Methods for shortening patient-reported outcome measures.
Daphna HarelMurray BaronPublished in: Statistical methods in medical research (2018)
Patient-reported outcome measures are widely used to assess patient experiences, well-being, and treatment response in clinical trials and cohort-based observational studies. However, patients may be asked to respond to many different measures in order to provide researchers and clinicians with a wide array of information regarding their experiences. Collecting such long and cumbersome patient-reported outcome measures may burden patients, increase research costs, and potentially reduce the quality of the data collected. Nonetheless, little research has been conducted on replicable, and reproducible methods to shorten these instruments that result in shortened forms of minimal length. This manuscript proposes the use of mixed integer programming through Optimal Test Assembly as a method to shorten patient-reported outcome measures. This method is compared to the existing standard in the field, which is selecting items based on having high discrimination parameters from an item response theory model. The method is then illustrated in an application to a fatigue scale for patients with Systemic Sclerosis.
Keyphrases
- patient reported outcomes
- patient reported
- systemic sclerosis
- end stage renal disease
- clinical trial
- newly diagnosed
- ejection fraction
- chronic kidney disease
- mental health
- prognostic factors
- interstitial lung disease
- randomized controlled trial
- palliative care
- high resolution
- physical activity
- high throughput
- electronic health record
- depressive symptoms
- sleep quality
- single cell
- artificial intelligence
- study protocol
- data analysis