Training Calendar

2019 Stata Autumn School, London

Cass Business School 6 days (23rd September 2019 - 28th September 2019) Stata Advanced, Intermediate
Autumn School, Bayesian Statistics, Medical statistics, Missing Data, Network Meta Analysis, Statistics


Our Stata Autumn School comprises a series of six, 1-day courses delivered by experienced biostatisticians and econometricians: Robert Grant founder of BayesCamp (London), Dr Matteo Quartagno from ICTM-UCL (London), Prof Aurelio Tobias from IDAEA-CSIC (Barcelona) and Dr Giovanni Cerulli from CNR-IRCrES (National Research Council of Italy - Research Institute on Sustainable Economic Growth). Each course will include an initial introduction to the topic followed by hands-on examples. There will be plenty of time dedicated to interpretation of the results, discussion of assumptions and comparison of approaches.

This is a great opportunity for students, academics and professionals to expand their statistical skills and learn how they can apply statistics from biostatisticians at the forefront of their specialist fields. The combination of separate courses allows complete flexibility to register only the courses that they find most relevant to their research interests.

The separate courses comprising the Stata Autumn School are:

  • Course 1: Introduction to Bayesian Analysis
  • Course 2: Multivariate Analysis and Unsupervised Learning
  • Course 3: Analysis of Multilevel and Longitudinal data
  • Course 4: Dealing with missing data
  • Course 5: Network Meta-analysis
  • Course 6: Structural Equation Modelling

Timberlake Consultants are the official Stata distributors to the UK, Ireland, Middle East and North Africa, Spain, Portugal, Poland and Brazil.


Course 1: Introduction to Bayesian Analysis

Date: Monday, 23 September 2019
Delivered by: Dr Robert Grant, BayesCamp
Learning ratio: 50% theory, 50% practical

A one-day course aimed at beginners in Bayesian analysis, which will get you up and running with Stata’s capability from version 14 onward. You will also gain an understanding of the computation that takes place behind the scenes, as this helps with refining your models.

Course Overview:

Bayesian methods data analysis differ from traditional, frequentist analysis in that we think about how the data came to be, and then represent that by connecting together probability distributions. For example, a hierarchical logistic regression with missing data will have distributions for each of the missing values, a distribution for the cluster-level differences, distributions for the unknown parameters, and finally a distribution for the residuals around the predicted values. By connecting together probability building blocks like this, we can make complex, flexible models for our data. This helps us account for problems like missing data,

The course will cover the following:

  • Session 1: How does Bayesian analysis work? Simulation, Markov chain Monte Carlo, prior and posterior distributions, data-generating processes. Practical session to get up and running with Stata’s bayes prefix.
  • Session 2: Opportunities and pitfalls when building more complex models. Practical session looking at more bayes commands and introducing bayesmh
  • Session 3: Critiquing and refining your model: convergence and mixing.
  • Session 4: Linking beyond Stata: BUGS, JAGS and Stan.

Course 2: Multivariate Analysis and Unsupervised Learning

Date: Tuesday, 24 September 2019
Delivered by:Dr Robert Grant, BayesCamp
Learning ratio: 50% theory, 50% practical

Course Overview:

This one-day course is ideal for anyone embarking on the analysis of data with many variables, where there is no “outcome” or dependent variable. Many exploratory techniques are available in Stata to help you tackle this sort of analysis. Statisticians might call them multivaraiate analysis or exploratory data analysis, while machine learning people might call them unsupervised learning.

This one-day course introduces Stata functions to find patterns in very wide datasets. We will include analytical and visualisation methods related to: clustering, dimension reduction, automatic outlier detection, shape analysis, factor analysis, and item-response theory.

The course will cover the following:

  • Session 1: Dimension reduction: principal components analysis, exploratory factor analysis and correspondence analysis
  • Session 2: Clustering: k-means and k-medians, hierarchical clustering and dendrograms
  • Session 3: Distances and shapes: outliers, Procrustes analysis, multidimensional scaling, biplots and symmetric maps.
  • Session 4: Links beyond today: structural equation models (see Course 6 by Giovanni Cerulli), discriminant analysis, confirmatory factor analysis.

Course 3: Analysis of Multilevel and Longitudinal data

Date: Wednesday, 25 September 2019
Delivered by: Dr Matteo Quartagno (ICTM-UCL)
Learning ratio: 50% theory and 50% practical

We will introduce the course with examples of settings where the usual assumption of independent units of analysis does not hold. If this dependency is ignored, any subsequent inferences are potentially invalid. Dependency therefore must be dealt with. We will discuss alternative approaches to achieve this, with the focus initially on methods that explicitly specify the nature of the dependency, i.e. mixed effects models. We will start by considering the situation where the outcome is continuous and there are only two levels of aggregation. As mixed effects models are a development of ANOVA and the linear regression model, this is where we will start. We will then introduce random intercept models, with and without covariates, using the mixed command, and then more general random coefficient models. Throughout we will discuss assumptions and ways to assess the appropriateness of the fitted model. In the afternoon, we will consider examples where the outcome is not continuous, introducing generalized linear mixed effects models, available in Stata through the meglm command. We will conclude the day by introducing Generalised Estimating Equations, an alternative approach that targets population-averaged, rather than subject-specific, effects. We will see how to implement this method in Stata with the xtgee command.

  • Session 1: Impact of dependency, choice of strategies and revision of linear regression
  • Session 2: The random coefficient model
  • Session 3: Generalised Linear Mixed Models
  • Session 4: Generalised Estimating Equations

Course 4: Dealing with missing data

Date: Thursday, 26 September 2019
Delivered by: Dr Matteo Quartagno (ICTM-UCL)
Learning ratio:
 50% theory and 50% practical

We begin by illustrating with a simple dataset the adverse consequences missing data can have on inferences. Next, we give an intuitive explanation of Rubin’s classification scheme for missingness mechanisms (MCAR, MAR, MNAR), and explore how missingness mechanisms can be described and investigated using Stata. We then move on to a brief discussion of the deficiencies in several commonly used ad-hoc approaches to handling missing data before we introduce the method of multiple imputation (MI), a principled approach for handling missing data under the MAR assumption. Both joint model and chained equations imputation will be described, and we will apply these to data using Stata's MI commands. We briefly introduce an alternative approach to handling missing data, that of inverse probability weighting, and illustrate how this is readily performed in Stata, and conclude by emphasising the important role of sensitivity analyses when analysing partially observed datasets.

  • Session 1: Impacts of missing data, classifying missingness mechanisms and ad-hoc methods
  • Session 2: An introduction to multiple imputation (MI) (single variable)
  • Session 3: Multiple imputation for multiple variables (chained equations)
  • Session 4: Inverse probability weighting and sensitivity analyses after MI

Course 5: Network Meta-analysis

Date: Friday, 27 September 2019
Delivered by: Prof. Aurelio Tobías, Spanish Scientific Research Council
Learning ratio: 50% theory and 50% practical

A one-day course that is aimed at both academics and practitioners, with a basic knowledge of Stata, who are interested in applying network meta-analysis using Stata commands designed for this purpose.

Course Overview:

Meta-analysis has traditionally been used to synthesize the effectiveness of an intervention from a collection of studies. However, when there are no studies directly comparing two or more interventions, traditional meta-analysis cannot estimate their comparative benefits. Although if there is information available regarding the effectiveness of two interventions, named B and C, in comparison to a common comparator A, an indirect treatment comparison may be used to estimate a comparison of the effectiveness of B compared with C. Approaches to meta-analysis have been increasingly implemented to estimate the effects of multiple interventions, taking into account the full network of available studies and simultaneously incorporating direct and indirect comparisons.

This one-day course introduces the main statistical techniques to analyse a network meta-analysis in practice using the network suite of Stata commands. It is aimed at both academics and practitioners, with a basic knowledge of Stata, who are interested in applying indirect comparisons and network meta-analysis using Stata commands designed for this purpose.

The course will cover the following:

  • Session 1: Types of comparisons in meta-analysis. Indirect comparisons with meta-regression
  • Session 2: Multivariate meta-analysis
  • Session 3: Full network meta-analysis
  • Session 4: Practical exercises using the network suite of commands

Course 6: Structural Equation Modelling

Date: Saturday, 28 September 2019
Delivered by: Dr Giovanni Cerulli (CNR-IRCrES)
Learning ratio: 50% theory and 50% practical

This course provides participants with the essential tools, both theoretical and applied, for a proper use of structural equation models (SEM) for statistical causal modelling using Stata. After attending the course, the participant will be able to master articulated causal designs, by identifying, estimating and testing both direct and indirect causal effects in the presence of unobservable endogeneity, selection bias, measurement error, and simultaneity. Participants will obtain extensive hands-on experience by working on real datasets examples from different social and biomedical sciences. Technical treatment of the subjects will be set out only to properly address real-world applications. Delegates will capitalize on visual intuitive graphical representations of causal links, as well as on traditional algebraic approach. The course will enable them to recognize and design causal paths in their own studies, by understanding underlying assumptions in different fields of application.

  • Session 1: Structural equation modelling for path models. Path-model terminology and notation. Identification and estimation of direct, indirect, and total effects. Recursive and nonrecursive models.
  • Session 2: Estimation of a full structural equation model. Tests for SEM reliability and goodness-of-fit.
  • Session 3: Use of the Stata 15 SEM packages sem and gsem. Using the SEM Builder: an example
  • Session 4: Fitting, modifying and constraining a SEM with sem and gsem

Pre-course readings

  • Course 1:
    • Catalá-López F, Tobías A, Cameron C, Moher D, Hutton B. Network meta-analysis for comparing treatment effects of multiple interventions: an introduction. Rheumatol Int. 2014; 34(11): 1489-96.
  • Course 2:
    • StataCorp, (2013). Data-Management Reference Manual, Stata Press.
  • Course 3:
    • Multilevel Modelling resources (available via the Centre for Multilevel Modelling, University of Bristol).
  • Course 4:
    • Sterne, J., et al., (2009). Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls, 338: b2393, BMJ.
    • Schafer, J. L., (1999). Multiple imputation: a primer, 8: 3-15, SMMR.
  • Course 5:
    • StataCorp, (2013). Structural Equation Modeling, Stata Press. (Stata 13 or Stata 12 release).
    • Kleinbaum, D., and Klein, M., (2011). Survival Analysis, a Self-Learning Text, Springer.
  • Course 6:
    • StataCorp, (2013). Survival Analysis and Epidemiological Tables, Stata Press.
    • Kleinbaum, D., and Klein, M., (2011). Survival Analysis, a Self-Learning Text, Springer.

Suggested Readings

  • Course 1:
    • Van Buuren, S., (2007). Multiple imputation of discrete and continuous data by fully conditional specification, 16: 219-242, SMMR.
    • Carpenter, J. R., & Kenward, M. G., (2012). Multiple imputation and its application, Wiley.
  • Course 2:
    • Levy, P., and Lemeshow, S., (1999). Sampling of Populations, Wiley.
  • Course 3:
    • Cleves, M., Gould, W., Gutierrez, R. G., and Marchenkov, Y. V., (2010). An Introduction to Survival Analysis Using Stata, 3rd Ed., Stata Press.
  • Course 4:
    • Rabe-Hesketh, S., and Skrondal, A., (2012). Multilevel and Longitudinal Modeling Using Stata, Volume I: Continuous responses, 3rd Ed.. Stata Press.
  • Course 5:
    • Introductory: Kline, R. B., (2004). Principles and Practice of Structural Equation Modeling, 2nd Ed., New York: Guildford.

    • Advanced: Skrondal, A., and Rabe-Hesketh, S., (2004). Generalized Latent Variable Modeling, Boca Raton, Fla: Chapman.

DAILY TIMETABLE (subject to minor changes)

08:45-09:15 Registration

09:30 - 11:00 Session 1

11:00 - 11:15 Tea / coffee break

11:15 - 12:45 Session 2

12:45 - 13:45 Lunch

13:45 - 15:15 Session 3

15:15 - 15:30 Tea / coffee break

15:30 - 17:00 Session 4

  • 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, laptops can be hired for a fee of £10.00 (ex. VAT) per day).
  • If you need assistance in locating hotel accommodation, please notify us at the time of booking. Please note: do not book accommodation or travel until the course has been confirmed
  • 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.

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All prices exclude VAT or local taxes where applicable.

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