Optimizing Computer Adaptive Test Performance: A Hybrid Simulation Study to Customize the Administration Rules of the CAT-EyeQ in Macular Edema Patients

Purpose: In previous research the EyeQ item bank, which measures vision-related quality of life (Vr-QoL), was calibrated for future use as a computer adaptive test (CAT). The aim of the current study was to define optimal administration rules. Methods: CAT simulations were performed using real responses. Patients (N = 704; mean age, 76.2 years), having macular edema completed the EyeQ. Four CAT simulations were performed, which were set with different administration rules regarding length, accuracy level and the association with best health, which means the test was aborted after the first 4 responses of having no complaints. Results: The CATDefault showed a mean test length of 6.9 and 15.1% unreliable estima-tions. Extending the test length to 15 items (CATAlt1) resulted in a mean test length of 7.3 and slightly decreased the percentage unreliable estimations (11.5%). Under CATAlt2, the percentage unreliable estimations was 15.1% and the mean test length was 9.7. Percentages of floor/ceiling effects for CATDefault, CATAlt1, and CATAlt2 were 3.1, 3.0, and 3.1, respectively. CATBestHealth reduced the mean test length to 5.9 and showed 18.2% unreliably estimated patients, of which 14.2% had floor/ceiling scores. Conclusions: This study shows that the CATBestHealth provided reliably estimated ability scores, with a negligible increase in the number of unreliably estimated patients and ensures that patients having little or no vision-related quality of life problems are minimally burdened with completing items. Translational Relevance: The computer adaptive test EyeQ, set with optimal administration rules, can now be used for the computer adaptive assessment of vision-related quality of life in patients suffering from exudative retinal diseases in ophthalmic clinical practice