PROMIS Scale Validation in Clinical Trials of Common Musculoskeletal Injuries

Patient-reported outcomes are included in randomised clinical trials (RCTs) as primary or secondary endpoints to measure the impact of interventions and are increasingly recognised by regulators, clinicians, and patients as valuable tools that ensure comprehensive assessment. The Patient-Reported Outcomes Measurement Information System (PROMIS) was initiated in 2004 and funded by the United States’ National Institute of Health. The objective of the PROMIS initiative was to enable efficient and interpretable clinical research and practice by building and validating a common, accessible item bank to measure key symptoms and health domains applicable to a range of chronic conditions using modern psychometric tools [1, 2].

In 2016 the American Academy of Orthopaedic Surgeons endorsed the use of the PROMIS Global Health Instrument to measure the general quality of life of orthopaedic patients. Despite the attention, PROMIS’s role as a new standard in orthopaedic trauma remains uncertain according to a recent systematic review. The report highlights limited use of PROMIS and concludes that barriers in adoption of PROMIS as a standard include: limited comparisons with other legacy measures and lack of crosswalk tools to measures such as SF-36 and other Health-Related Quality of Life measures commonly used in health economics such as EQ5D [3].

Psychometricians, also highlight the need for ongoing instrument validation, even for established tools, since validity does not reside in the instruments themselves but characterised the inferences derived from the data generated by their use, in a given context. Despite these recommendations most applied research includes cursory or no psychometric evaluation of the tools used.  This project is about understanding the performance of PROMIS measures of physical functioning in the context of clinical trials of surgical interventions for common musculoskeletal injuries [4, 5].

The main aim of the project is to assess the psychometric properties of the PROMIS Upper extremity scale using data arising from Computerised Adaptive testing used in RCTs undertaken by the Oxford Trauma and Emergency Care research group of the Nuffield Department of Rheumatology, Orthopaedics and Musculoskeletal Sciences. Elements of this research project will include a review of validation studies evaluating the scale, modelling to evaluate core assumptions in adult and paediatric populations, and exploring the variation across studies of the standard error in measurement and treatment responsiveness to assess how these can impact sample size calculations. Linking treatment effects on the physical functioning scale with health economic measures will be undertaken to assess relative responsiveness to treatment. Instruments more responsive to treatment reduce sample size requirements for future trials. Meta-analytic approaches to linking treatment effects across different scales can also be used to support health economic analyses and evaluation of interventions across studies using different outcome scales [6-9].

KEYWORDS: Patient-Reported Outcomes Measurement Information System; Computerised Adaptive Testing; Clinical Trials; musculoskeletal injuries; relative responsiveness to treatment.

1.         Hanmer, J., et al., A reporting checklist for HealthMeasures’ patient-reported outcomes: ASCQ-Me, Neuro-QoL, NIH Toolbox, and PROMIS. Journal of Patient-Reported Outcomes, 2020. 4(1): p. 21. https://doi.org/10.1186/s41687-020-0176-4

2.         Reeve, B.B., et al., Psychometric Evaluation and Calibration of Health-Related Quality of Life Item Banks: Plans for the Patient-Reported Outcomes Measurement Information System (PROMIS). Medical Care, 2007. 45(5). 10.1097/01.mlr.0000250483.85507.04

3.         O'Hara, N.N., et al., Is PROMIS the new standard for patient-reported outcomes measures in orthopaedic trauma research? Injury, 2020. 51: p. S43-S50. 10.1016/j.injury.2019.10.076

4.         DeVellis, R.F. and C.T. Thorpe, Scale development: Theory and applications. 2021: Sage publications. 10.1111/peps.12499

5.         Streiner, D.L., G.R. Norman, and J. Cairney, Health Measurement Scales: A practical guide to their development and use. 2014: Oxford University Press. https://doi.org/10.1093/med/9780199685219.001.0001

6.         Ades, A.E., et al., Simultaneous synthesis of treatment effects and mapping to a common scale: an alternative to standardisation. Research Synthesis Methods, 2015. 6(1): p. 96-107. 10.1002/jrsm.1130

7.         Kounali, D., et al., How much change is enough? Evidence from a longitudinal study on depression in UK primary care. Psychological Medicine, 2022. 52(10): p. 1875-1882. 10.1017/S0033291720003700

8.         Kounali, D.Z., et al., The relative responsiveness of test instruments can be estimated using a meta-analytic approach: an illustration with treatments for depression. J Clin Epidemiol, 2016. 77: p. 68-77. 10.1016/j.jclinepi.2016.03.005

9.         Lu, G., D. Kounali, and A.E. Ades, Simultaneous multioutcome synthesis and mapping of treatment effects to a common scale. Value Health, 2014. 17(2): p. 280-7. 10.1016/j.jval.2013.12.006

SUPERVISORS:

The supervisory team will include  Daphne Kounali, Jonathan A Cook, and Matthew Costa. All three have substantial supervisory experience of post-graduate students.

Professor Jonathan A Cook (University of Oxford) is a highly experienced clinical trial statistician with a particular interest in the conduct of surgical trials. He has led out several methodological research projects related to surgical trial design and 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 all successfully completed their masters and DPhils respectively.

Professor of orthopaedic trauma surgery Matthew Costa (University of Oxford) is Chief Investigator for a series of randomised trials and associated studies supported by grants from the UK NIHR with an interest in the clinical and cost effectiveness of musculoskeletal interventions. 

Dr Daphne Kounali (University of Oxford) is an experienced clinical trial statistician with a particular interest in standardization methodology to support the production of evidence in real-world medical practice and has developed new methods in comparative effectiveness and mapping between patient-reported and clinical outcomes which allow for simultaneous synthesis and mapping of treatment effects across different measurement scales. She is currently supervising one student undertaking their MSc in Statistical Science at Oxford University in addition to four former students who successfully completed their Masters and DPhil in epidemiology and statistics in other UK universities. 

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 (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). 

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 one of the following programmes, using the specified course code.

D.Phil in Musculoskeletal Sciences (course code: RD_ML2)

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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