The aim was to develop a multidimensional computerized adaptive ă short-form questionnaire, the MusiQoL-MCAT, from a fixed-length QoL ă questionnaire for multiple sclerosis. ă A total of 1992 patients were enrolled in this international ă cross-sectional study. The development of the MusiQoL-MCAT was based on ă the assessment of between-items MIRT model fit followed by real-data ă simulations. The MCAT algorithm was based on Bayesian maximum a ă posteriori estimation of latent traits and Kullback-Leibler information ă item selection. We examined several simulations based on a fixed number ă of items. Accuracy was assessed using correlations (r) between initial ă IRT scores and MCAT scores. Precision was assessed using the standard ă error measurement (SEM) and the root mean square error (RMSE). ă The multidimensional graded response model was used to estimate item ă parameters and IRT scores. Among the MCAT simulations, the 16-item ă version of the MusiQoL-MCAT was selected because the accuracy and ă precision became stable with 16 items with satisfactory levels (r >= ă 0.9, SEM <= 0.55, and RMSE <= 0.3). External validity of the ă MusiQoL-MCAT was satisfactory. ă The MusiQoL-MCAT presents satisfactory properties and can individually ă tailor QoL assessment to each patient, making it less burdensome to ă patients and better adapted for use in clinical practice.