Eva K Fenwick1,2,3, Jyoti Khadka4, Konrad Pesudovs4, Gwyn Rees1, Tien Y Wong2,3, Ecosse L Lamoureux1,2,3. 1. Centre for Eye Research Australia, The Royal Victorian Eye and Ear Hospital, University of Melbourne, Melbourne, Australia. 2. Singapore Eye Research Institute, Singapore National Eye Centre, Singapore. 3. Duke-NUS, Singapore, National University of Singapore, Singapore. 4. Discipline of Optometry and Vision Science, Flinders University of South Australia, South Australia, Australia.
Abstract
Purpose: The purpose of this study was to assess the psychometric properties of diabetic retinopathy (DR) and diabetic macular edema (DME) quality-of-life (QoL) item banks and determine the utility of the final calibrated item banks by simulating a computerized adaptive testing (CAT) application. Methods: In this clinical, cross-sectional study, 514 participants with DR/DME (mean age ± SD, 60.4 ± 12.6 years; 64% male) answered 314 items grouped under nine QoL item pools: Visual Symptoms (SY); Ocular Comfort Symptoms (OS); Activity Limitation (AL); Mobility (MB); Emotional (EM); Health Concerns (HC); Social (SC); Convenience (CV); and Economic (EC). The psychometric properties of the item pools were assessed using Rasch analysis, and CAT simulations determined the average number of items administered at high and moderate precision levels. Results: The SY, MB, EM, and HC item pools required minor amendments, mainly involving removal of six poorly worded, highly misfitting items. AL and CV required substantial modification to resolve multidimensionality, which resulted in two new item banks: Driving (DV) and Lighting (LT). Due to unresolvable psychometric issues, the OS, SC, and EC item pools were not pursued further. This iterative process resulted in eight operational item banks that underwent CAT simulations. Correlations between CAT and the full item banks were high (range, 0.88-0.99). On average, only 3.6 and 7.2 items were required to gain measurement at moderate and high precision, respectively. Conclusions: Our eight psychometrically robust and efficient DR/DME item banks will enable researchers and clinicians to accurately assess the impact and effectiveness of treatment therapies for DR/DME in all areas of QoL.
Purpose: The purpose of this study was to assess the psychometric properties of diabetic retinopathy (DR) and diabetic macular edema (DME) quality-of-life (QoL) item banks and determine the utility of the final calibrated item banks by simulating a computerized adaptive testing (CAT) application. Methods: In this clinical, cross-sectional study, 514 participants with DR/DME (mean age ± SD, 60.4 ± 12.6 years; 64% male) answered 314 items grouped under nine QoL item pools: Visual Symptoms (SY); Ocular Comfort Symptoms (OS); Activity Limitation (AL); Mobility (MB); Emotional (EM); Health Concerns (HC); Social (SC); Convenience (CV); and Economic (EC). The psychometric properties of the item pools were assessed using Rasch analysis, and CAT simulations determined the average number of items administered at high and moderate precision levels. Results: The SY, MB, EM, and HC item pools required minor amendments, mainly involving removal of six poorly worded, highly misfitting items. AL and CV required substantial modification to resolve multidimensionality, which resulted in two new item banks: Driving (DV) and Lighting (LT). Due to unresolvable psychometric issues, the OS, SC, and EC item pools were not pursued further. This iterative process resulted in eight operational item banks that underwent CAT simulations. Correlations between CAT and the full item banks were high (range, 0.88-0.99). On average, only 3.6 and 7.2 items were required to gain measurement at moderate and high precision, respectively. Conclusions: Our eight psychometrically robust and efficient DR/DME item banks will enable researchers and clinicians to accurately assess the impact and effectiveness of treatment therapies for DR/DME in all areas of QoL.
Authors: Preeti Gupta; Eva K Fenwick; Ryan E K Man; Alfred T L Gan; Charumathi Sabanayagam; Debra Quek; Chaoxu Qian; Chui Ming Gemmy Cheung; Ching-Yu Cheng; Ecosse L Lamoureux Journal: Sci Rep Date: 2022-05-19 Impact factor: 4.996
Authors: Ryan Eyn Kidd Man; Eva K Fenwick; Jyoti Khadka; ZhiChao Wu; Simon Skalicky; Konrad Pesudovs; Ecosse L Lamoureux Journal: Transl Vis Sci Technol Date: 2022-06-01 Impact factor: 3.048
Authors: Yesha S Shah; Michael Cheng; Aleksandra Mihailovic; Eva Fenwick; Ecosse Lamoureux; Pradeep Y Ramulu Journal: Ophthalmology Date: 2021-10-28 Impact factor: 14.277
Authors: Krystal Khoo; Ryan E K Man; Gwyn Rees; Preeti Gupta; Ecosse L Lamoureux; Eva K Fenwick Journal: Qual Life Res Date: 2019-03-16 Impact factor: 4.147
Authors: T Petra Rausch-Koster; Michiel A J Luijten; F D Verbraak; Ger H M B van Rens; Ruth M A van Nispen Journal: Transl Vis Sci Technol Date: 2022-04-01 Impact factor: 3.283
Authors: Eva K Fenwick; John Barnard; Alfred Gan; Bao Sheng Loe; Jyoti Khadka; Konrad Pesudovs; Ryan Man; Shu Yen Lee; Gavin Tan; Tien Y Wong; Ecosse L Lamoureux Journal: Transl Vis Sci Technol Date: 2020-06-03 Impact factor: 3.283