Applying multidimensional computerized adaptive testing to the MSQOL-54: a simulation study

Abstract

Background The Multiple Sclerosis Quality of Life-54 (MSQOL-54) is one of the most commonly-used MS-specific health-related quality of life (HRQOL) measures. It is a multidimensional, MS-specific HRQOL inventory, which includes the generic SF-36 core items, supplemented with 18 MS-targeted items. Availability of an adaptive short version providing immediate item scoring may improve instrument usability and validity. However, multidimensional computerized adaptive testing (MCAT) has not been previously applied to MSQOL-54 items. We thus aimed to apply MCAT to the MSQOL-54 and assess its performance. Methods Responses from a large international sample of 3669 MS patients were assessed. We calibrated 52 (of the 54) items using bifactor graded response model (10 group factors and one general HRQOL factor). Then, eight simulations were run with different termination criteria: standard errors (SE) for the general factor and group factors set to different values, and change in factor estimates from one item to the next set at < 0.01 for both the general and the group factors. Performance of the MCAT was assessed by the number of administered items, root mean square difference (RMSD), and correlation. Results Eight items were removed due to local dependency. The simulation with SE set to 0.32 (general factor), and no SE thresholds (group factors) provided satisfactory performance: the median number of administered items was 24, RMSD was 0.32, and correlation was 0.94. Conclusions Compared to the full-length MSQOL-54, the simulated MCAT required fewer items without losing precision for the general HRQOL factor. Further work is needed to add/integrate/revise MSQOL-54 items in order to make the calibration and MCAT performance efficient also on group factors, so that the MCAT version may be used in clinical practice and research.

Application of the professional maturity scale as a computerized adaptive testing

“…The study also found that when variable length stopping rules are used (SE<0.30; SE<0.50; SE<0.70), ability estimation can be made with 84%-90% fewer items than the original scale. This finding is in parallel with the advantage of the following ratios in the number of items: the rate of 73.3% was reached as a result of the study of Gardner et al (2004); the rates of 36%-65% reached as a result of the study of Smits et al (2011); the 50.86% rate reached as a result of Aybek and Demirtaşlı's ( 2017) study; the rate of 75% obtained in the study of Gibbons et al (2016); the rate of 67% obtained in the study by Stochl et al (2016); the 50%-85% rates obtained in the study by Petersen et al (2016); the 30%-71% rates obtained in the study by Choi and McClenen (2020); the rate of 75% obtained in the study by Harrison et al (2020); the 50%-63% rates obtained in the study by Yasuda et al (2021); the 62%-96% rates obtained in the study by Liu et al (2022); the rate of 78% obtained in the study by Giordano et al (2023). In the studies conducted by Smits et al (2011) andAybek andDemirtaşlı (2018), it can be said that the low ratio in test lengths is because there is a more limited pool of items compared to other studies.…”