Dynamic prediction statistical models to characterise localised prostate cancer prognosis

Prostate cancer is the most common cancer in men in the UK, with 46,690 new cases in 2014 (CRUK 2017). Initial management of localised disease includes external beam radiation therapy (EBRT) or radical prostatectomy. The majority of patients in the UK receive EBRT. Following radiation, disease is monitored for recurrence using the blood test prostate specific antigen (PSA), which is repeatedly measured over time. Rises in PSA suggest the disease is re-growing, and trigger further investigations. To more accurately guide clinical decision making, monitoring of PSA after EBRT would be aided by dynamic prognostic tools that incorporate the complete post-treatment PSA evolution, amidst other disease-related information.

Prospective long follow-up and high quality data in clinical trials provide a perfect opportunity to explore different patterns of prognosis of disease. Measurements for different outcomes are collected for each patient, and each outcome usually analysed separately. However, joint statistical modelling of different outcomes and available clinical and pathological markers collected alongside follow-up would allow more accurate predictions of the prognosis of a patient. The long follow-up permits a better understanding of how patients transition through different, progressive stages of their disease.

Prognostic models will be developed based on data from 2 large practice-changing clinical trials investigating radiotherapy interventions in localised prostate cancer. The MRC RT01 trial (Dearnaley et al., 2014) showed that escalated-dose conformal radiotherapy with short-course neoadjuvant androgen deprivation therapy (ADT) improved biochemical progression-free survival. The CHHiP trial (Dearnaley et al., 2016) showed that hypofractionated radiotherapy using 60 Gy in 20 fractions is non-inferior to conventional fractionation using 74 Gy in 37 fractions in patients receiving short-course ADT and has been recommended as standard of care for EBRT in this patient population. Access to the trials data will be accessed with the permission of the respective Trial Management Groups.

The overarching aim of the project is to develop dynamic prediction statistical models for disease prognosis in localised prostate cancer patients following EBRT and neoadjuvant ADT, to obtain personalised predictions enriched by integrating both pre-EBRT information (time-fixed or baseline) and time-dependent information collected during follow-up.

This will be achieved through the following specific aims:
• To describe prostate cancer prognosis dynamics as observed in 2 practice-changing clinical trials in localised prostate cancer by identification of time-dependent events and longitudinal processes of interest.
• Development and validation of dynamic prediction models for the prognosis of prostate cancer incorporating time-dependent information.
• Development of a user-friendly web-based risk calculator implementing the models to obtain individual predictions of prognosis that would help clinical management of future patients.

You will gain experience in clinical trials and prostate cancer disease. You will develop an understanding on and critically leverage complex statistical models that best fit our clinical problem. You will gain experience on implementing complex statistical analyse sin R and Stata.

Candidate profile
Candidates must have a first class or upper second class honours BSc Honours/MSc or equivalent in Mathematical Sciences including a statistical component or Statistics

How to apply
Full details about these studentship projects, and the online application form, are available on our website, at: www.icr.ac.uk/phds Applications for all projects should be made online. Please ensure that you read and follow the application instructions very carefully.

Closing date: Monday 20th November 2017
Applicants should be available for interview 29h and 30th January 2018.

Please apply via the ICR vacancies web portal https://studentapps.icr.ac.uk/

Download a PDF of the complete project proposal:
https://d1ijoxngr27nfi.cloudfront.net/default-document-library/hall-icr-studentship-proposal---prof-ehall-20170831-web.pdf