Breiman, F. (2017). Classification and Regression Trees. Taylor & Francis. https://doi.org/10.1201/9781315139470
Choi, S. W., Reise, S. P., Pilkonis, P. A., Hays, R. D., & Cella, D. (2010). Efficiency of static and computer adaptive short forms compared to full-length measures of depressive symptoms. Quality of Life Research, 19(1), 125–136. https://doi.org/10.1007/s11136-009-9560-5
Fayers, P. M. (2007). Applying item response theory and computer adaptive testing: the challenges for health outcomes assessment. Quality of Life Research, 16(1), 187–194. https://doi.org/10.1007/s11136-007-9197-1
Finkelman, M. D., He, Y., Kim, W., & Lai, A. M. (2011). Stochastic curtailment of health questionnaires: a method to reduce respondent burden. Statistics in Medicine, 30(16), 1989–2004. https://doi.org/10.1002/sim.4231
Finkelman, M. D., Smits, N., Kim, W., & Riley, B. (2012). Curtailment and stochastic curtailment to shorten the CES-D. Applied Psychological Measurement, 36(8), 632–658. https://doi.org/10.1177/0146621612451647
Flens, G., Smits, N., Terwee, C. B., Dekker, J., Huijbrechts, I., & Beurs, E. (2017). Development of a Computer Adaptive Test for Depression Based on the Dutch-Flemish Version of the PROMIS Item Bank. Evaluation and the Health Professions, 40(1), 79–105. https://doi.org/10.1177/0163278716684168
Gibbons, R. D., Chattopadhyay, I., Meltzer, H. Y., Kane, J. M., & Guinart, D. (2022). Development of a computerized adaptive diagnostic screening tool for psychosis. Schizophrenia Research, 245, 116–121. https://doi.org/10.1016/j.schres.2021.03.020
Gibbons, R. D., Hooker, G., Finkelman, M. D., Weiss, D. J., Pilkonis, P. A., Frank, E., Moore, T., & Kupfer, D. J. (2013). The CAD-MDD: A computerized adaptive diagnostic screening tool for depression. Journal of Clinical Psychiatry, 74(7), 669–674. https://doi.org/10.4088/JCP.12m08338
Gibbons, C., Porter, I., Gonçalves-Bradley, D. C., Stoilov, S., Ricci-Cabello, I., Tsangaris, E., Gangannagaripalli, J., Davey, A., Gibbons, E. J., Kotzeva, A., Evans, J., Wees, P. J., Kontopantelis, E., Greenhalgh, J., Bower, P., Alonso, J., & Valderas, J. M. (2021). Routine provision of feedback from patient-reported outcome measurements to healthcare providers and patients in clinical practice. Cochrane Database Systematic Reviews. https://doi.org/10.1002/14651858.CD011589.pub2
Gibbons, R. D., & Wang, P. S. (2023). The science of psychiatric measurement. Psychiatric Annals, 53(9), 400–404. https://doi.org/10.3928/00485713-20230818-01
Gibbons, R. D., Weiss, D. J., Frank, E., & Kupfer, D. (2016). Computerized adaptive diagnosis and testing of mental health disorders. Annual Review of Clinical Psychology, 12, 83–104. https://doi.org/10.1146/annurev-clinpsy-021815-093634
Greenhalgh, J. (2009). The applications of PROs in clinical practice: what are they, do they work, and why? Quality of Life Research, 18(1), 115–123. https://doi.org/10.1007/s11136-008-9430-6
Hagenaars, J.A.P., McCutcheon, A.L. (eds.): Applied Latent Class Analysis. Cambridge University Press, (2002).https://doi.org/10.1017/CBO9780511499531
Kemper, C. J., Trapp, S., Kathmann, N., Samuel, D. B., & Ziegler, M. (2018). Short versus long scales in clinical assessment: Exploring the trade-off between resources saved and psychometric quality lost using two measures of obsessive–compulsive symptoms. Assessment, 26(5), 767–782. https://doi.org/10.1177/1073191118810057
Kroenke, K., Spitzer, R. L., & Williams, J. B. W. (2001). The PHQ-9. Journal of General Internal Medicine, 16(9), 606–613. https://doi.org/10.1046/j.1525-1497.2001.016009606.x
Article CAS PubMed PubMed Central Google Scholar
Kroenke, K., Spitzer, R. L., Williams, J. B. W., Monahan, P. O., & Löwe, B. (2007). Anxiety disorders in primary care: Prevalence, impairment, comorbidity, and detection. Annals of Internal Medicine, 146(5), 317–325. https://doi.org/10.7326/0003-4819-146-5-200703060-00004
Kruyen, P. M., Emons, W. H. M., & Sijtsma, K. (2013). On the shortcomings of shortened tests: A literature review. International Journal of Testing, 13(3), 223–248. https://doi.org/10.1080/15305058.2012.703734
Levis, B., Benedetti, A., Thombs, B.D., DEPRESsion Screening Data (DEPRESSD) Collaboration, Riehm, K.E., Saadat, N., Levis, A.W., Azar, M., Rice, D.B., Chiovitti, M.J., Sanchez, T.A., Boruff, J., Cuijpers, P., Gilbody, S., Ioannidis, J.P.A., Kloda, L.A., McMillan, D., Patten, S.B., Shrier, I., Ziegelstein, R.C., Akena, D.H., Arroll, B., Ayalon, L., Baradaran, H.R., Baron, M., Bombardier, C.H., Butterworth, P., Carter, G., Chagas, M.H., Chan, J.C.N., Clover, K., Conwell, Y., Man-van Ginkel, J.M., Delgadillo, J., Fann, J.R., Fischer, F.H., Fung, D., Gelaye, B., Goodyear-Smith, F., Greeno, C.G., Hall, B.J., Hambridge, J., Harrison, P.A., Härter, M., Hegerl, U., Hides, L., Hobfoll, S.E., Hudson, M., Inagaki, M., Ismail, K., Jetté, N., Khamseh, M.E., Kiely, K.M., Kwan, Y., Liu, S.-I., Lotrakul, M., Loureiro, S.R., Löwe, B., Marsh, L., McGuire, A., Sidik, S.M., Munhoz, T.N., Muramatsu, K., Osório, F.L., Patel, V., Pence, B.W., Persoons, P., Picardi, A., Reuter, K., Rooney, A.G., Santos, I.S., Shaaban, J., Sidebottom, A., Simning, A., Stafford, L., Sung, S.C., Tan, P.L.L., Turner, A., Feltz-Cornelis, C.M., van Weert, H.C., Vöhringer, P.A., White, J., Whooley, M.A., Winkley, K., Yamada, M., Zhang, Y.(2019). Accuracy of patient health questionnaire-9 (phq-9) for screening to detect major depression: individual participant data meta-analysis. BMJ. 10.1136/bmj.l1476
Linzer, D. A., & Lewis, J. B. (2011). poLCA: An R package for polytomous variable latent class analysis. Journal of Statistical Software, 42(10), 1–29.
Löwe, B., Kroenke, K., & Gräfe, K. (2005). Detecting and monitoring depression with a two-item questionnaire (phq-2). Journal of Psychosomatic Research, 58(2), 163–171. https://doi.org/10.1016/j.jpsychores.2004.09.006
Marshall, S., Haywood, K., & Fitzpatrick, R. (2006). Impact of patient-reported outcome measures on routine practice: a structured review. Journal of Evaluation in Clinical Practice, 12(5), 559–568. https://doi.org/10.1111/j.1365-2753.2006.00650.x
McHugh, M. L. (2012). Interrater reliability: The kappa statistic. Biochemia Medica, 22(3), 276.
PubMed PubMed Central Google Scholar
Morris, J., Perez, D., & McNoe, B. (1997). The use of quality of life data in clinical practice. Quality of Life Research, 7(1), 85–91. https://doi.org/10.1023/A:1008893007068
Nelson, E. C., Eftimovska, E., Lind, C., Hager, A., Wasson, J. H., & Lindblad, S. (2015). Patient reported outcome measures in practice. BMJ. https://doi.org/10.1136/bmj.g7818
Article PubMed PubMed Central Google Scholar
Neulinger, B., Ebert, C., Lochbühler, K., Bergmann, A., Gensichen, J., & Lukaschek, K. (2024). Screening tools assessing mental illness in primary care: A systematic review. European Journal of General Practice, 30(1), 2418299. https://doi.org/10.1080/13814788.2024.2418299
Article PubMed PubMed Central Google Scholar
Psychogyiopoulos, A., Smits, N., & Van der Ark, L.A. (2025). Estimating the joint item-score density using an unrestricted latent class model: advancing flexibility in computerized adaptive testing. Journal of Computerized Adaptive Testing, 12(3), 136–164. https://doi.org/10.7333/2507-1203136
R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2022). R Foundation for Statistical Computing. https://www.R-project.org/
Reise, S. P., & Waller, N. G. (2009). Item response theory and clinical measurement. Annual Review of Clinical Psychology, 5, 27–48. https://doi.org/10.1146/annurev.clinpsy.032408.153553
Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6(2), 461–464. https://doi.org/10.1214/aos/1176344136
Sijtsma, K., Ellis, J. L., & Borsboom, D. (2024). Recognize the value of the sum score, psychometrics’ greatest accomplishment. Psychometrika, 89(1), 84–117. https://doi.org/10.1007/s11336-024-09964-7
Article PubMed PubMed Central Google Scholar
Smits, N., Van der Ark, L. A., & Conijn, J. M. (2018). Measurement versus prediction in the construction of patient-reported outcome questionnaires: can we have our cake and eat it? Quality of Life Research, 27(7), 1673–1682. https://doi.org/10.1007/s11136-017-1720-4
Smits, N., & Finkelman, M. D. (2015). Shortening the PHQ-9: a proof-of-principle study of utilizing Stochastic Curtailment as a method for constructing ultrashort screening instruments. General Hospital Psychiatry, 37(5), 464–469. https://doi.org/10.1016/j.genhosppsych.2015.04.011
Smits, N., Finkelman, M. D., & Kelderman, H. (2016). Stochastic Curtailment of questionnaires for three-level classification: Shortening the CES-D for assessing low, moderate, and high risk of depression. Applied Psychological Measurement, 40(1), 22–36. https://doi.org/10.1177/0146621615592294
Spitzer, R. L., Kroenke, K., Williams, J. B. W., & Löwe, B. (2006). A brief measure for assessing generalized anxiety disorder: The GAD-7. Archives of Internal Medicine, 166(10), 1092–1097. https://doi.org/10.1001/archinte.166.10.1092
Strobl, C., Malley, J., & Tutz, G. (2009). An introduction to recursive partitioning: Rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychological Methods, 14(4), 323–348.
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