Training Calendar

An Introduction to Linear Mixed Models using Stata

Cass Business School, Bunhill Row, London EC1Y 8TZ 2 days (16th March 2020 - 17th March 2020) Stata Various
Delivered by: James Gallagher; Sandro Leidi
Medical statistics, Statistics


Mixed modelling is a modern and powerful data analysis tool for modelling clustered data, typically used for modelling data collected in studies where the levels of a factor are considered to be a random selection from a wider pool, or in the presence of a multi-level structure with different levels of variability.

Mixed models offer potential benefits such as: the ability to model complex data structures, greater generalisability of results, accommodation of missing values and the possibility of increasing the precision of treatment comparisons. In particular, mixed models have been extensively used in trials to analyse repeated measurements where measurements taken over time naturally cluster according to patient.

The course will focus on medical and health related applications of mixed modelling. Specific applications include multi-centre trials and cross-over trials in addition to the analysis of repeated measurements.

The course focuses on the linear mixed model, assuming normally distributed data, and on how to fit linear mixed models and interpret the results for a range of common medical and health related applications.

Only essential theoretical aspects of mixed models will be summarised.

Stata software will be used for practical work and to illustrate analyses in presentations.

Course Agenda

Day 1

Session 1:

  • Random effects and variance components: concept of random vs fixed effects.
  • The variance components model for modelling clustered data.
  • The mixed command for the analysis of mixed models and important options.

Session 2:

  • Blocking: fixed or random? The mixed model for the analysis of a complete and incomplete block design.
  • Comparisons with a fixed effects analysis; benefits of considering a block effect as random.

Session 3:

  • Modelling hierarchies: multilevel modelling for data the design has several levels of variation in a hierarchical structure.
  • Incorporation of fixed effects.

Session 4:

  • Mixed modelling: a brief consideration of maximum likelihood and REML for fitting a mixed model.
  • Model selection - inference for fixed effects, problems in small unbalanced datasets and the Kenward-Roger method.

Day 2

Session 1:

  • Model checking: use of residuals to check the assumptions for a mixed model.

Session 2:

  • Multi-centre trials: analysis of data, using a mixed model, from a clinical trial that follows a single protocol but is conducted at more than one medical institution.
  • Comparisons with a fixed effects analysis.

Session 3:

  • Cross-over studies: modelling data, using a mixed model, from a classical clinical trial design in which all subjects are allocated a sequence of treatments.
  • Advantages over a fixed effects analysis.

Session 4:

  • Repeated Measurements: the analysis of repeated measurement data from a randomised trial. The random coefficient model: motivation, definition and use.
  • The marginal model as an alternative modelling approach: selection of covariance structures for modelling the correlation between successive measurements.
  • Random coefficient vs marginal model.
  • Convergence issues.

Daily Timetable (subject to minor changes)

TimeSession / Description
08:30-09:00 Arrival and Registration
09:00-10:45 Session 1
10:45-11:00 Break
11:00-12:30 Session 2
12:30-13:30 Lunch
13:30-15:15 Session 3
15:15-15:30 Break
15:30-17:15 Session 4

Principal texts for pre-course reading:

  • West, B.T., Welch, K.B. and Galecki, A.T. (2014) Linear Mixed Models. A practical guide using statistical software. 2nd Edition, CRC Press.

Principal texts for post-course reading:

  • Brown, H. and Prescott, R. (2015) Applied Mixed Models in Medicine. 3rd edition, Wiley.


It is assumed that participants are Stata users and are familiar with the practical use of linear models, covering both regression models and ANOVA.

Terms & Conditions

  • Student registrations: Attendees must provide proof of full time student status at the time of booking to qualify for student registration rate (valid student ID card or authorised letter of enrolment).
  • Additional discounts are available for multiple registrations.
  • Cost includes course materials, lunch and refreshments.
  • Delegates are provided with temporary licences for the software(s) used in the course and will be instructed to download and install the software prior to the start of the course. (Alternatively, we can also provide laptops free of charge to attending delegates).
  • If you need assistance in locating hotel accommodation in the region, please notify us at the time of booking.
  • Payment of course fees required prior to the course start date.
  • Registration closes 5-calendar days prior to the start of the course.
    • 100% fee returned for cancellations made over 28-calendar days prior to start of the course.
    • 50% fee returned for cancellations made 14-calendar days prior to the start of the course.
    • No fee returned for cancellations made less than 14-calendar days prior to the start of the course.

The number of delegates is restricted. Please register early to guarantee your place.

  •  CommercialAcademicStudent
    2-Day Pass (16/03/2020 - 17/03/2020)

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