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Assessing the generalisability of trial findings using large orthopaedic population studies #NDORMS-2020/5


About This PhD Project

Project Description

Research Project Outline


Randomised trials (RCTs) are widely accepted as the preferred approach to evaluating health care interventions. However, the clinical relevance and generalisability of the findings are often disputed. In particular, RCTs are often criticised in that the recruited population is only a subset of those who could potentially be involved, and also that they differ in terms of key characteristics from the wider clinical population (e.g. age, gender as well as disease specific risk and potential treatment effect modifiers factors). Where treatments are already available in clinical practice, it is often argued that those who received the interventions in clinical practice are different; this is particular the case surgical RCTs which tend to be relatively small (1). It is sometimes claimed that those who deliver the interventions (e.g. specific operation) (2) are different in routine practice or that non-compliance (3) make the RCT finding hard to apply. Or alternatively, where there is a new intervention, it is argued that the result would differ if the whole of the clinical populations where to receive the new intervention. There is a need to explore this issue further using surgical trial datasets within relevant population datasets.

Different approach have been used to assess the generalisability of RCT findings. These include more informal approaches to comparison of measures, to recently more complex approaches including weighting (e.g. using inverse probability weights) (4), and other approaches which allow the consistency of the findings to be formally assessed.

The main aim of the project is to assess the use of cohort population data (e.g. registry) to inform the assessment of the generalisability of RCT findings in an orthopaedic setting. Elements of research will include a review of current practice, use of a variety of both informal and more statistical driven, approaches on an example trial nested within an orthopaedic setting, and simulation studies to explore the tolerance of RCT findings with regards to clinical factors, and the sample size required for such evaluations.

Supervisors


The supervisory team will include Jonathan A Cook (https://www.ndorms.ox.ac.uk/team/jonathan-cook), Xavier Griffin (https://www.ndorms.ox.ac.uk/team/xavier-griffin) and Ben Ollivere (https://www.nottingham.ac.uk/medicine/people/benjamin.ollivere).

Associate Professor Jonathan A Cook is a highly experienced clinical trial statistician with a particular interest in the conduct of surgical trials. He has lead out a number of methodological research project related to surgical trial design and the design and analysis of randomised trials.

Associate Professor Xavier Griffin is an experience orthopaedic clinical trialist with an interest in methodology of clinical trials.

Associate Professor Ben Ollivere is an orthopaedic surgeon and clinical trialist and is the chief investigator of the ORiF trial (https://www.ndorms.ox.ac.uk/clinical-trials/current-trials-and-studies/orif), an RCT nested within a large population registry.

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 (https://www.ndorms.ox.ac.uk/csm). 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, the CSM 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 CSM.

Students will have access to various courses run by the CSM, 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


The Department accepts applications throughout the year but it is recommended that, in the first instance, you contact the relevant supervisor(s) or the Graduate Studies Officer, Sam Burnell (), who will be able to advise you of the essential requirements.

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 or MSc by research will commence in October 2020.

Applications should be made to one of the following programmes using the specified course code:
D.Phil in Musculoskeletal Sciences (course code: RD_ML2)

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

References

1. Copsey B, Thompson J, Vadher K, Ali U, Dutton S, Fitzpatrick R, Lamb SE, Cook JA. Sample size calculations are poorly conducted and reported in many randomised trials of hip and knee osteoarthritis: results of a systematic review. Journal of Clinical Epidemiology 2018 https://doi.org/10.1016/j.jclinepi.2018.08.013
2. Cook JA, Elders A, Boachie C, Bassinga T, Fraser C, Altman DG, Boutron I, Ramsay CR, MacLennan GS. A systematic review of the use of an expertise-based randomised controlled trial design. Trials 2015, 16:241. https://trialsjournal.biomedcentral.com/articles/10.1186/s13063-015-0739-5
3. Cook JA, Maclennan GS, Palmer T, Lois N, Emsley R. Instrumental variable methods for a binary outcome were used to informatively address non-compliance in a randomised trial in surgery. Journal of Clinical Epidemiology. 2018, 96: 126-132. pii: S0895-4356(17)30564-4.
https://www.doi.org/10.1016/j.jclinepi.2017.11.011.
4. Harman E. GR, Ramshai R, Sekhon JS. From sample average treatment effect to population average treatment effect on the treated: combining experimental with observational studies to estimate the population treatment effects. J RSS Series A 2015;178(3):757-78.

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