Advancing joint modelling methodology for clinical prediction

This is an interdisciplinary project between the Department of Mathematics (Christiana Charalambous, Timothy Waite) and the Centre for Health Informatics (David Jenkins). Clinical prediction models (CPMs) are algorithms that use information about a patient at a given time point to generate risk estimates for an outcome. These models are widely adopted throughout healthcare and can be used to inform clinical decisions, for example, if an individual should receive an intervention. Traditionally, CPMs have used data from a single time point and often consider a single outcome. However, the adoption of electronic health records and the increase in availability of data provides rich longitudinal (e.g. repeatedly measured biomarkers) and time-to-event (e.g. death or disease progression) data, which are often underutilised. Complex models that use multi-outcome (potentially correlated) data, such as joint models, are beginning to be adopted and evidence from the literature suggests this could improve predictive accuracy and in turn patient outcomes. Although a promising approach, there are strong assumptions required and there is lack of methodological development for healthcare usage. In addition to this, it is unclear when repeated longitudinal measurements should be recorded. Thus, there is a need to advance the joint modelling methodology for clinical prediction and investigate approaches to validate these models as well as determine when to monitor (take the next measurements) for a patient.

Objectives and outcomes of the project:

1. Review the existing literature on joint modelling for clinical prediction.

2. Undertake methodological development for the formulation and validation of joint models, testing the developed methods throughout a range of scenarios in simulated and real-world health data.

• Extend time-dependent AUC methodology for joint model validation.

• Perform simulation studies to compare the proposed method to existing approaches for prediction and model validation.

• Extend current minimum sample size methodology for joint models.

3. Assess the impact of different study design approaches to investigate the optimal way to record clinical measurements and monitor patients (through simulation, application to real-world healthcare datasets and methods development).

• Undertake simulation studies to investigate the impact of longitudinal measurement frequency on joint model performance.

• Investigate techniques for Bayesian design of experiments in joint models.

4. Develop guidance for the development, validation and use of joint models in clinical practice.

The combination of simulation and real-world data will allow us to evaluate the methods under a range of scenarios and parameter combinations and assess the real-world impact of the methods. The collaboration with the Centre for Health Informatics will provide access to real-world health data, such as the Greater Manchester care record, UK Biobank and cardiovascular data from the Manchester University NHS Foundation Trust and Wythenshawe Hospital, that the centre regularly utilises. The project will also provide recommendations to determining when to monitor patients and guidance for developing and validating joint models for clinical prediction.

Eligibility

Applicant’s should have:

• Obtained or working towards a 1st class degree in Mathematics (BSc/MMath) or Distinction level Masters in (Bio)Statistics, Data Science or similar.

• Research experience, e.g. UG/MSc project, research internship or other.

• Background in some or all of the following: longitudinal data analysis, survival analysis, design of experiments, Bayesian statistics.

• Good programming skills in a language such as R or Python

• Good communication skills (oral and written)

• Openness to working across disciplines

Funding

At Manchester we offer a range of scholarships, studentships and awards at university, faculty and department level, to support both UK and overseas postgraduate researchers applying for competition and self-funded projects.

For more information, visit our funding page or search our funding database for specific scholarships, studentships and awards you may be eligible for.

Before you apply

We strongly recommend that you contact the supervisor for this project before you apply.

How to apply

Apply online through our website: https://uom.link/pgr-apply-fap

When applying, you’ll need to specify the full name of this project, the name of your supervisor, if you already having funding or if you wish to be considered for available funding through the university, details of your previous study, and names and contact details of two referees.

Your application will not be processed without all of the required documents submitted at the time of application, and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered.

After you have applied you will be asked to upload the following supporting documents:

• Final Transcript and certificates of all awarded university level qualifications
• Interim Transcript of any university level qualifications in progress
• CV
• Supporting statement: A one or two page statement outlining your motivation to pursue postgraduate research and why you want to undertake postgraduate research at Manchester, any relevant research or work experience, the key findings of your previous research experience, and techniques and skills you’ve developed. (This is mandatory for all applicants and the application will be put on hold without it).
• Contact details for two referees (please make sure that the contact email you provide is an official university/work email address as we may need to verify the reference)
• English Language certificate (if applicable)

If you have any questions about making an application, please contact our admissions team by emailing [email protected].

Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact.

We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.

We also support applications from those returning from a career break or other roles. We consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder).