Table 1 Glossary of key terms.
Concept, process or tool | Definition |
|---|---|
Patient reported outcome measure (PROM) | PROMs are sets of questions or ‘items’ which form an ‘instrument’ used to quantify the subjective impacts of disease or its treatment. They can be broadly split into generic, or disease-specific measures. Generic measures usefully support comparison of the health status of different disease groups, whilst disease-specific PROMs, including instruments focused on signs or symptoms, offer more sensitive measurement of change in health status for that disease. |
Quality of life (QoL) | Health-related QoL is a multidimensional construct, including all domains in which a patient can be affected by a disease or its treatments. These typically include symptoms, daily activities, mental, social, emotional, convenience and economic impacts. |
Classic test theory | Classic test theory (CTT) is a quantitative approach to test the reliability and validity of a scale. It considers the relationship between the expected score (or ‘true’ score) and observed score on any given measurement. The true score is one assumed to be that which would be obtained if there were no errors in measurement. It assumes that random errors (i.e. the difference between a true score and a set of observed scores on the same individual) are normally distributed (without measuring/testing this) and summary item responses are coded so that higher responses reflect more of the concept. |
Rasch model | The Rasch Model measures latent traits (like difficulty with daily vision-related tasks) and provides an internally valid measure by allowing non-linear raw data to be converted to a linear scale, which then can be evaluated through the use of parametric statistical tests. It assumes that the probability of a given person/item interaction is governed by the difficulty of the item and the ability of the person, that are determined by the item locations on the presumed latent variable along with the rating scale structure. |
Principal Component Analysis (PCA) | Principal Component Analysis (PCA) is a dimension-reducing tool that replaces the variables in a data set by a smaller number of derived variables. |
