Mapping to Quality of Life and Capability Measures in CataractSurgery Patients: From Cat-PROM5 to EQ-5D-3L EQ-5D-5L and ICECAP-O UsingMixture Modelling

摘要

Cataract is a prevalent and potentially blinding eyecondition. Cataract surgery, the only proven treatment for this condition, is avery frequently undertaken procedure. The objective of this analysis was todevelop a mapping algorithm that could be used to predict quality of life andcapability scores from the Cat-PROM5, a newly developed, validatedpatient-reported outcome measure for patients undergoing cataract surgery. We estimated linear models and adjusted limiteddependent variable mixture models. Data were taken from the Predict-CAT cohortof up to 1181 patients undergoing cataract surgery at two sites in England. TheCat-PROM5 was mapped to two quality of life measures (EQ-5D-3L and EQ-5D-5L) andone capability measure (ICECAP-O). All patients reported ICECAP-O and one orother of the EQ-5D measures both before and after cataract surgery. Modelperformance was assessed using likelihood statistics, graphical inspections ofmodel fit, and error measurements. Adjusted limiteddependent variable mixture models dominated linear models on all performancecriteria. Mixture models offered very good fit. Three component models thatallowed component membership to be a function of covariates (age, sex, anddiabetic status depending on specification and outcome measure) and whichconditioned on covariates offered the best performance in almost all cases. Anexception was the EQ-5D-5L post-surgery for which a two-component model wasselected. Mapping from Cat-PROM5 to quality of lifeand capability measures using adjusted limited dependent variable mixture modelsis feasible, and the estimates can be used to support cost-effectivenessanalysis in relation to cataract care. Mixture models performed strongly forboth quality of life outcomes and capability outcomes.