PhD Studentships

We offer several PhD projects in Biostatistics. Below is a list projects that we currently offer.

Some projects are fully funded (for UK/EU students; non-UK/EU students will need to pay the excess in tuition fees), some are self-funded (the applicant is asked to pay all the fees).

Interested individuals are encouraged to contact the supervisors of individual projects to gain further information.

Furthermore we invite potential applications for other projects that fit within our expertise.  The Department of Biostatistics offers supervision to PhD students in a wide variety of research areas.  Particular areas of expertise include clinical trials methodology research, evidence synthesis, health informatics, multivariate data modelling, joint longitudinal and event history modelling, statistical genetics and pharmacogenomics, prognostic modelling and causal analysis (see our website for our expertise).

  • Several self-funded studentships in Biostatistics

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    Self-funded project: Complex treatment by covariate interactions in network meta-analysis.

    Funding: Self-funded

    Deadline: We are accepting applications and reviewing them as they arrive

    Primary supervisor: Dr Sarah Donegan (Department of Biostatistics, University of Liverpool).

    Secondary supervisor: Prof Catrin Tudur Smith (Department of Biostatistics, University of Liverpool).

    Project description: For most diseases, many treatments exist. Network meta-analysis (NMA) can estimate the relative effects of all treatment pairings even when treatments are not compared in the same trial. Therefore, NMA has huge potential because it is useful for all clinical fields.

    It is common to explore treatment by covariate interactions in meta-analyses. Interactions can be included in an NMA model to evaluate whether each treatment effect varies with a covariate (e.g. a patient or methodological characteristic, such as, disease severity or allocation concealment).

    The benefits of including interactions in NMA can be substantial. The model can produce the relative effects of all treatment pairings for each covariate value. For example, including an interaction for disease severity (i.e. severe or non-severe) could give one set of the relative effects for patients with severe disease, and another set for patients with non-severe disease. This allows different recommendations to be made for different patient groups; personalising treatment in this way can benefit patients. For example, for the treatment of epilespy, sodium valproate is recommended for patients with generalized seizures whereas carbamazepine is advised for patients with partial seizures. Furthermore, when heterogeneity (i.e. variability across trials) or inconsistency (i.e. variability between direct and indirect evidence) is detected in the NMA without interactions, including interactions provides the opportunity to assess whether the covariate reduces this variabiltiy; this can help analysts understand how best to analyse data and draw valid clinical inferences.

    Currently, research has focused on including interactions for a single covariate (e.g. disease severity), rather than numerous covariates simultaneously (e.g. disease severity and dose). However, it is unlikely that one covariate would cause all heterogeneity or inconsistency if it exists, but instead several covariates would contribute. Therefore, when the purpose of including interactions is to explain variability, it seems sensible to include multiple covariates simultaneously.

    Additionally, current NMA publications explore linear interactions, rather than non-linear interactions. A non-linear interaction is observed when the graph of treatment effect versus a covariate is not a straight line. For example, a review showed that the graph of relative risk of mortality versus BMI was a j-shaped curve. In such cases, if a linear interaction is fitted, the analyst may fail to detect that an interaction exists and this could lead to incorrect clinical guidance.

    The overarching aim of the project is to develop methodology for including multiple, complex treatment by covariate interactions in NMA. The student could develop NMA models including interactions for several covariates and non-linear interactions; highlight the underlying modelling assumptions; develop methods to assess the assumptions; and demonstrate methods using real and/or simulated individual patient data and aggregate data.

    Any enquiries relating to the project and/or suitability should be directed to Dr Donegan (donegan@liv.ac.uk).

    Person specification: The successful candidate is likely to hold a 1st or 2:1 degree in a relevant discipline (statistics or mathematics). A Masters degree in Statistics would be desirable. An understanding of statistical models is essential. Experience of coding in a statistical package (e.g. R, SAS, stata, Winbugs) is desirable.

    Training and support: write and publish their research in journals;

    • present their research findings in person;
    • understand the research topic;
    • write statistical code to apply the models in Winbugs and/or stata;
    • meet and network with other researchers.

    Links: https://www.liverpool.ac.uk/translational-medicine/staff/sarah-donegan/


    Self-funded project: Identifying Copy Number Variants using Whole Exome Sequencing Data.

    Funding: Self-funded

    Deadline: We are accepting applications and reviewing them as they arrive

    Primary supervisor: Dr. Anna Auer-Fowler (Department of Biostatistics)

    Secondary supervisor: Prof Andrew Morris (Department of Biostatistics)

    Project description:  

    Copy Number Variants (CNVs) are a common form of genetic variation and are known to contribute to genetic diseases. Whole Exome Sequencing (WES) is a relatively cheap form of genetic sequencing which targets just the coding regions of the genome. WES is becoming increasingly popular, particularly in clinical applications where the causal genes are known.

    Identifying CNVs from WES is currently unreliable; therefore CNVs are often ignored in WES studies or detected by an alternative and potentially costly technology. Therefore, improving CNV calling from WES data has the potential to reveal important CNVs and reduce the cost by removing the need for alternative technologies.

    The break points of the CNVs generally lie outside of the coding regions targeted in WES, therefore it is currently assumed that the signals associated with the break points will not be observed. However, WES generates a large number of off target reads (40-60% of all reads) some of which will contain additional information for the identification of CNVs. These off target reads have proved informative in other applications but are largely ignored in the field of CNV detection.

    The aim of this project is to improve CNV calling from WES data by incorporating multiple signals and using all reads generated. Additionally, these methodological improvements will be applied to large WES studies and therefore contribute to our understanding of the role of CNVs in complex human traits.

    Any enquiries relating to this project and/or suitability should be directed to Dr Fowler (a.fowler@liverpool.ac.uk).

    Scientific objectives:

    1. Develop a statistical model for CNVs in WES data which integrates multiple signals from on- and off-target reads.  Bayesian approaches are effective in incorporating prior information, such as sequence content, and hierarchically linking multiple samples, and therefore adoption of a Bayesian framework will increase robustness of the model. The 1,000 genomes will act as a ‘gold standard’ data set for bench marking and optimization.

    2. Implement efficient software for this model, allowing it to be applied to large numbers of samples.

    3. Apply it to: (i) 2,500 WES from the Estonian Biobank, for which detailed disease phenotypes and lifestyle data are available; and (ii) 52,000 WES from the T2DGENES Consortium to study the contribution of CNVs to T2D risk and related metabolic traits.

    Person specification:  The successful candidate is likely to hold a 1st or 2:1 degree in a relevant discipline (statistics or mathematics or computing or bioinformatics) preferably with a Masters degree. Experience of programming is essential (e.g. R, C++, Python).

    Training and support: The student will receive support from supervisors to enable them to understand their research, publish their work, attend scientific conferences. Further training in statistics and genetics will be provided through targeted courses run by the Department of Biostatistics and the Institute of Translational Medicine. Additionally, Liverpool University run courses on broader subjects such as scientific writing and computing programming skills if required. Being embedded in the statistical genetics group will allow the student to benefit from the expertise of the group as a whole.  The student will receive broader exposure to statistical genomics as part of the North of England Genetic Epidemiology Group (NEGEG), which offers the opportunity to younger researchers to regularly present their research and to network with other students and postdoctoral researchers based at universities in the North of England. 

    Links: https://www.liverpool.ac.uk/translational-medicine/research/statistical-genetics/


    Self-funded project: Development and application of methodology for “polygenic risk” prediction in pharmacogenetic genome-wide association studies.

    Funding: Self-funded

    Deadline: We are accepting applications and reviewing them as they arrive

    Supervisor: Dr Andrea Jorgensen (Department of Biostatistics)

    Secondary supervisor: Prof Andrew Morris (Department of Biostatistics), Prof Sir Munir Pirmohamed (Department of Molecular and Clinical Pharmacology)

    Project description:  The proposal is focused around development of “polygenic risk scores” for clinical outcomes in pharmacogenetic association studies.  These approaches have been successfully applied in genome-wide association studies (GWAS) of complex human traits, but have predominantly focused on binary outcomes (presence/absence of disease) and quantitative measures (such as anthropometrics and lipid profiles).  However, in pharmacogenetic studies, the outcome of interest is often more complex, such as categorical “sub-phenotypes” (e.g. severity of adverse drug reaction) and “time to event” data (e.g. survival time after clinical intervention). 

    For rare clinical outcomes, such as severe drug-induced hypersensitivity, we also expect a major contribution of rare genetic variants of large effect, which are not widely incorporated in polygenic risk scores for complex traits.

    The primary aim of this project is to develop and apply methodology for polygenic risk scores for complex pharmacogenetic outcomes (including categorical and time to event data).

    Scientific objectives: In order to achieve these aims, the primary objectives of this project are: (i) to adapt methodology previously proposed for polygenic risk scores in the context of binary and quantitative outcomes to complex clinical outcomes in pharmacogenetic studies (including categorical and time to event data); (ii) develop novel methodology to build polygenic risk scores on the basis of gene-based analyses of rare variants; (iii) evaluate utility of incorporating prior biological information on genes/variants associated with related outcomes into polygenic risk scores; and (iv) apply these approaches to a pharmacogenetic GWAS undertaken at the University of Liverpool.

    Person specification:  The successful candidate is likely to hold a 1st or 2:1 degree in a relevant discipline (statistics or mathematics). A Masters degree in Statistics would be desirable. Some experience of working with genetic data would be desirable but not essential. Experience of coding in a statistical package (e.g. R, SAS, stata, Winbugs) is desirable.

    Training and support: The supervisors will provide continuous support to the student, lending their expertise and extensive experience of developing and applying statistical methodology to the analysis of genetic data. The student will also receive training on analysing genetic datasets by attending training workshops (one at University of Liverpool and one at Sanger Institute, Cambridge), and on working with rare variants by attending a training course on this topic (University of Liverpool). The student will also have the opportunity attend training workshops on working with time-to-event data, if required. More general training, such as training on scientific writing and on presentation skills will also be provided through the University of Liverpool’s Doctoral College.

    Links: https://www.liverpool.ac.uk/translational-medicine/staff/andrea-jorgensen/) and Statistical and Pharmacogenetics Research Group (www.liverpool.ac.uk/translational-medicine/research/statistical-genetics/about/)

  • MRC/NIHR Trials Methodology Research Partnership PhD Projects

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    General requirements and application procedure

    You should hold a 1st or 2:1 class undergraduate degree, preferably with a Masters postgraduate qualification.

    Please note the English Language Requirement for EU Students is an IELTS score of 6.5 with no band score lower than 5.5.

    For application enquires please contact Professor Paula Williamson, prw@liverpool.ac.uk .

    To apply please send your CV, cover letter indicating the projects(s) of interest, and the names and addresses of at least two references to prw@liverpool.ac.uk.

    Closing date:  Thursday 21 March 2019 


    PhD projects available

    Improving the development and uptake of COS

    It is increasingly accepted that insufficient attention has been given to the patient outcomes needed to inform decision-making in healthcare. The COMET Initiative (www.comet-initiative.org/) promotes the development of core outcome sets (COS), which are defined as the minimum set of patient outcomes that should be measured for a particular medical condition. COS are becoming an accepted part of the evaluation of healthcare interventions.

    This PhD project could take several directions, depending on the interests of the student. These include: increasing the involvement of people in low and middle income countries, improving the methods for and quality of COS development, patient involvement and participation, COS for public health interventions.

    Opportunities to work with collaborators at NICE (https://www.nice.org.uk/ ), The Global Health Network (https://tghn.org/ ), ECRIN (https://www.ecrin.org/ ), CRIGH (https://www.ecrin.org/news/clinical-research-initiative-global-health-crigh-officially-launched ) and Cochrane (https://www.cochrane.org/ ).

    Suitable for students with background knowledge of quantitative and/or qualitative methods in healthcare research, psychology, public health, social science.

    Co-supervisors: Professor Paula Williamson (prw@liv.ac.uk ), Professor Bridget Young (byoung@liverpool.ac.uk ), Dr Susanna Dodd (shinds@liverpool.ac.uk), Dr Elizabeth Gargon (gargon01@liverpool.ac.uk ), depending on the topic


    Core outcome sets and the healthcare ecosystem

    It is increasingly accepted that insufficient attention has been given to the patient outcomes needed to inform decision-making in healthcare. The COMET Initiative (www.comet-initiative.org/) promotes the development of core outcome sets (COS), which are defined as the minimum set of patient outcomes that should be measured for a particular medical condition. COS are becoming an accepted part of research (such as a clinical trial that will inform decision-making) and clinical practice (such as an audit or in routinely collected health records).

    Electronic health records (EHR) have the potential to facilitate research by providing a readily available source of data. Clinical trials, real-world evaluations, and monitoring of managed entry/early market access could provide more relevant evidence, and be more efficient if COS can be measured, recorded, and extracted from EHR.

    This PhD project will investigate the growing interest in identifying how COS might fit into the different stages of the healthcare research and delivery ecosystem.

    Opportunities to work with collaborators at NICE (https://www.nice.org.uk/ ) and the Liverpool Clinical Trials Centre within the UK Registered CTU Network (https://www.ukcrc-ctu.org.uk/ ).

    Suitable for students with background knowledge of quantitative and/or qualitative methods in healthcare research, psychology, public health, social science.

    Co-supervisors: Dr Susanna Dodd (shinds@liverpool.ac.uk ) and Professor Paula Williamson (prw@liv.ac.uk )


    Automated searching for methodological research articles

    Previous MRC funding has established resources for methods research based on systematic reviews of the literature about outcomes (COMET, www.comet-initiative.org ), trial recruitment (ORRCA I, www.orrca.org.uk ), and biomarker-guided trial designs (BiGTeD, www.bigted.org ). Current MRC HTMR funding will establish a similar resource for trial retention (ORCCA II). Such reviews involve a labour intensive manual process (only 1-6% of abstracts identified being eligible), which cannot keep pace with the rate of research publication.

    Automation has great potential to make systematic reviews quicker and cheaper. Recent advances in text mining, natural language processing and machine learning have demonstrated that tasks within the systematic review process can be automated or assisted by automation.

    This PhD project will review available software and develop improved methods for automating search algorithms, to be tested and used in the future for keeping these repositories up-to-date.

    Opportunities to work with TMRP collaborators undertaking systematic reviews of methodological research topics.

    Suitable for students with background knowledge of computer science.

    Co-supervisors: Professor Carrol Gamble (carrolp@liverpool.ac.uk ), Professor Frans Coenen (Coenen@liverpool.ac.uk ) and Professor Paula Williamson (prw@liv.ac.uk )


    Design and analysis of clinical trials with joint longitudinal and event-time outcome data

    While much progress has been made developing methodology and software for joint modelling for a range of scenarios, its use in the design and analysis of clinical trials is underdeveloped. Challenges include missing data due to dropout, model selection/building, inclusion of information at randomisation.

    This PhD project will investigate the design of clinical trials where joint modelling is to be employed. Using simulation and data from existing studies, you will investigate existing and new methods, develop user-friendly software for sample size calculations, and propose guidelines to guide clinical trialists.

    Opportunities to work with the Liverpool Clinical Trials Centre within the UK Registered CTU Network (https://www.ukcrc-ctu.org.uk/ ).

    Suitable for students with background knowledge of statistics.

    Co-supervisors: Dr Ruwanthi Kolamunnage-Dona (kdrr@liverpool.ac.uk ) and Dr Maria Sudell (mesudell@liverpool.ac.uk )


    Multiple testing for correlated outcomes in clinical trials

    In clinical trials, teams are usually interested in investigating multiple primary and secondary outcomes to obtain regulatory approval for a drug product and make label claims. In some disease areas there may be multiple variables that are of interest to clinicians and regulatory bodies. In some instances, opinions vary and there may be multiple variables to be tested. However, testing multiple outcomes will increase the probability of falsely claiming efficacy and there are a wide range of multiple testing procedures available.

    The aim of this project is to investigate hierarchical and/or multiple testing procedures when the primary and/or secondary key outcomes are correlated. The candidate will perform research into methods to include this correlation (which may be known, but not necessarily) in the testing procedure whilst maintaining the overall significance level at a conventional rate. Clinical trial simulations may also be carried out to investigate in which scenarios the methodology may be especially applicable.

    Opportunities to work with the Liverpool Clinical Trials Centre within the UK Registered CTU Network (https://www.ukcrc-ctu.org.uk/ ).

    Suitable for students with background knowledge of statistics.

    Co-supervisors: Dr Reynaldo Martina (reynaldo@liverpool.ac.uk ) and Professor Catrin Tudur Smith (cat1@liverpool.ac.uk )