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 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 Dr Daniela De Angelis Professor Vern Farewell Dr Adrian Mander Dr Fiona Matthews Dr Sach Mukherjee Professor (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 really be. the number of patients assigned to another Other issues we are working on include how to treatment – whilst maintaining the integrity of define outcome measures in Systemic Lupus the study. These methods can also use biomarker Erythematosus and in phase II cancer trials.

Lab Studies Human Safety Expanded Safety E cacy & safety Several Years Days or Weeks Weeks or Months Several years

Tens Hundreds Thousands

Preclinical Phase I Phase I/II Phase III

Stages of Clinical Trials

8 MRC BSU 2014 FOCUS: Smoking Cessation Trials

In smoking cessation trials, it is common to assume that all those who fail to provide outcome data have also failed to give up smoking. This widely used, and advocated, “missing = smoking” assumption sets smoking cessation trials apart from those performed in other areas. This is because elsewhere it is common to assume that data are missing at random. A more accessible approach to the analysis of smoking cessation trials with missing outcome In order to investigate this issue, Dan Jackson data is also currently being developed. This allows performed statistical analyses using data from the trialists to make a range of alternative assumptions, iQUIT trial, which is an internet based smoking such as “missing = smoking” or instead that data cessation trial. These analyses used information are missing at random. By exploring how sensitive on the repeated attempts (telephone calls and an important conclusions are to the assumption made email) made to try to obtain participants’ smoking about missing outcome data, trialists can assess statuses. The hypothesis that “missing = smoking” how robust their conclusions are. We call this type was not supported by these analyses. of investigation a “sensitivity analysis”.

MRC BSU 2014 9 Evidence Synthesis to Inform Health

Health-related decision making, whether at population or individual level, needs an understanding of how diseases spread and how interventions will impact on them. This often requires us to identify and combine many sources of information so that recommendations and subsequent decisions are based on a relevant and sound evidence base. For example, when the objective is to control an infectious disease in the community, we need to know how many people are affected by the disease (prevalence) and in which age groups; how fast it is currently spreading (incidence); how it is transmitted (transmission and infectivity); and the geographical locations where the disease is more prevalent. This information feeds into relevant recommendations, such as on vaccination or treatment strategies, which also Evidence on each of these aspects typically comes require an understanding of which subgroups of from several studies, may be incomplete and the population should be vaccinated, or for which biased, refers to populations different from those patients a particular treatment is cost-effective. of interest, and/or is sparse. Robust statistical methods are then needed to integrate such multiplicity of evidence in a coherent manner to make them useful inputs to decision making.

Research in the “Evidence Synthesis to Inform Health” theme aims to develop methods for the design, estimation and assessment of models for combining diverse sources of information in order to answer questions about the optimum management of patients and health resources. As well as developing methods that are general and can be used in a wide range of situations, we apply these methods to important topical questions about infectious diseases, addictions, dementia and health technologies.

10 MRC BSU 2014 FOCUS: Characterising epidemics

Anne Presanis' the modelling of the HIV epidemic, information research focuses on comes from public health surveillance, community the development and surveys and health registries. These data application of methods are combined in an evidence synthesis with to combine multiple information from previous studies or expert data sources to estimate opinion to provide authoritative estimates of the different aspects of how number of individuals infected with HIV informed infectious diseases spread. by all available information. As well as characterising epidemics, Anne also works The group's work on HIV has led to the adoption of on “conflict diagnostic” their model to provide the UK's annual official HIV methods for assessing whether different data prevalence estimates since 2005, informing public sources provide a consistent view of the quantities health policies on HIV. The most recent estimate being estimated. indicates that 98,400 (credible interval 93,500 – 104,300) people were living with HIV in the UK in Anne's work resulted in estimates of the severity 2012, of whom 22% (18-27%) were still unaware of of the 2009 influenza pandemic and estimates their infection. of HIV prevalence and incidence in the UK. In

Schematic Proportion in risk group representation ρ of the model for HIV prevalence HIV prevalence, π showing how the multiple data δ sources are related Proportion to the quantities diagnosed to be estimated, via intermediate functions. ρπδ N ρπδ π(1 δ) ρπδ g − g

NATSAL UA surveys SOPHID SOPHID Proportion Prevalence of Proportion of diagnosed Total number of men who undiagnosed infection attributable to of diagnosed are MSM infection each group infections

Stratified by time, risk group g and region

MRC BSU 2014 11 Complex and Observational Studies in Longitudinal Data

develop new statistical methodology to analyse such data. A particular emphasis is on modelling the relationships between different types of information collected for individuals and on methods to deal with the situation when some potentially desirable information is missing.

We have major involvement with substantive medical research in areas such as ageing, rheumatology, lung function, heart disease, cystic fibrosis and epidemics. This involvement motivates much of our work. In light of this close link with specific applications, key components of our The human and financial costs associated with work are: 1) specification of appropriate questions medical research motivate efforts to make the of interest, 2) consideration of general principles best possible use of the data collected. Current and scope for the translation and adaptation of medical research requires the analysis of data methods across different application areas and that can be very complex, with much of it derived 3) development of new methodology when from recording information for individuals at necessary. Thus, methodology developed within various points in time. Such data are of interest one specific application will be typically applied to many of BSU’s scientists, and pose significant to another to establish generic applicability and methodological challenges. We aim to apply will also be used to inform design and sampling available methodology and, where necessary, aspects of studies.

12 MRC BSU 2014 FOCUS: Modelling of Disease

A major emphasis of Li established a plausible causal link between Su’s research is on the swollen and painful joints and subsequent modelling of disease permanent joint deformities. In addition, novel progression, with a statistical methodology was developed to model particular focus on episodes of disease remission and the factors that rheumatological disease. influence these.

For both psoriatic arthritis, An international collaboration has led to which is an autoimmune comprehensive modelling of neuropsychiatric arthritis associated with and renal manifestations in lupus patients which psoriasis, and for systemic has led to a much better understanding of this lupus erythematosus, feature of the disease. Information on patterns models have been developed to link the variable of neuropsychiatric lupus and predictors of these pattern of potentially reversible disease symptoms have been provided as well as information on the to permanent damage. For example, in psoriatic impact of these and other features of this disease arthritis, an analysis of data on single joints on patients’ quality of life.

MRC BSU 2014 13 Emerging Research: Statistics and machine learning for precision medicine Sach Mukherjee

The overarching goal of Our work addresses statistical and machine learning our research programme questions central to precision medicine, including is the development and issues that arise due to high dimensionality, disease application of statistical heterogeneity and the complexity of underlying and machine learning biochemical processes. Realizing the promise of approaches that can exploit precision medicine will require not only predictive molecular and genomic machine learning tools but also scalable approaches data to better understand to explore biological mechanisms underlying disease heterogeneity and variation in therapeutic response. Accordingly, assist in directing therapies our efforts encompass prediction of therapeutic to patients likely to benefit. Recent biotechnological response, investigation of relevant aspects of advances (including sequencing and proteomics) underlying biological mechanisms and dynamics and offer unprecedented opportunities to transform the approaches that combine mechanistic and predictive clinical management of many diseases by taking into viewpoints. We work on biomedical questions, in account patient-specific molecular characteristics close collaboration with experimental teams in in addition to classical clinical features ("precision the UK, US and Europe, as well as methodological medicine"). This can involve, for example, defining research in statistics and machine learning motivated new disease subtypes or identifying treatments by such questions. that may be suitable for individuals with specific molecular characteristics. Machine learning and Data-driven characterization of signalling networks. Proteomic technologies interrogate signalling dynamics in samples of computational statistics are sister fields that combine interest. Sample-specific network connections between proteins ideas from computer science, mathematics and are inferred using probabilistic models known as dynamic statistics to find patterns in data. Bayesian networks. These models integrate time-course data with existing Primary Data Existing Biology biochemical Sample of Interest Lysate Array (e.g. cancer cell line) knowledge via a Lyse cells and Network Prior prepare cell lysates Denature cellular protein principled approach by SDS sample buffer Serial dilution of cell lysate rooted in Bayesian Array the cell lysate on nitrocellulose coated slides statistics. Time Courses

Probe with validated antibodies to level (a.u.) level quantify phospho-proteins in RTK signaling

phospho−protein time Generate and Validate Testable Hypotheses

Dynamic Bayesian networks & Network Inference

Weight prior information objectively relative to primary data (Set prior strength parameter λ using empirical Bayes method) Inferred Network Topology (specific to biological context)

Capture non-linear, combinatorial effects

Calculate edge probabilities via exact Bayesian model averaging

14 MRC BSU 2014 Emerging research: Stratified Medicine Brian Tom

Unprecedented availability Stratified medicine is a comprehensive strategy for of “Omics” and imaging data, medicine, and specifically for treatment of patients, coupled with an increasing that utilises molecular (biomarker) and clinical effort to collect intermediate information to identify “homogeneous” subgroups phenotypes and clinical of patients who are likely to respond similarly and epidemiological data to treatment, have similar underlying disease for complex diseases both mechanism or have similar outcome risk. It concerns prospectively and through targeting therapies and making optimal decisions at linkage to a wide range of appropriate times for groups of patients with shared routinely collected health biological characteristics. records, has provided enormous opportunities to gain further insight The BSU recognises the importance of research into disease mechanism, outcomes and molecular into stratified medicine and the translation to heterogeneity. clinical practice. It is an emerging theme in the Unit and poses many novel statistical methodological This insight may: challenges for design, modelling, computation, discovery and validation. We are undertaking a • inform medical or public health interventions, programme of research that encompasses clinical • improve the design of future studies, trial design and sampling, risk stratification, • help identify relevant parts of the biological prediction and validation, integrative and system “out of control”, joint modelling of biomarkers, mechanistic understanding and dynamic treatment regimes. • aid in the discovery of new pharmacological We have established links to current MRC strategic targets and the design/development of initiatives on stratified medicine through our preventative, diagnostic and therapeutic tools partnerships with consortia in rheumatoid arthritis, and medicines, Gaucher disease and the UK dementia platform. • allow prediction of adverse events and response or eventual failure to treatment, and • allow the monitoring and forecasting of disease course and progression.

MRC BSU 2014 15 Training and Career Development

Currently more than 60 staff, students and support staff make up the MRC Biostatistics Unit.

The BSU places a strong emphasis on training the statisticians of the future. Through our Career Development and PhD programmes we are committed to filling the strategic skills gap in statistics identified by Professor Adrian Smith in 2010.

Furthermore the Unit provides a unique environment in which researchers apply their statistical and mathematical knowledge to real data to solve real problems.

Alumni of the BSU have gone on to many varied and exciting careers across the world and many are returning back to Cambridge in 2014 to help us celebrate our Centenary year.

Staff Numbers - March 2014 Senior Visiting Computing Group Leaders Investigator Investigator PhD Students Workers and and Admin Total Statisticians Statisticians Students support staff 10 12 13 14 9 13 71

16 MRC BSU 2014 PhD Programme

The MRC Biostatistics Unit provides an ideal place to commence a career in Biostatistics. Our thriving PhD programme is hugely successful and PhD Alumni first destinations- location competition for places on the programme is high. The BSU's relaxed yet enthusiastic, dedicated and stimulating environment provides students with 21% the essential tools, both in research and transferable 58% skills, to set them on their way to a successful career as independent researchers. 21%

BSU "I love being Director of UK Studies here because the Rest of World PhD students teach me Ian White so much!" Quotes from BSU PhD Alumni

“I chose to do a Ph.D. at the BSU because of its strong reputation for high-quality research in Biostatistics. My project PhD Alumni first destinations- employment sector required a lot of computation, so I definitely appreciated the excellent computing facilities and very helpful computing staff. I also found the Unit to be a fantastic place 8% to study with very friendly people and a lot of opportunities to present and discuss work. In fact I liked it so much that I stayed 92% on to do a postdoc!”

“The BSU is great: interesting projects, excellent support and a nice, friendly Academia environment. I'm really glad that I studied Business for my PhD here and I'd highly recommend it to others”

MRC BSU 2014 17 Careers Focus

Jack Bowden Olympia Papachristofi

Jack's work on “Unbiased Olympia is a third year PhD estimation of odds ratios: student. After completing combining genome wide undergraduate studies in scans with replication Mathematics at Imperial studies” was awarded College London and ‘best paper of 2009’ by obtaining the MAST the International Society in Pure Mathematics of Genetic Epidemiology. from the University of In 2014 he was awarded Cambridge, she obtained a four year Methodology Research Fellowship by an MRC studentship for a PhD in Biostatistics. the MRC to develop methods for bias-adjusted After nomination by the Unit, she subsequently inference in Biostatistics, with applications to received a Gates scholarship awarded by the Bill clinical trials, epi-genetics, stratified medicine and and Melinda Gates Foundation. Her current project meta-analysis. deals with the assessment of the impact of learning curves, multiple operators and non-proportional As Jack explains: “In today's competitive world, hazards in clinical trials of surgical procedures and scientists are under pressure to collect evidence as devices. efficiently as possible by making use of existing data sources, novel trial designs and the latest technology. Olympia comments: "From the beginning, the Unit However, failure to understand the process by which felt like an ideal place for a young researcher to start data are created can lead to a biased picture of their career; the interaction with scientists of diverse the information provided, leading to poor medical statistical expertise and the breadth of opportunities decision making. My research will develop the to attend courses and seminars is a great way to gain methodological tools to help alleviate this general invaluable experience. Most importantly, it is such a problem”. friendly and cosy environment to work in; everyone is very approachable, which creates a great support system and keeps me reassured there is always someone around to reach out to for advice. Obtaining a Gates Scholarship gave me the opportunity to be a member of an amazing group of international students of diverse and interesting backgrounds and benefit from scholar organised events that ranged from a Scholars' yearly-trip to the Lake District to the GSS conference, with last year's key note speaker being His Holiness the Dalai Lama."

18 MRC BSU 2014 Public Engagement

Engagement with the public The MRC Biostatistics Unit are regular exhibitors at the annual Cambridge Science Festival which is held in the Spring each year and regularly attracts over 30,000 visitors. We have developed an exciting array of interactive exhibits which are designed to stimulate interest in biostatistics amongst the participants.

We have also exhibited at both the local and national 'Big Bang' events whose audience is primarily school children.

BSU in the Media BSU science is often featured in the media. News coverage ranges from radio interviews and soundbites to stories covering our findings in the print and online media.

Example topics of BSU media coverage • Analysis of epidemics such as swine-flu, Hepatitis C and AIDS • Analysis of disease prevalence in Alzheimer's disease • Cost-effectiveness studies ranging from screening for abdominal aortic aneurysm to implantable devices • Drugs-related deaths and military deaths • Performance monitoring of government organisations

MRC BSU 2014 19 Knowledge Transfer

Other more recent software developed in the Unit such as "ice" and "msm" have also been cited over 2,000 times. We continue to develop software and computational code which we regularly distribute. All software developed in the BSU is made open source.

Courses The BSU runs a number of short courses at the Cambridge Institute of Public Health every year. Topics range from courses on our software such Software as "Introduction to Bayesian Monte Carlo Methods The BSU has a long history of successful software in WinBUGS software" to more methodological development. Our most famous software courses such as "Practical Use of Multiple 'WinBUGS' celebrated it's 25 year anniversary in Imputation to Handle Missing Data". 2010 and it's user manual has been cited over 3,000 times in research articles.

Our courses are very well UK attended (351 participants

Europe over 14 courses in the period 2010-2012) and delegates come from all over the world to attend them. The figure left shows a diagramatic representation of the location of employer and employment sector of the participants over this period of time.

Key

Student

Academia

Non-EU Industry

20 MRC BSU 2014 Knowledge Transfer

Armitage Lecture Series We are honoured that Professor Armitage is still a Inaugurated in 2002, regular attendee at BSU events. the BSU holds an annual Workshops lecture and workshop named in honour of Other workshops we have hosted, with the aim of Professor bringing together key people, have covered topics CBE who was at the Unit such as: from 1947 to 1961. • Explaining the results of complex Every year in late autumn probabilistic modelling: conflict, an internationally consistency and senstivity analyses established senior researcher is invited to spend • Methods for Adjusting for Treatment a week in the Unit giving seminars and hosting Switches in Late-Stage Cancer Trials informal discussions to give an opportunity to learn • NICE encounter: evidence synthesis about the work of the Unit and build collaborations. The Armitage Lecture itself is the centrepiece and the public health around which a full-day workshop is built. • Adaptive trial design research

Recent Armitage Lecturers include Professor Giovanni Parmigiani (Harvard School of Public Health), Professor Els Goetghebeur (University of Gent) and Professor Jerry Lawless (University of Waterloo).

MRC BSU 2014 21 BSU timeline 1963 1910s MRC 1992 1913 Leukaemia 1965 Trials BUGS program: 1994 Bradford Hill's Bayesian Surgical 2010s inference Advisory criteria for causation Performance: Using Gibbs Council league tables, Sampling and Medical Research Committee Bristol Inquiry, And the story goes on... appointed; with the Statistical Department 1960 risk-adjustment as one of four founding departments 2009 Armitage's 1990s Complex and 1918 Sequential 1966 1995 Cost- Trials 1990 effectiveness War Work: Cancer Observational The statistics Transmissible of ultrasound on munition epidemiology of genetic disease screening of Longitudinal workers & linkage epidemiology: abdominal epidemics studies BSE, vCJD, aortic aneurysm data 1960s Hepatitis C virus, swine-flu 1956 2004 1920s Radiation 1967 1996 Multiple 1989 Imputation hazards to Safety 1922 Epilepsy: Gilks, Richardson software in man: of oral natural and Spiegelhalter: Stata released Greenwood’s Hiroshima contraceptives history and Markov Chain standard error and Monte Carlo for life-tables leukaemia randomized Statistical Genomics trials Methods in Practice 1954 2004 1970s Cochrane Armitage & Doll's 1988 1998 multi-stage Reviewers’ 1927 1978 Database linkage: Handbook, theory of AIDS/HIV: high risk of Issue 1 : carcinogenesis Safety of amniocentesis UK’s projections, drugs-related published 1st female Silver immunological deaths soon after Guy Medallist surveillance, prison-release of the Royal prisons and Statistical Society progression 1954 2002 MRC 2000s 1987 Multi-state Markov models for disease Tuberculosis 1980s progression: MSM software first released Design & Analysis of Randomized Trials 1930s Trials: efficacy Breslow & Day’s 2000 Lab Studies of the BCG Statistical Methods Human Safety 1931 1981 Cost-effectiveness Several Years Expanded Safety Days or Weeks E cacy & safety vaccine in Cancer Research: modelling Weeks or Months Hilda Mary Woods Gore's Statistics The Design and Several years Tens in Question: series Hundreds & William T. Russell: Analysis of Cohort Thousands in British Medical 1st modern 1950 Studies textbook on Journal 2002 Preclinical Phase I Medical Statistics Smoking and Phase I/II 1986 Phase III lung cancer, Bayesian The Statistics 2000 the mortality measures of Screening Stages of Clinical Trials 1937 of doctors in 1982 The of model relation to statistics complexity Hill's Lancet series Statistics in Medicine: smoking of ageing and fit and book on Colton, Freedman and Johnson Principles of 1950s Evidence-synthesis to Inform Health Medical Statistics founding editors UNDIAGNOSED AND INCIDENT INFECTIONS IN MSM 2002-12 1948 1984 2002 2002 8000 First MRC The Statistics Multicentre BREATHE Trial randomized 1983 Betting ‘Streets’ for Mortality of Surfactant-treated babies of Transplantation Higgins and Thompson’s I2: quantifying 6000 1940s 15% Aneurysm NUMBER OF 20% 25% 30% 35% INFECTIONS controlled trial: 40% 45% in the UK heterogeneity in a meta-analysis Screening 4000 streptomycin Bayesian 1942 INDIVIDUAL PRIOR Study (MASS) Methods OPINION treatment of RELATIVE 2000 FREQUENCY trial World War II in 0.25 pulmonary tuberculosis 0.20 0.15 CONCENSUS 0 & Tuberculosis Clinical PRIOR 2002 0.10 2004 2006 2008 2010 FROM YEAR 2012 0.05 15 OPINIONS Trials Estimated Incidence Undiagnosed Infection 15% 20% RANGE OF 25% 30% 35% 40% EQUIVALENCE CONTROL MORTALITY 45% BSU timeline 1963 1910s MRC 1992 1913 Leukaemia 1965 Trials BUGS program: 1994 Bradford Hill's Bayesian Surgical 2010s inference Advisory criteria for causation Performance: Using Gibbs Council league tables, Sampling and Medical Research Committee Bristol Inquiry, And the story goes on... appointed; with the Statistical Department 1960 risk-adjustment as one of four founding departments 2009 Armitage's 1990s Complex and 1918 Sequential 1966 1995 Cost- Trials 1990 effectiveness War Work: Cancer Observational The statistics Transmissible of ultrasound on munition epidemiology of genetic disease screening of Longitudinal workers & linkage epidemiology: abdominal epidemics studies BSE, vCJD, aortic aneurysm data 1960s Hepatitis C virus, swine-flu 1956 2004 1920s Radiation 1967 1996 Multiple 1989 Imputation hazards to Safety 1922 Epilepsy: Gilks, Richardson software in man: of oral natural and Spiegelhalter: Stata released Greenwood’s Hiroshima contraceptives history and Markov Chain standard error and Monte Carlo for life-tables leukaemia randomized Statistical Genomics trials Methods in Practice 1954 2004 1970s Cochrane Armitage & Doll's 1988 1998 multi-stage Reviewers’ 1927 1978 Database linkage: Handbook, theory of AIDS/HIV: high risk of Issue 1 Ethel Newbold: carcinogenesis Safety of amniocentesis UK’s projections, drugs-related published 1st female Silver immunological deaths soon after Guy Medallist surveillance, prison-release of the Royal prisons and Statistical Society progression 1954 2002 MRC 2000s 1987 Multi-state Markov models for disease Tuberculosis 1980s progression: MSM software first released Design & Analysis of Randomized Trials 1930s Trials: efficacy Breslow & Day’s 2000 Lab Studies of the BCG Statistical Methods Human Safety 1931 1981 Cost-effectiveness Several Years Expanded Safety Days or Weeks E cacy & safety vaccine in Cancer Research: modelling Weeks or Months Hilda Mary Woods Gore's Statistics The Design and Several years Tens in Question: series Hundreds & William T. Russell: Analysis of Cohort Thousands in British Medical 1st modern 1950 Studies textbook on Journal 2002 Preclinical Phase I Medical Statistics Smoking and Phase I/II 1986 Phase III lung cancer, Bayesian The Statistics 2000 the mortality measures of Screening Stages of Clinical Trials 1937 of doctors in 1982 The of model relation to statistics complexity Hill's Lancet series Statistics in Medicine: smoking of ageing and fit and book on Colton, Freedman and Johnson Principles of 1950s Evidence-synthesis to Inform Health Medical Statistics founding editors UNDIAGNOSED AND INCIDENT INFECTIONS IN MSM 2002-12 1948 1984 2002 2002 8000 First MRC The Statistics Multicentre BREATHE Trial randomized 1983 Betting ‘Streets’ for Mortality of Surfactant-treated babies of Transplantation Higgins and Thompson’s I2: quantifying 6000 1940s 15% Aneurysm NUMBER OF 20% 25% 30% 35% INFECTIONS controlled trial: 40% 45% in the UK heterogeneity in a meta-analysis Screening 4000 streptomycin Bayesian 1942 INDIVIDUAL PRIOR Study (MASS) Methods OPINION treatment of RELATIVE 2000 FREQUENCY trial World War II in 0.25 pulmonary tuberculosis 0.20 0.15 CONCENSUS 0 & Tuberculosis Clinical PRIOR 2002 0.10 2004 2006 2008 2010 FROM YEAR 2012 0.05 15 OPINIONS Trials Estimated Incidence Undiagnosed Infection 15% 20% RANGE OF 25% 30% 35% 40% EQUIVALENCE CONTROL MORTALITY 45% MRC Biostatistics Unit Cambridge Institute of Public Health, Forvie Site, Robinson Way Cambridge Biomedical Campus , Cambridge CB2 0SR

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Printed March 2014