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.

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.

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.

Background

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.

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.

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.

Studentship

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.

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.

Supervision

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.

How to apply

To apply for this scholarship opportunity applicants should complete an online application form and attach the following documentation to support their application.

• a full academic CV
• degree certificate and transcripts of marks
• Evidence that you meet the University’s minimum English language requirements (if applicable)

To help us identify that you are applying for this scholarship project please ensure you provide the following information on your application form;

• Select PhD in Medicine as your programme of study
• Give the full project title and name the supervisors listed in this advert
• For source of funding please state you are applying for a CRUK CTU Scholarship

Eligibility

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

If English is not your first language, you must provide evidence that you meet the University’s minimum English language requirements. The minimum English language entry requirement for the Faculty of Medicine & Health is an IELTS of 6.5 overall with at least 6.0 in each component or equivalent. The test must be dated within two years of the start date of the course in order to be valid.

Other Conditions:

• Applicants must not have already been awarded or be currently studying for a doctoral degree
• Awards must be taken up by 1st November 2024
• Applicants must live within a reasonable distance of the University of Leeds whilst in receipt of this scholarship