Purpose: Instead of applying the usual longitudinal methods to assess the outcome of low-vision rehabilitation services in terms of vision-related quality of life, a three-level Item Response Theory (IRT) method was proposed.
Methods: The translated Vision-Related Quality of Life Core Measure (VCM1) and Low Vision Quality Of Life (LVQOL) questionnaires were used in a nonrandomized follow-up study among elderly patients (n = 296) referred to two different low-vision rehabilitation services in the Netherlands. Factor analysis was performed on the matrix of polychoric correlations to investigate (uni-)dimensionality and to prepare both questionnaires for the multilevel IRT analyses. A statistical model, which was characterized by a graded response model for rating scales, was developed. Threshold and item difficulty parameters and group by time-specific mean fixed effects were estimated. Random individual effects were predicted. Measurement invariance across occasions was tested.
Results: The VCM1 and the LVQOL "reading and fine work" dimension showed item parameter drift. In the multidisciplinary rehabilitation center patients, deterioration was found on the "mobility" dimension after 1 year and improvement was found on "adjustment" and "visual (motor) skills" after 5 months (p < 0.05). Patients in both low-vision services showed improvement on the "reading small print" subscale at both follow-up time points (p < 0.05).
Conclusions: Except for improvement in "reading small print," low-vision rehabilitation services did not seem to contribute substantially to any other dimensions of vision-related quality of life. The results showed a change in only a limited number of individual patients. However, with regard to the field of low-vision rehabilitation, the proposed IRT method seemed to be successful in the follow-up of individuals. IRT specific software was unnecessary. The data did not have to be complete and the use of cumulative logits made the proposed IRT method an economical and efficient approach. Because of item parameter drift, the VCM1 was difficult to interpret. The use of multilevel IRT models with longitudinal data and dependent observations is recommended.