Cancer Data Driven Detection: Understanding and handling missing data due to differential tendencies of clinicians to record symptoms for the development of cancer risk prediction models CRUK funded PhD Ref: 5814

Supervisors

Professor Gary Abel, Department of Health and Community Sciences, University of Exeter

Professor Richard Neal, Department of Health and Community Sciences, University of Exeter

Professor Angela Wood (Cambridge), Department of Health and Data Science, University of Cambridge

Professor Matthew Sperrin (Manchester), Department of Biostatistics and Health Data Science, University of Manchester

The University of Exeter’s Department of Health and Community Sciences is inviting applications for a PhD studentship funded by the CD3 programme to commence on 1/10/26 or as soon as possible thereafter.  For eligible students the studentship will cover Home tuition fees plus an annual tax-free stipend of at least £22,113 for 4 years full-time.  The student would be based in the Faculty of Health and Life Sciences at the St Luke’s Campus in Exeter.

CD3 is a new, multidisciplinary and multi-institutional strategic national research programme dedicated to using data to transform our understanding of cancer risk and enable early interception of cancers. It represents a major, multi-million-pound flagship investment funded through a strategic programme award by Cancer Research UK, the National Institute for Health and Care Research (NIHR), Engineering and Physical Sciences Research Council (EPSRC), and the Peter Sowerby Foundation; in partnership with Health Data Research UK (HDR UK) and the Economic and Social Research Council’s Administrative Data Research UK programme (ADR UK). This studentship is one of a number attached to this programme and one of three linked projects addressing issues related to missing data. 

Early cancer diagnosis is often challenging for patients presenting with vague, non-specific symptoms that may be linked to multiple cancer sites. This project aims to improve diagnostic decision-making in such patients by understanding how cancer risk prediction models are influenced by missing and incomplete symptom data recorded in electronic health records (EHRs) and developing methods to address any issues. Unlike standard missing data problems (e.g., missing height or lab results), researchers often do not know when information on symptoms is missing. The usual approach is to assume that if there is no code for symptoms recorded in the dataset then the symptoms were not present. However, we know that some clinicians are more likely than others to record symptoms in coded form.

Using large-scale linked electronic health record data, mixed-effects models will be employed to quantify the extent of variation between general practices and individual clinicians in recording symptoms using symptom codes. Temporal analyses will assess how these patterns change over time. Building on these findings, the project will quantify how different patterns of missingness may influence estimates of the risk that a patient has underlying cancer in the first instance, and subsequently, how they may impact risk prediction model performance and calibration. Novel methods will be developed to incorporate incomplete or uncertain information, including delta-adjustment imputation and other approaches that explicitly model symptom recording probabilities.

Applicants should be able to demonstrate excellent analytical and programming skills (for example in Stata, R or Python), experience working with data, and an enthusiasm for interdisciplinary research that bridges data science, healthcare, and population health.

The student will have the opportunity to attend the structured Early Detection Training Programme (run in partnership with the Alliance for Cancer Early Detection (ACED)), providing PhD students with a comprehensive foundation to cancer early detection.

The studentship will be awarded on the basis of merit. Students who pay international tuition fees are not eligible to apply. The conditions for eligibility of home fees status are complex and you will need to seek advice if you have moved to or from the UK (or Republic of Ireland) within the past 3 years or have applied for settled status under the EU Settlement Scheme.

Entry requirements

Applicants for this studentship must have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK, in an appropriate area subject such as statistics, mathematics, computer science, engineering, or a related biomedical or population health discipline, and may also have a Master’s degree in a quantitative or health data field.

If English is not your first language you will need to meet the English language requirements and provide proof of proficiency. Click here for more information.

How to apply

Apply now

To apply, please click the ‘Apply Now’ button above. In the application process you will be asked to upload several documents

•            CV

•            Letter of application (outlining your academic interests, prior research experience and reasons for wishing to undertake the project).

•            Transcript(s) giving full details of subjects studied and grades/marks obtained (this should be an interim transcript if you are still studying)

•            Names of two referees familiar with your academic work. You are not required to obtain references yourself. We will request references directly from your referees if you are shortlisted.

•            If you are not a national of a majority English-speaking country you will need to submit evidence of your proficiency in English.

The closing date for applications is midnight on 3rd April 2026.  Interviews will be held virtually.

All application documents must be submitted in English. Certified translated copies of academic qualifications must also be provided.

Please quote reference 5814 on your application and in any correspondence about this studentship.

Summary