MRC Biostatistics Unit 2014 Index 2 Director's introduction 4 Introduction to the Unit 6 Statistical Genomics 7 Focus: Detecting Streptococcus pneumoniae serotypes 8 Design and Analysis of Randomised Trials 9 Focus: Smoking cessation trials 10 Evidence Synthesis to Inform Health 11 Focus: Characterising epidemics 12 Complex and Observational Studies in Longitudinal Data 13 Focus: Modelling of disease 14 Emerging Research 14 Statistics and machine learning for precision medicine 15 Stratified Medicine 16 Training and Career Development 17 PhD programme 18 Careers focus 19 Public Engagement 21 Knowledge Transfer 21 Software and Courses 22 Workshops 22 BSU Timeline 24 Maps and contact details to the Unit Director's Introduction The Medical Research Council has had a statistical unit since its inception in 1913. One hundred years on, the Biostatistics Unit (BSU) is one of the largest groups of biostatisticians in Europe, and a major centre for research, training and knowledge transfer, with the mission “to advance biomedical science by maintaining an international leading centre for the development, application and dissemination of statistical methods”. The critical mass of methodological, applied and computational expertise assembled amongst its staff provides a unique and stimulating environment in cutting edge biostatistics, with a balance between innovation and dissemination of statistical methods. Pioneering work involving fundamental aspects of medical statistics, clinical trials and public health has been developed by eminent members throughout the BSU’s rich history. The randomised controlled trial in medicine, Bradford Hill’s criteria for causality and the 2-stage Armitage Doll theory of carcinogenesis are early landmarks. In the eighties, the BSU responded to national priorities and produced HIV AIDS UK projections. In the nineties, the BSU was deeply engaged in making inference for complex data accessible to the scientific community and in producing innovative methodology for performance monitoring. Our current and recent research, on the cost-effectiveness of screening, new trial designs, evidence synthesis methods, longitudinal and multi-state processes and models for linking genetic information to disease, has direct impact on clinical practice and public health. Now Biostatistics is facing new exciting challenges thrown up by fast emerging biotechnological advances as well as new study designs. Epidemiological and biomedical sciences are increasingly taking advantage of new high-throughput technologies, like genetic sequencing, as well as electronic online systems to assemble large and feature-rich datasets requiring in-depth analysis. Our focus is to deliver new analytical and computational strategies for the challenging tasks facing biomedicine and public health. Additionally, and in line with the MRC mission, the BSU has placed strong emphasis on the training of a new generation of biostatisticians, and on producing skilled researchers in this high demand area. Our PhD programme provides opportunities for students from the mathematical sciences or related subjects to enter the world of biostatistics and benefit from rigorous training while engaging with exciting applications. As biostatisticians, we interact closely with biomedical researchers, epidemiologists and public health professionals. Our successful history of anticipating emerging needs for statistical expertise in the health domain and the stimulating scientific environment of the BSU will ensure that we continue to make a significant impact on future statistical practice in biomedicine. Sylvia Richardson Director MRC Biostatistics Unit 2 MRC BSU 2014 Introduction “Our focus is to deliver new analytical and computational strategies for the challenging tasks facing biomedicine and public health.” MRC BSU 2014 3 Introduction to the Unit Research Groups 4 MRC BSU 2014 Programme Leaders Professor Sheila Bird Dr Daniela De Angelis Professor Vern Farewell Dr Adrian Mander Dr Fiona Matthews Dr Sach Mukherjee Professor Sylvia Richardson (Director) Dr Brian Tom Dr Lorenz Wernisch Dr Ian White MRC BSU 2014 5 Statistical Genomics In the past decade, biological and medical research Proposing and improving statistical tools for these has changed dramatically with the ability to tasks is important to ensure that these expensive sequence genomes in a cost effective way and datasets, which are now being collected in many to measure thousands of biological markers that clinical and epidemiological studies, are exploited characterise normal or pathological processes. Such to their full potential. knowledge has a huge potential to improve our understanding of diseases such as cancer, diabetes, The Statistical Genomics research team are cardiovascular and infectious diseases. Particularly developing new and improved techniques for interesting is the exploration of these data for finding important combinations of features in large genetic, life-style and environmental causes of genetic and genomics datasets that characterise or diseases. However, these new biotechnologies predict health outcomes and will therefore lead to produce vast amounts of information making their a better understanding of the underlying biological analysis difficult. mechanisms. Methods are developed in an open- source environment allowing easy adoption by Statisticians are faced with the challenging task of researchers throughout the field. In order to aid finding specific combinations of genetic biomarkers the dissemination and increased utilisation of and risk factors that are related to disease status these methods, we work with collaborators to amongst a vast array of possibilities. In order demonstrate their applicability through case to develop effective treatments, the complex studies related to (among others) autoimmune and interactions of the thousands of components of infectious diseases, type 2 diabetes, coronary heart a cellular system working together in a network disease and a variety of cancers. need to be understood as well, at least to some approximation. 6 MRC BSU 2014 FOCUS: Detecting Streptococcus pneumoniae serotypes Richard Newton is The Bacterial Microarray Group at St. George's, analysing data on University of London (BµG@S) designed a novel Streptococcus pneumoniae, genomic microarray capable of detecting multiple also called pneumococcus, serotype carriage in clinical samples. At the BSU which is the main cause of we developed a sophisticated Bayesian statistical pneumonia and meningitis model for detection and classification of these in children and the elderly serotypes from the microarray data with both and a major cause of high specificity and sensitivity. This method has mortality worldwide. demonstrated an enhanced ability to detect Asymptomatic carriage of multiple serotype carriage and also to determine the pneumococcus in the the relative abundance of serotypes present. nasopharynx facilitates onwards transmission to new hosts and is a pre-cursor to invasive disease. Over 5,000 samples have been analysed to date Colonized individuals may carry more than one from numerous studies worldwide, including pneumococcal serotype and effective detection academic research groups, not-for-profit is essential for understanding the epidemiology organisations and vaccine companies. The BµG@S of disease association, assessing the impact of microarray was shown to be the leading method for vaccine roll-out, monitoring indirect vaccine effects multiple serotype detection by the PneuCarriage via herd immunity and informing future vaccine project, an independent methods evaluation development. funded by the Bill & Melinda Gates Foundation and led by the Murdoch Childrens Research Institute, Australia. This has initiated follow-on funding for further evaluation and roll-out of the method to two regional centres in Australia and South Africa. In addition, numerous translational opportunities for routine adoption of this method in vaccine trials and research studies are being explored with key strategic partners. MRC BSU 2014 7 Design and Analysis of Randomised Trials Clinical trials provide us with the best evidence measurements on individual trial participants to about the benefits and harms of drugs and other inform trial adaptations, maximise patient benefit health interventions. This research theme aims to and increase the number of successful trials. improve how clinical trials are run and then, once the study has completed, how the data collected Despite careful design and conduct, many clinical are analysed and interpreted. trials suffer complications which make the analysis difficult. When individuals allocated to a placebo Traditionally, many clinical trials specify a rigid start taking the active treatment, as happens in protocol with preset numbers of patients on late-stage cancer trials, the benefit of treatment is each treatment or dose, even though there is underestimated, and we are developing statistical potentially limited information about the treatment methods to compare treatment with no treatment. or intervention in question. The techniques When some trial outcomes are incomplete, results developed at the BSU will allow clinical trialists may be biased, and we are also developing to review data as it is collected and improve the statistical methods which can estimate the benefit design of the study as it progresses – for example, of treatment while acknowledging the uncertainty stopping the use of one treatment or increasing about what these missing data might