Usher Institute of Population Health Sciences and Informatics

Mixed models analysis of medical data using SAS PROC MIXED and beyond

Topics covered include:

Day 1

  • General concepts and underlying statistical theory
  • Use and interpretation of PROC MIXED
  • Application in multi-centre trials
  • Application in crossover trials
  • Consideration of issues such as biased standard errors, significance testing and negative variance components

Day 2

  • Application in repeated measures trials

  • Random coefficients models

  • Generalised linear mixed models (GLMMs) and PROC GLIMMIX
  • Overview of other types of mixed models: for categorical data, highly structured data; brief introduction to the Bayesian approach 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 multi-centre trials treatment standard errors are more appropriately based on between centre variation (fixed effects standard errors are based on within centre variation); in crossover trials more accurate treatment means are often achieved by combining within and between patient estimates. Suitable procedures are readily available for fitting these models well known packages such as SAS. This has led widespread application and knowledge of mixed models becoming essential for medical statisticians. 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


Non-profit making institutions    



Fees include daily morning coffee, lunch, afternoon tea, dinner on the first day, course notes and a copy of the text book "Applied Mixed Models in Medicine" (third edition) by Helen Brown and Robin Prescott.

Members of the International Society for Clinical Biostatistics (ISCB) are entitled to a £25 reduction in the course fee.

The speaker

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. Most of her career has been within academia but she also has experience within the pharmaceutical industry and the health service. She has co-authored three editions of the text book ‘Applied Mixed Models in Medicine’ and taught many short courses on mixed models both in Edinburgh and for external institutions.


The course will be held in the Holiday Inn, Edinburgh-West, one mile from the city centre and easily accessible from the main railway station and airport.


Course participants have the opportunity to stay at the Holiday Inn, Edinburgh-West at a discounted rate of £92 per night. To book at this rate please call the Holiday Inn Events Department on 0131 311 4903 and quote code RI1 or email Alternatively there are several other hotels and guest houses within walking distance of the Holiday Inn.

Terms and conditions

In the event of cancellation, the following charges will apply: if cancelled more than 30 days prior to the first day of the course, a cancellation charge of 50% of the course fee; if cancelled less than 30 days prior to the first day of the course, the cancellation charge will be 100% of the course fee. No charge will apply if an alternative delegate is substituted.

Further information


Phone: 0131 651 2150

Cover of textbook Applied Mixed Models in Medicine

Mixed models analysis of medical data using SAS PROC MIXED and beyond

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.