@article {152,
	title = {Using computerized adaptive testing to reduce the burden of mental health assessment},
	journal = {Psychiatric Services},
	volume = {59},
	number = {4},
	year = {2008},
	note = {Gibbons, Robert DWeiss, David JKupfer, David JFrank, EllenFagiolini, AndreaGrochocinski, Victoria JBhaumik, Dulal KStover, AngelaBock, R DarrellImmekus, Jason CR01-MH-30915/MH/United States NIMHR01-MH-66302/MH/United States NIMHResearch Support, N.I.H., ExtramuralUnited StatesPsychiatric services (Washington, D.C.)Psychiatr Serv. 2008 Apr;59(4):361-8.},
	month = {Apr},
	pages = {361-8},
	edition = {2008/04/02},
	abstract = {OBJECTIVE: This study investigated the combination of item response theory and computerized adaptive testing (CAT) for psychiatric measurement as a means of reducing the burden of research and clinical assessments. METHODS: Data were from 800 participants in outpatient treatment for a mood or anxiety disorder; they completed 616 items of the 626-item Mood and Anxiety Spectrum Scales (MASS) at two times. The first administration was used to design and evaluate a CAT version of the MASS by using post hoc simulation. The second confirmed the functioning of CAT in live testing. RESULTS: Tests of competing models based on item response theory supported the scale{\textquoteright}s bifactor structure, consisting of a primary dimension and four group factors (mood, panic-agoraphobia, obsessive-compulsive, and social phobia). Both simulated and live CAT showed a 95\% average reduction (585 items) in items administered (24 and 30 items, respectively) compared with administration of the full MASS. The correlation between scores on the full MASS and the CAT version was .93. For the mood disorder subscale, differences in scores between two groups of depressed patients--one with bipolar disorder and one without--on the full scale and on the CAT showed effect sizes of .63 (p<.003) and 1.19 (p<.001) standard deviation units, respectively, indicating better discriminant validity for CAT. CONCLUSIONS: Instead of using small fixed-length tests, clinicians can create item banks with a large item pool, and a small set of the items most relevant for a given individual can be administered with no loss of information, yielding a dramatic reduction in administration time and patient and clinician burden.},
	keywords = {*Diagnosis, Computer-Assisted, *Questionnaires, Adolescent, Adult, Aged, Agoraphobia/diagnosis, Anxiety Disorders/diagnosis, Bipolar Disorder/diagnosis, Female, Humans, Male, Mental Disorders/*diagnosis, Middle Aged, Mood Disorders/diagnosis, Obsessive-Compulsive Disorder/diagnosis, Panic Disorder/diagnosis, Phobic Disorders/diagnosis, Reproducibility of Results, Time Factors},
	isbn = {1075-2730 (Print)},
	author = {Gibbons, R. D. and Weiss,  D. J. and Kupfer, D. J. and Frank, E. and Fagiolini, A. and Grochocinski, V. J. and Bhaumik, D. K. and Stover, A. and Bock, R. D. and Immekus, J. C.}
}
@article {147,
	title = {Computerized adaptive measurement of depression: A simulation study},
	journal = {BMC Psychiatry},
	volume = {4},
	number = {1},
	year = {2004},
	pages = {13-23},
	abstract = {Background: Efficient, accurate instruments for measuring depression are increasingly importantin clinical practice. We developed a computerized adaptive version of the Beck DepressionInventory (BDI). We examined its efficiency and its usefulness in identifying Major DepressiveEpisodes (MDE) and in measuring depression severity.Methods: Subjects were 744 participants in research studies in which each subject completed boththe BDI and the SCID. In addition, 285 patients completed the Hamilton Depression Rating Scale.Results: The adaptive BDI had an AUC as an indicator of a SCID diagnosis of MDE of 88\%,equivalent to the full BDI. The adaptive BDI asked fewer questions than the full BDI (5.6 versus 21items). The adaptive latent depression score correlated r = .92 with the BDI total score and thelatent depression score correlated more highly with the Hamilton (r = .74) than the BDI total scoredid (r = .70).Conclusions: Adaptive testing for depression may provide greatly increased efficiency withoutloss of accuracy in identifying MDE or in measuring depression severity.},
	keywords = {*Computer Simulation, Adult, Algorithms, Area Under Curve, Comparative Study, Depressive Disorder/*diagnosis/epidemiology/psychology, Diagnosis, Computer-Assisted/*methods/statistics \& numerical data, Factor Analysis, Statistical, Female, Humans, Internet, Male, Mass Screening/methods, Patient Selection, Personality Inventory/*statistics \& numerical data, Pilot Projects, Prevalence, Psychiatric Status Rating Scales/*statistics \& numerical data, Psychometrics, Research Support, Non-U.S. Gov{\textquoteright}t, Research Support, U.S. Gov{\textquoteright}t, P.H.S., Severity of Illness Index, Software},
	author = {Gardner, W. and Shear, K. and Kelleher, K. J. and Pajer, K. A. and Mammen, O. and Buysse, D. and Frank, E.}
}
