**The Centre for Mathematical Sciences**** research seminars and events are listed below.**

The four main seminar series are in applied mathematics, pure mathematics, statistics and theoretical physics. Visit the centre's webpages for the latest seminar updates and information.

**16 July, 11:00-12:00 (Room 210, Rolle Building): Xmeta: a comprehensive tool-box for advanced meta-analysis - meta-analysis, publication bias, and beyond**

- Speaker: Yong Chen, University of Pennsylvania

**Abstract: **In this talk, Yong is going to introduce a web-platform that they have been developing over the last few years, known as ‘xmeta’, which aims to facilitate advanced comprehensive meta-analysis for applied investigators within and beyond the Perelman School of Medicine at University of Pennsylvania. The functionality of this platform includes implementation of various multivariate meta-analysis for continuous and/or binary outcomes in randomised controlled trials, meta-analysis of diagnostic tests with/without gold standard, as well as methods to identify and correct for publication biases or small study effects. In addition, Yong will also introduce the online-analysis feature, which allows applied investigators to conduct these analyses without programing by themselves. At the end, Yong will talk about the future direction of incorporating semi-automated text mining to speed up the systematic review process.

Seminar organiser: Dr Yinghui Wei (yinghui.wei@plymouth.ac.uk)

**20 July, 14:00-15:00 (Room 2.13, South Cloisters Building, St Luke's Campus, University of Exeter): Methodological advances in evidence synthesis**

- Speaker: Orestis Efthimiou, University of Bern

**Abstract: **Network meta-analysis (NMA) is an extension of the usual (pairwise) meta-analysis. It is a statistical tool for synthesizing evidence obtained from studies comparing multiple competing interventions for the same disease. In this lecture, we will go through some recent advances in the field. First, we will discuss a new model for the NMA of binary outcomes. This model generalises the well-known Mantel-Haenszel method, and can be especially valuable for the case of rare events, e.g. when synthesising data on mortality or serious adverse events. The method has been implemented in R in freely available, easy-to-use routines. Second, we will discuss models for including non-randomised studies in NMA. Non-randomised studies can reveal whether or not interventions are effective in real-life clinical practice and there is a growing interest in including such evidence in the decision-making process. Here we present and compare an array of alternative methods, and we apply some of the methods in previously published clinical examples. Finally, we will discuss methods for individual participant data network meta-analysis (IPD-NMA). IPD are considered the gold standard in evidence synthesis, and inclusion of IPD in NMA offers unique advantages, such as increase in precision, decrease in heterogeneity, as well as the capacity to individualise the treatment according to a patient’s characteristics. We showcase our methods using an example from depression.

Note: This is a joint RSS South West Local Group and Exeter Health Statistics event.

Seminar organiser: Dr Yinghui Wei (yinghui.wei@plymouth.ac.uk)

**23 July, 14:00-15:00 (venue: Room 210, Rolle Building): N-of-1 Trials for Making Personalised Treatment Decisions with Personalised Designs Using Self-Collected Data**

- Speaker: Christopher Schmid, Brown University

**Abstract: **N-of-1 trials hold great promise for enabling participants to create personalised protocols to make personalised treatment decisions. Fundamentally, N-of-1 trials are single-participant multiple-crossover studies for determining the relative comparative effectiveness of two or more treatments for one individual. An individual selects treatments and outcomes of interest, carries out the trial, and then makes a final treatment decision with or without a clinician based on results of the trial. Established in a clinical environment, an N-of-1 practice provides data on multiple trials from different participants. Such data can be combined using meta-analytic techniques to inform both individual and population treatment effects. When participants undertake trials with different treatments, the data form a treatment network and suggest use of network meta-analysis methods. This talk will discuss ongoing and completed clinical research projects using N-of-1 trials for chronic pain, atrial fibrillation, inflammatory bowel disease, fibromyalgia and attention deficit hyperactivity disorder. Several of these trials collect data from participants using mobile devices. I will describe design, data collection and analytic challenges as well as unique aspects deriving from use of the N-of-1 design and mobile data collection for personalised decision-making. Challenges involve defining treatments, presenting results, assessing model assumptions and combining information from multiple participants to provide a better estimate of each individual’s effect than from his or her own data alone.

Seminar organiser: Dr Yinghui Wei (yinghui.wei@plymouth.ac.uk)

**18 September, 16:30-18:00 (Room tbc, Rolle Building): Statistical Problem Solving: the Art and Science of Learning and Teaching from Data**

- Speaker: Christine Franklin, School Statistics Ambassador, American Statistical Association

The Teaching Statistics Trust Lecture 2018 is for teachers of statistics in all subjects in schools and colleges. The event is free to attend and refreshments are provided.

- Improve your teaching of statistical problem solving and data literacy
- Discover the art and science of learning and teaching from data
- Improve ways to teach statistics in AS- and A-level Mathematics
- Help teaching the Skills Challenge Certificate in social science subjects
- Take away useful teaching resources

**Abstract:** After nearly 40 years as a statistics educator, Christine often reflects about her professional experience with learning and teaching statistics – remembering the past and feeling guilt about how poorly she must have taught her students those first years, trying to stay current with constantly changing pedagogy and assessment in the present, and making predictions about the future. How often do you reflect about your experience as a statistics teacher? Christine often reflects on what a great feeling it is to start each day knowing we work with students and colleagues aiming to see those light bulb moments of understanding the usefulness of data and statistical reasoning skills, but also the importance of being a healthy sceptic of the interpretation of small and big data we are often presented with.

What are the lessons we have learned that will positively impact the data and statistical literacy of our students in the future? Christine has been fortunate to collaborate with amazing colleagues through the years who have enlightened and inspired her to learn these lessons to continue the journey for advocating data and statistical literacy in our society.

In this presentation she will discuss the art and science of learning and teaching from data (as she has experienced from writing four editions of *Statistics: The Art and Science of Learning from Data* and other resources specifically written for teachers) to help you improve teaching statistical problem solving and data literacy at the school level.

Contact Professor Neville Davies (neville.davies@plymouth.ac.uk) or Dr Yinghui Wei (yinghui.wei@plymouth.ac.uk) for any queries.