Table 1 Glossary of key terms

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 health, vision-specific, or disease-specific measures. Generic health measures usefully support comparison of the health status of different disease groups, whilst vision and disease-specific PROMs, including instruments focused on signs or symptoms, offer more sensitive measurement of change in health status

Quality of life (QoL)

QoL is a multidimensional construct, with domains potentially including symptoms relating to vision function, eye disease, or other aspects of health and organ function, impacts of disease and treatment on aspects of daily functioning, including daily activities, mental, social, emotional and economic functioning

Classic test theory

Classic test theory (CTT), also known as true score theory, is a quantitative approach to test the reliability and validity of a scale based on items. It considers the relationship between the expected score (or ‘true’ score) and observed score on any given measurement. The true score is one that 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 and item responses are coded so that higher responses reflect more of the concept

Item response theory (IRT)

Item Response Theory (IRT) refers to psychometric statistical model that attempts to map data observed on participants to latent traits assumed to be causing the observations, in order to explain as much of the observed variance as possible. IRT assumes that the latent construct and items of a measure are organised in an unobservable continuum and its main purpose focuses on establishing the individual’s position on that continuum. As in CTT, IRT requires each item be distinct from the others, yet similar and consistent with them in reflecting all important respects of the underlying construct

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