Presenter(s): Prof Ziv Skhedy (uHasselt) & Prof Khangelani Zuma (HSRC)

Abstract: The workshop covers 2 main topics: (1) an introduction to infectious diseases modelling using R and  (2) survey methodology and policy planing  for HIV/AIDS and monitoring HIV patient under ART treatment. In this workshop, the participants are introduced and exposed  to the topics of modelling infectious diseases (both theory and practical) , HIV/AIDS surveys and monitoring HIV/AIDS patients under ART treatment. Specific objectives are:

• to learn the theory behind transmission models for infectious diseases;
• to be able to implement different transmission models in R using the deSolve R package;
• to understand and interpret transmission parameters;
• to learn about the HIV/AIDS problem in Africa;
• to Understand the main approaches for HIV/AIDS surveys;
• to learn about the 90:90:90 target for HIV/AIDS.

Presenter(s): Prof Geert Molenberghs (uHasselt & KU Leuven)

Abstract: We begin by presenting linear mixed models for continuous hierarchical data. The focus lies on the modeler’s perspective and on applications. Emphasis will be on model formulation, parameter estimation, and hypothesis testing, as well as on the distinction between the random-effects (hierarchical) model and the implied marginal model.

Then, models for non-Gaussian data will be discussed, with a strong emphasis on generalized estimating equations (GEE) and the generalized linear mixed model (GLMM). To usefully introduce this theme, a brief review of the classical generalized linear modeling framework will be presented. Similarities and differences with the continuous case will be discussed. The differences between marginal models, such as GEE, and random-effects models, such as the GLMM, will be explained in detail.

When analyzing hierarchical and longitudinal data, one is often confronted with missing data, due to a variety of (known or unknown) reasons. It will be shown that, if no appropriate measures are taken, missing data can cause seriously jeopardize results, and interpretation difficulties are bound to occur. Precisely, a framework will be sketched to handle incomplete data. Methods to properly analyze incomplete data, under flexible assumptions, are presented. These include ignorable likelihood analysis, ignorable Bayesian analysis, weighted estimating equations, and multiple imputation. To conclude, the issue of sensitivity to non-verifiable assumption is discussed, and addressed through sensitivity analyses.

All developments will be illustrated with worked examples using the SAS System.

Presenter: Prof Lehana Thabane (McMaster University)

Abstract: Conducting subgroup analyses in randomized controlled trials is common to determine subgroups of patients most likely to benefit from interventions. However, there the practice is fraud with problems leading to concerns with the credibility of subgroup analysis results. In this workshop, we address the following:

• What is a subgroup analysis?
• What are the motivations for doing subgroup analyses?
• What are the perils of subgroup analyses?
• What is the current practice of subgroup analyses in trials?
• How can one assess the credibility of a subgroup analysis?
• How should we approach subgroup analyses in trials?
• What are the appropriate regulatory guidances?
• How to think about subgroup analyses in the context meta-analysis

Presenter: Prof Andreas Ziegler (StatSol)

Abstract: The skill of writing an article – and getting it published in a peer reviewed journal – is highly awarded, but rarely taught. This one-day Tim Albert course concentrates on this skill, and it focuses on the process of writing. Participants should come with an idea for an article, and during the day they will work this up in easy stages. The course is strictly limited to a maximum of 12 participants.

By the end of the session participants will:

• understand the publications game – and how to be a winner;
• make logical and informed decisions on where to send articles;
• reduce rewriting in the final stages by early planning;
• cut ‘information overload’ by using simple brainstorming techniques;
• make the most of scarce time by writing for 10 minutes a day;
• use ‘evidence based’ writing to solve disputes with others;
• apply simple marketing techniques to increase the chances of a submitted article being looked at favourably.