Department of Biostatistics
Clinical Trials and Methodology Research
The Clinical Trials Research Centre (CTRC) provides a support and advice service to NHS professionals and academic researchers for NHS research. CTRC supports phase I - IV multi centre randomised controlled trials, systematic reviews, core outcome sets, service evaluation and other research. CTRC is part of the Liverpool Trials Collaborative, which is fully registered with the UKCRC.
Visit the CTRC website for further information.
The MRC North West Hub for Trials Methodology Research (NWHTMR) aims to facilitate and accelerate the translational process through the development of new and improved approaches for the design, conduct and analysis of clinical trials, particularly in methodologically challenging areas.
The MRC NWHTMR is a collaboration between the Universities of Liverpool, Lancaster, Bangor and Manchester, involving experts in medical statistics, clinical trial methodology, pharmacoeconomics, sociology and clinical psychology.
Visit the MRC NWHTMR website for further information.
Evidence synthesis is a process for bringing together relevant information from a range of sources and disciplines on a specific research question. This enables a body of evidence to be coherently summarised to help stakeholders make decisions and guide the direction of future studies. Combining results from different studies can be a powerful tool to explore how interventions may work differently for different people or under different conditions.
Members of the evidence synthesis research group are involved in the application of evidence synthesis methods and the development of underpinning methods. We have particular expertise in the areas of network meta-analysis, individual participant data (IPD), selective reporting, and the meta-analysis of joint models, longitudinal data, pharmacogenetics data, time-to-event data, and prognosis studies.
We have strong links with the Cochrane Collaboration through our roles as convenors and members of methods groups, as Cochrane review authors, editorial board members, and through providing statistical support for review groups including Epilepsy, Cystic Fibrosis and Genetic Disorders, Neuromuscular, and Infectious Diseases Groups.
We collaborate with the Liverpool Reviews and Implementation Group (LRiG) to conduct high-quality reviews of the clinical and cost effectiveness of health technologies, commissioned by National Institute for Health Research (NIHR) Health Technology Assessment programme, in the form of Technology Assessment Review.
For further information please contact the lead of the Evidence Synthesis Research Group Professor Catrin Tudur Smith
The Department hosts the Health Informatics Group for the Faculty of Health & Life Sciences (FHLS), a multidisciplinary collaborative chaired by Professor Paula Williamson. The HI group supports the University’s rapidly expanding Health Data Science Network.
Within the Department of Biostatistics, there is a particular focus on the theme of Analytics, Methods and Standards (Prof Paula Williamson and Dr Keith Bodger). The development, validation and application of better and more consistent ways to record, extract and analyse health outcomes is an essential pre-requisite for harnessing the potential of electronic health records and health-related datasets. Members of the department are engaged in developing, sharing and using novel methodologies to enhance the way health outcomes are measured and used in clinical trials, disease registries, administrative datasets and electronic health records to support research, quality improvement and healthcare delivery.
Emerging data collection protocols in clinical research introduce complexities, which are either not covered by existing generally available software, or more fundamentally, require further statistical methodological development. The Department accommodates several inter-linked research groups with interests on methodologies and software of Multivariate data modelling, Joint longitudinal and event history modelling, Statistical Genetics and Pharmacogenomics, Prognostic modelling and Causal analysis.
Multivariate data modelling
Multivariate data modelling is often applied to address research questions such as ‘are there a small number of underlying factors explaining the variables in the dataset?’, ‘is there a natural clustering in the dataset?’, ‘can we classify individuals into groups based on their characteristics?’ or ‘can we predict that a person will develop a particulr condition/disease by looking at a range of symptoms?’
The multivariate modelling research group has expertise in multivariate methodologies, their application to clinical data, and the development of software in R and in other statistical packages. The group aims to develop methodologies and disseminate concepts among researchers and clinicians.
Commonly applied multivariate techniques are discriminant function analysis, cluster analysis, principal components analysis, factor analysis, multidimensional scaling and MANOVA amongst others.
Joint longitudinal and event history modelling
Often in health research, patients are followed-up over time to monitor the progression of a disease as it develops using some biomarkers. Hence, the need for joint modelling of combined longitudinally repeated measurements and event-time data to investigate how the patterns over time in biomarkers relate to prognosis for the patient, and in particular to the timing of clinically significant events. However, a major difficulty is how best to merge information from the longitudinally repeated measurements and event history data, especially as the longitudinal data is usually irregularly and imperfectly observed.
Further, although longitudinal data are prevalent throughout the medical literature, joint modelling methods are not routinely used. Often, simpler approaches are used, for example separate analyses of longitudinal and event-time data, because of the ready availability of standard software. These methods potentially suffer from inefficiency or, worse, severe bias through misspecification, for example by failing to take account of informative dropout during the intended follow-up period.
The JoineR collaboration involves (1) Development of novel statistical methods for the analysis of complex longitudinal data structures, (2) Implementation of user-friendly software for new statistical methods, and (3) Dissemination of the modern methods of statistical analysis to the medical research community.
Statistical Genetics and Pharmacogenomics
The Statistical Genetics and Pharmacogenomics Research Group focus on developing, evaluating and applying methodology for the analysis of genome-wide association (GWA) and re-sequencing studies of complex human traits, with a particular focus on type 2 diabetes and related metabolic traits, women’s health disorders and response to pharmaceutical drugs (efficacy and adverse events).
Key themes of our research include: aggregating GWA studies from diverse populations through trans-ethnic meta-analysis; modelling of multiple correlated traits; analysis of rare variants in gene-based analyses of time-to-event outcomes; evidence synthesis for pharmacogenetic studies.
The findings of our research will contribute to evaluating the utility of GWA studies of common and rare genetic variation for complex trait gene mapping, and will ultimately provide a more comprehensive view of the genetic architecture of common human diseases and drug response.
For further information, contact Prof Andrea Jorgensen, Professor Andrew Morris or visit Statistical Genetics and Pharmacogenomics Research Group website
Prognosis research provides information crucial to understanding, explaining and predicting future clinical outcomes in people with existing disease or health conditions. In particular, clinical prediction models estimate the risk of existing disease (diagnostic prediction model) or future outcome (prognostic prediction model) for an individual, conditional on their values of multiple predictors (prognostic or risk factors) such as age, sex and biomarkers.
A large number of prediction models are published in the medical literature each year, and most are developed using a regression framework such as logistic and Cox regression. Prediction models are also known as risk scores, prognostic indices, or prognostic scores.
The prognosis research group is focussed on (1) Development and validation (both internal and external) methodologies of prognostic models, (2) Extending areas of clinical application, and (3) Disseminating concepts among researchers and healthcare professionals. The group has expertise in the development of software in R language and in other statistical packages, as well as presentation formats based on stakeholder engagement.
Nonadherence is common in general medical practice and trials, with huge economic and clinical consequences. Nonadherence complicates trial analysis and interpretation, as the underlying random assignment mechanism, which forms the basis for unbiased hypothesis testing, no longer reflects treatment received.
In the presence of deviations from randomised treatment, an intention to treat (ITT) analysis only provides an estimate of the policy of prescribing treatment, rather than of the efficacy of treatment actually received. Regulatory and funding bodies are increasingly recognising the importance of considering adherence in trial efficacy analyses, with growing awareness of appropriate statistical causal methodologies which seek to overcome selection biases associated with simple methods such as per protocol analysis. However, such methodologies are not commonly used in trial settings, as they are unfamiliar to trial statisticians and tend to be more complex and computationally intensive than simpler methods.
The causal analysis research group is involved in 1) Application of causal methodologies in randomised trials, 2) Implementation of user-friendly analysis tools to make causal analysis methods easily accessible to trial statisticians and 3) Development of novel methodologies to obtain accurate adherence information to inform causal analyses.
For further information, contact Dr Susanna Dodd.
Bayesian methods offer a different viewpoint to classical frequentist approaches. Rather than treating unknown parameters as fixed quantities, Bayesian approaches treat them as unknown random variables. This enables one to model the uncertainty by defining a probability distribution over the possible values of the unknown parameters (regarded as variables) . Prior to observing data, a prior distribution for each unknown variable is specified based on prior beliefs regarding the likely values of the unknown parameter. After observing data, Bayes theorem is then used to compute the posterior probability for the unknown variables and inferences can then be drawn.
The Bayesian Methods Group is open to research staff and students who apply Bayesian methods in their research. The group has expertise in applying Bayesian methods in a range of contexts, such a meta-analysis, trial analysis and joint modelling. The group can apply methods using a range of software including, Rstan, direct coding in R and WinBugs."
For further information, contact Dr. Sarah Donegan