Flexible parametric survival models in Randomised controlled trials practice

RESEARCH PROJECT OUTLINE:

Overall survival, disease-free survival, progression-free survival and cause-specific survival are typical primary or secondary time-to-event outcomes in randomized controlled trials (RCTs) collecting survival data. They are outcomes defined as the time (usually from randomization) until the occurrence of the event of interest.

The Cox proportional hazards model is the most common regression model used in RCTs to model survival data and compare outcomes across different treatments. No parametric assumptions are needed about the shape of the baseline hazard function which is often ignored. Flexible parametric models for survival data have been introduced by Royston and Parman (2002) and they use splines to explicitly model the baseline hazard. Flexible parametric models also allow reporting of effect measures directly calculated from absolute risks such as survival differences or restricted mean survival differences (see also Royston and Parman 2013 and Rutherford, Crowther and Lambert, 2013).

This DPhil project will investigate the use of flexible parametric survival models for the typical primary and secondary outcomes used in RCTs. Their routine use will be assessed with respect to key aspects of RCTs designs: the sample sizes required, the number of events of the outcome of interest, the complexity of the hazard function, the time dependent effects and the presence of competing events.

KEYWORDS:

Complex Hazard Functions, Flexible Survival Models, Randomised Controlled Trials, Survival Analysis Modelling, Time to Event Outcome

SUPERVISORS:

The supervisory team will include Dr Sofia Massa and Professor Jonathan A Cook. They have substantial supervisory experience of post-graduate students.

Dr Sofia Massa is a Lead Statistician interested in methodological aspects impacting the design and analysis of randomised trials. Currently she supervises 3 DPhil students and 1 MSc student.

Professor Jonathan A Cook is a highly experienced clinical trial statistician that has lead out a number of methodological research project related to the design and analysis of randomised trials. Currently, he supervises 2 masters and 4 DPhil level students in addition to 6 and 5 former students who successfully completed their masters and DPhils respectively.

TRAINING:

The Botnar Research Centre plays host to the University of Oxford's Institute of Musculoskeletal Sciences, which enables and encourages research and education into the causes of musculoskeletal disease and their treatment. The proposed project will be part of the statistical methodology work in the Centre for Statistics in Medicine. Training will be provided in techniques including systematic reviewing, critiquing clinical trial methodology, and evaluating the value of statistical methods in practice. 

A core curriculum of lectures will be taken in the first term to provide a solid foundation in a broad range of subjects including musculoskeletal biology, inflammation, epigenetics, translational immunology, data analysis and the microbiome.  Students will also be required to attend regular seminars within the Department and those relevant in the wider University.

Students will be expected to present data regularly in Departmental seminars, in the Centre for Statistics and Medicine and to attend external conferences to present their research globally, with limited financial support from the Department. Students will also have the opportunity to work closely with the Centre for Statistics in Medicine.

Students will have access to various courses run by the Medical Sciences Division Skills Training Team and other Departments. All students are required to attend a 2-day Statistical and Experimental Design course at NDORMS and run by the IT department (information will be provided once accepted to the programme).

HOW TO APPLY:

Please contact the relevant supervisor(s), to register your interest in the project, and the Departmental Education Team (graduate.studies@ndorms.ox.ac.uk), who will be able to advise you of the essential requirements for the programme and provide further information on how to make an official application.

Interested applicants should have, or expect to obtain, a first or upper second-class BSc degree or equivalent in a relevant subject and will also need to provide evidence of English language competence (where applicable). The application guide and form is found online and the DPhil will commence in October 2025.

Applications should be made to the following programme, using the specified course code.

D.Phil in Clinical Epidemiology and Medical Statistics (course code: RD_NNRA1)

For further information, please visit http://www.ox.ac.uk/admissions/graduate/applying-to-oxford.

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