Psychometric sensitivity analyses for clinical trials and observational studies that use patient-reported outcome measures

Background

Patient-reported outcome measures (PROMs) are questionnaires used to assess various aspects of a person's health. They have become a common tool for measuring outcomes in randomised controlled trials (RCTs) and observational studies. When interpreting PROM scores, researchers often rely on assumptions about the measurement properties of the PROM. These assumptions might include:

• Content validity (the PROM contains relevant and comprehensive questions)
• Unidimensionality (the PROM does not combine questions about unrelated health factors)
• Measurement invariance (different population subgroups interpret and respond to the questions in similar ways)
• Interval scaling (the scores obtained from the PROM are continuous)
• Negligible measurement error at the individual level
• A consistent relationship between score and the measured construct each time the PROM is administered
• A target difference between groups that is truly meaningful

However, not all PROMs are developed to the same standards, and many have not been validated using modern psychometric techniques that assess these properties. Simulation studies have suggested that violations of these measurement assumptions can introduce bias. Nevertheless, the extent to which this affects the results or conclusions of a trial remains unclear.

Aim

To explore the potential role of psychometric sensitivity analyses that test the stability of a study’s results to violations of these measurement assumptions.

Project outline

1)     The candidate will review the measurement properties of PROMs that have been used as primary outcome measures in RCTs funded by the National Institute for Health Research Health Technology Appraisal programme in the last five years. This will follow methodology outlined by the consensus-based standards for the selection of health measurement instruments (COSMIN) and the difference elicitation in trials (DELTA2) recommendations.

2)     The candidate will evaluate PROM measurement properties, such as unidimensionality, measurement invariance, interval scaling, response shift, and measurement imprecision in existing trial datasets. They will explore the impact of any violations of these assumptions on trial results through sensitivity analyses.

3)     The candidate will engage relevant stakeholders to co-produce guidance on the assessment of measurement assumptions in RCTs and observational studies that use PROMs.

Relevant publications

Prinsen CAC, Mokkink LB, Bouter LM, et al. COSMIN guideline for systematic reviews of patient-reported outcome measures. Qual Life Res. 2018;27(5):1147-1157. doi:10.1007/s11136-018-1798-3 

Cook JA, Julious SA, Sones W, et al. DELTA2 guidance on choosing the target difference and undertaking and reporting the sample size calculation for a randomised controlled trial. BMJ. Published online November 5, 2018:k3750. doi:10.1136/bmj.k3750

Harrison CJ, Plessen CY, Liegl G, et al. Item response theory may account for unequal item weighting and individual-level measurement error in trials that use PROMs: a psychometric sensitivity analysis of the TOPKAT trial. Journal of Clinical Epidemiology. 2023;158:62-69. doi:10.1016/j.jclinepi.2023.03.013

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. Training will be provided in techniques including patient and public involvement and engagement, clinical trial design, systematic review, COSMIN methodology, R programming, linear modelling, and advanced psychometrics including factor analysis, structural equation modelling, item response theory, and plausible value imputation.

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 Surgical Intervention Trials Unit 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 Psychometrics and Health Outcomes team at Charité Universitätsmedizin, Berlin, as part of the Oxford-Berlin Partnership for Enhancing Measurement in Clinical Trials.

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 

It is recommended that, in the first instance, you contact the relevant supervisor(s) and the Graduate Studies Office (graduate.studies@ndorms.ox.ac.uk), 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 D.Phil will commence in October 2024.

Applications should be made to one of the following programmes 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.