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. 2021 May 3;10(6):12.
doi: 10.1167/tvst.10.6.12.

Calibration of the Activity Inventory Item Bank: A Patient-Reported Outcome Measurement Instrument for Low Vision Rehabilitation

Micaela Gobeille  1 , Chris Bradley  1 , Judith E Goldstein  1 , Robert Massof  1
Affiliations

Calibration of the Activity Inventory Item Bank: A Patient-Reported Outcome Measurement Instrument for Low Vision Rehabilitation

Micaela Gobeille et al. Transl Vis Sci Technol. .

Abstract

Purpose: To provide calibrated item measures and rating category thresholds for the Activity Inventory (AI), an adaptive visual function questionnaire, from difficulty ratings obtained from a large sample of new low vision patients at pre-rehabilitation baseline.

Methods: Baseline AI (510 items) rating scale data from five previous low vision rehabilitation outcome studies (n = 3623) were combined, and the method of successive dichotomizations was used to estimate calibrated item measures and rating category thresholds. Infit statistics were analyzed to evaluate the fit of the data to the model. Factor analysis was applied to person measures estimated from different subsets of items (e.g., functional domains such as reading, mobility) to evaluate differential person functioning.

Results: Estimated item measures were well targeted to the low vision patient population. The distribution of infit statistics confirmed the validity of the estimated measures and the two-factor structure previously observed for the AI.

Conclusions: Our calibrated item measures and rating category thresholds enable researchers to estimate changes in visual ability from low vision rehabilitation on the same scale, facilitating comparisons between studies.

Translational relevance: The work described in this paper provides calibrated item measures and rating category thresholds for a visual function questionnaire to measure patient-centered outcomes in low vision clinical research. The calibrated AI also can be used as a patient outcome measure and quality assurance tool in clinical practice.

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Conflict of interest statement

Disclosure: M. Gobeille, None; C. Bradley, None; J. Goldstein, None; R. Massof, None

Figures

Figure 1.
Figure 1.
Wright map of person and item measures. Relative frequency of item measures (gray bars) and person measures (red bars) are shown. Item measures range from –4.0 to 5.2 logits, whereas person measures range from –4.1 to 6.2 logits. There is little overlap between person measures and item measures in the tails of the distributions.
Figure 2.
Figure 2.
Standard errors of estimated item measures and person measures. Item measure standard errors are plotted against calibrated item measures (A), and person measures standard errors are plotted against the estimated person measures (B). Standard errors for the calibrated item measures are much smaller than for the person measures. Both graphs show a typical U-shaped distribution, with standard errors being smallest at the point where the items are most targeted by the persons and where the persons are most targeted by the items.
Figure 3.
Figure 3.
Person infit mean square and expected weighted sum of χ2 distributions. Person infit mean squares (gray bars) are plotted relative to a weighted sum of χ2 distributions (red line). Person infit mean squares more frequently fall in the right tail, with fewer infit mean squares falling around the expected value of 1.
Figure 4.
Figure 4.
Item infit z-score. (A) Item measures are plotted relative to item infit z-scores for each functional domain and for goals, with most z-scores falling beyond 2 SDs (indicated by black lines) from the expected mean and thus considered outliers. (B) The cumulative frequency is plotted. A cumulative frequency of 50% is achieved at an infit z-score of –3.06 in reading, 0.95 in visual information, 0.97 in goals, 4.67 in visual motor, and 6.84 in mobility.
Figure 5.
Figure 5.
Factor analysis for functional domains. Factor loadings are plotted for each functional domain. Reading loaded more heavily on factor 1, with 90% of explained variance attributed to factor 1 and 10% of explained variance attributed to factor 2. Goals loaded more heavily on factor 1, with 80% of explained variance attributed to factor 1 and 19% of explained variance attributed to factor 2. Visual information (73% of explained variance attributed to factor 1, 27% of explained variance attributed to factor 2) and visual motor (67% of explained variance attributed to factor 1, 33% of explained variance attributed to factor 2) fell between factor 1 and factor 2. Mobility loaded more heavily on factor 2, with 15% of explained variance attributed to factor 1 and 85% of explained variance attributed to factor 2.
Figure 6.
Figure 6.
Parameters estimated with MSD versus the Andrich Rating Scale Model. Estimated item measures (A), rating category thresholds (B), and person measures estimated from anchored items and thresholds (C) for MSD and the Andrich model are shown. Threshold number is indicated adjacent to each point.
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