00949nas a2200169   4500008003900000022001400039245011500053210006900168260000800237300001600245490000700261520040300268100001700671700002400688700002100712856004600733       2018                          d  a1573-264900aSome recommendations for developing multidimensional computerized adaptive tests for patient-reported outcomes0 aSome recommendations for developing multidimensional computerize  cApr  a1055–10630 v273 aMultidimensional item response theory and computerized adaptive testing (CAT) are increasingly used in mental health, quality of life (QoL), and patient-reported outcome measurement. Although multidimensional assessment techniques hold promises, they are more challenging in their application than unidimensional ones. The authors comment on minimal standards when developing multidimensional CATs.1 aSmits, Niels1 aPaap, Muirne, C. S.1 aBöhnke, Jan, R.  uhttps://doi.org/10.1007/s11136-018-1821-801723nas a2200145   4500008003900000245015500039210006900194300001000263490000700273520118100280100001701461700002701478700002001505856005201525       2016                          d00aStochastic Curtailment of Questionnaires for Three-Level Classification: Shortening the CES-D for Assessing Low, Moderate, and High Risk of Depression0 aStochastic Curtailment of Questionnaires for ThreeLevel Classifi  a22-360 v403 aIn clinical assessment, efficient screeners are needed to ensure low respondent burden. In this article, Stochastic Curtailment (SC), a method for efficient computerized testing for classification into two classes for observable outcomes, was extended to three classes. In a post hoc simulation study using the item scores on the Center for Epidemiologic Studies–Depression Scale (CES-D) of a large sample, three versions of SC, SC via Empirical Proportions (SC-EP), SC via Simple Ordinal Regression (SC-SOR), and SC via Multiple Ordinal Regression (SC-MOR) were compared at both respondent burden and classification accuracy. All methods were applied under the regular item order of the CES-D and under an ordering that was optimal in terms of the predictive power of the items. Under the regular item ordering, the three methods were equally accurate, but SC-SOR and SC-MOR needed less items. Under the optimal ordering, additional gains in efficiency were found, but SC-MOR suffered from capitalization on chance substantially. It was concluded that SC-SOR is an efficient and accurate method for clinical screening. Strengths and weaknesses of the methods are discussed.1 aSmits, Niels1 aFinkelman, Matthew, D.1 aKelderman, Henk  uhttp://apm.sagepub.com/content/40/1/22.abstract