From 16th to 19th October 2017, on the initiative of Prof Stephen Senn, Head of LIH’s Competence Centre for Methodology and Statistics, two training sessions were organised for the research community and pharmaceutical industry on how to optimise evidence from clinical trials.
When designing a clinical trial, the right sample size must be defined. The first course, given by Prof Steven JuliousJulious from the University of Sheffield, covered the crucial task of deciding how much evidence is needed when planning your clinical study. The participants had the opportunity to learn about calculations for sample size estimation and how the objectives of the clinical trial and the types of data can impact on these calculations.
The second course given by Prof Stephen Senn, Competence Centre for Methodology and Statistics, and Dr Susanne Schmitz, Health Economics and Evidence Synthesis Research Unit, Department of Population Health, covered evidence synthesis from clinical trials and the methods to perform meta-analysis. Meta-analysis is the application of statistical methods to summarising the evidence from independent studies investigating the same questions.
Both courses took place at “Hôtel Parc Belle-Vue” in Luxembourg City. They were targeted at statisticians, regulatory scientists, Clinical Research Associates, clinical epidemiologists, medical advisors and health economists and gathered 30 and 23 participants, respectively.
Prof Senn said afterwards: ‘It is a strange experience to attend lectures by a former PhD candidate (Prof Julious did his PhD with me). I found his course enjoyable and useful, but demanding. What was particularly valuable were the cunning and carefully crafted exercises challenging one to extract the necessary information to plan future trials from publications from similar previous trials that were not reported in the detail one might have desired.’ He adds: ‘It was also a pleasure to collaborate with Dr Schmitz in giving the meta-analysis course. Her Bayesian perspective nicely complemented my frequentist one.’
