Dynamic risk prediction for patients with multiple myeloma

One full scholarship is available in the Leeds Institute of Clinical Trials Research in the School of Medicine in 2024/25. <br /> <br /> This scholarship is open to UK applicants and covers fees plus £19,237 maintenance. This fully funded PhD place provides an exciting opportunity to pursue postgraduate research in cancer clinical trials methodology within the Leeds Cancer Research UK Clinical Trials Unit at the Leeds Institute of Clinical Trials Research. The Institute is an international leader in the field of clinical trials and the CRUK Unit is one of the largest in the UK being one of only 7 clinical trials units to receive a prestigious infrastructure award from Cancer Research UK. We conduct national and international early phase and late phase clinical trials specialising in blood cancers, treatment with radiotherapy, and colorectal cancer, and this PhD will integrate into these portfolios. <br /> <br /> We invite applications from prospective postgraduate researchers who wish to commence study for a PhD in the academic year 2024/25 for the CRUK Clinical Trials Unit Scholarship. The award is open to full-time or part-time candidates (UK only) who meet the eligibility for a place on a PhD degree at the School of Medicine.

<h5>Background</h5> <p>Multiple myeloma (MM) is a cancer of the bone marrow for which there is currently no cure. Standard treatment recommendations for newly diagnosed myeloma patients usually consist of continuous therapy for life. Teams treating MM and their patients require a clinical prediction model that provide personalised prediction of risk at key clinical milestones along the disease-treatment pathway to aid collaborative informed decision making.</p> <p>Current prediction models are mainly based on prognostic factors measured at baseline, generally at diagnosis or start of treatment [1,2]. Disease response, including minimal residue disease (MRD), is one of the strongest predictors of survival. Therefore, dynamic risk models which capture how risk evolves over the course of the disease may be informative for patients and clinicians. Dynamic prediction is a powerful approach to exploit the most recent information for obtaining more accurate predictions of further prognosis.</p> <p>There are several methods to estimate dynamic risk including simple methods such as conditional survival [3], to more complex methods such as landmark analysis, time to event methods incorporating time-varying and time-dependent effects, competing risk models, multi-state models and joint modelling of longitudinal and time to event data [4,5]. More complex modelling allows both baseline and time-dependent characteristics such as repeatedly measured biomarkers to be incorporated in the risk model. This allows us to assess if risk factors retain their prognostic impact in those who have survived to a given time point or if accumulating information over time is more useful to improve prediction at specific time points.</p> <h5>Studentship</h5> <p>The overall aim of this project is to build and disseminate robust dynamic clinical prediction models based on multi-modal data assets generated as part of national UK academic clinical trials. These trials contain detailed patient level information on clinical, genetic and disease response variables including longitudinal repeatedly measured biomarkers.</p> <p>The project will conduct a scoping review of methods to model dynamic risk and applications to myeloma. Informed by methods from scoping review, clinical predictions models for myeloma will be developed and applied. This will involve assessing if dynamic risk models improve predictive performance from static models, identifying the key landmarks in the treatment pathway where dynamic models are needed, internal and external validation of models and other methodological considerations. Methods to display and communicate personalised risk estimates will be developed.</p> <h5>Supervision</h5> <p>Day-to-day support will be provided by Dr Lesley Smith and Professor David Cairns and of the Leeds Cancer Research UK Clinical Trials Unit, with expertise in the development and implementation of statistical methods in clinical trials and clinical prediction models. Co-supervision to gain clinical context will be provided by Dr Christopher Parrish.</p> <h5>References</h5> <ol> <li>Cook G, Royle KL, Pawlyn C, Hockaday A, Shah V, Kaiser MF, et al. A clinical prediction model for outcome and therapy delivery in transplant-ineligible patients with myeloma (UK Myeloma Research Alliance Risk Profile): a development and validation study. Lancet Haematol 2019;6:e154–66. <a href="https://doi.org/10.1016/S2352-3026(18)30220-5">https://doi.org/10.1016/S2352-3026(18)30220-5</a>.</li> <li>D’Agostino M, Cairns DA, José Lahuerta J, Wester R, Bertsch U, Waage A, et al. Second Revision of the International Staging System (R2-ISS) for Overall Survival in Multiple Myeloma: A European Myeloma Network (EMN) Report Within the HARMONY Project. vol. 40. 2022.</li> <li>Hieke S, Kleber M, König C, Engelhardt M, Schumacher M. Conditional survival: A useful concept to provide information on how prognosis evolves over time. Clinical Cancer Research 2015;21:1530–6. <a href="https://doi.org/10.1158/1078-0432.CCR-14-2154">https://doi.org/10.1158/1078-0432.CCR-14-2154</a>.</li> <li>Schumacher M, Hieke S, Ihorst G, ENgelhardt M. Dynamic prediction: A challenge for biostatisticians, but greatly needed by patients, physicians and the public. Biometric Journal 2020;62:822-35. <a href="https://doi.org/10.1002/bimj.201800248">https://doi.org/10.1002/bimj.201800248</a></li> <li>Van Houwelingen HC. Dynamic prediction by landmarking in event history analysis. Scandinavian Journal of Statistics 2007;34:70–85. <a href="https://doi.org/10.1111/j.1467-9469.2006.00529.x">https://doi.org/10.1111/j.1467-9469.2006.00529.x</a>.</li> </ol>

<p>To apply for this scholarship opportunity applicants should complete an <a href="http://www.leeds.ac.uk/research-applying/doc/applying-research-degrees">online application form</a> and attach the following documentation to support their application.</p> <ul> <li>a full academic CV</li> <li>degree certificate and transcripts of marks</li> <li>Evidence that you meet the University's minimum English language requirements (if applicable)</li> </ul> <p>To help us identify that you are applying for this scholarship project please ensure you provide the following information on your application form;</p> <ul> <li>Select PhD in Medicine as your programme of study</li> <li>Give the full project title and name the supervisors listed in this advert</li> <li>For source of funding please state you are applying for a CRUK CTU Scholarship</li> </ul> <p><strong>Please note there are 4 advertised projects for the 2 available awards. If you are interested in more than one project, please submit only one application and indicate your order of preference for the projects. </strong></p> <p>If English is not your first language, you must provide evidence that you meet the University's minimum English language requirements (below).</p> <p><em>As an international research-intensive university, we welcome students from all walks of life and from across the world. We foster an inclusive environment where all can flourish and prosper, and we are proud of our strong commitment to student education. Across all Faculties we are dedicated to diversifying our community and we welcome the unique contributions that individuals can bring, and particularly encourage applications from, but not limited to Black, Asian, people who belong to a minority ethnic community, people who identify as LGBT+ and people with disabilities. Applicants will always be selected based on merit and ability.</em></p>

Applicants to this scholarship in the School of Medicine should normally have an Undergraduate degree of 2:1 or above (or international equivalent) in a relevant subject area. A Master’s degree is desirable, but not essential.

The minimum English language entry requirement for postgraduate research study in the School of Medicine is an IELTS of 6.5 overall with at least 6.0 in each component (reading, writing, listening and speaking) or equivalent. The test must be dated within two years of the start date of the course in order to be valid.

<p>We are offering 2 full-time PhD scholarships in the Leeds Institute of Clinical Trials Research within the School of Medicine for two UK candidates, covering a maintenance grant matching UKRI maintenance stipend (£19,237 in 2024/25) and UK tuition fees for three years, subject to satisfactory progress. The award will be made for one year in the first instance and renewable for a further period of up to two years, subject to satisfactory academic progress.</p> <p>Other Conditions:</p> <ul> <li>Applicants must not have already been awarded or be currently studying for a doctoral degree</li> <li>Applicants must live within a reasonable distance of the University of Leeds whilst in receipt of this scholarship</li> </ul>

<p>For further information please contact the Faculty of Medicine and Health PGR Admissions team<br /> e: <a href="mailto:fmhpgradmissions@leeds.ac.uk">fmhpgradmissions@leeds.ac.uk</a></p>