years of practical experience as a statistician mainly in medicine and the biosciences. She has been employed within academia, the health service and the pharmaceutical industry.
Robin Prescott Robin Prescott is Emeritus Professor of Health Technology Assessment at the University of Edinburgh. Previously he was Director of the Medical Statistics Unit at the University. He has been working in the medical field for over thirty years and has a particular interest in cross-over trials. He has wide experience of multi-centre trials and of working with the pharmaceutical industry.
The speakers are authors of Applied Mixed Models in Medicine in the John Wiley Statistics in Practice series, with a third edition published in January 2015. All delegates will receive a copy of this text book.
VENUE
The course will be held in the Holiday Inn, Edinburgh-West, 1.5 miles from the city centre and easily accessible from the main railway station and airport.
ACCOMMODATION
Course participants have the opportunity to stay at the Holiday Inn, Edinburgh-West at a discounted rate (telephone +44 (0) 131 311 4901 and quote code A29). Alternatively there are several other hotels and guest houses within walking distance of the hotel.
FURTHER INFORMATION
Website: http://www.mixedmodels2015.efconference.co.uk/
Email: [log in to unmask]
Phone 0131 651 2153
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The University of Edinburgh is a charitable body, registered in
Scotland, with registration number SC005336.
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