Development and Validation of an Item Bank for Drug Dependence Measurement Using Computer Adaptive Testing

References

• Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723. https://doi.org/10.1109/TAC.1974.1100705
   Web of Science ®Google Scholar
• American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (DSM-5®). American Psychiatric Pub.
   Google Scholar
• Baker, F. B. (1992). Item response theory: Parameter estimation techniques. Marcel Dekker.
   Google Scholar
• Baker, F. B., & Kim, S. H. (2004). Item response theory: Parameter estimation techniques. CRC Press.
   Google Scholar
• Bock, R. D., & Mislevy, R. J. (1982). Adaptive EAP estimation of ability in a microcomputer environment. Applied Psychological Measurement, 6(4), 431–444. https://doi.org/10.1177/014662168200600405
   Web of Science ®Google Scholar
• Bond, T., & Fox, C. M. (2015). Applying the Rasch model: Fundamental measurement in the human sciences. Routledge.
   Google Scholar
• Butcher, J. N., Keller, L. S., & Bacon, S. F. (1985). Current developments and future directions in computerized personality assessment. Journal of Consulting and Clinical Psychology, 53(6), 803–815. https://doi.org/10.1037/0022-006X.53.6.803
   PubMed Web of Science ®Google Scholar
• Butler, S. F., Black, R. A., McCaffrey, S. A., Ainscough, J., & Doucette, A. M. (2017). A computer adaptive testing version of the Addiction Severity Index-Multimedia Version (ASI-MV): The addiction severity CAT. Psychology of Addictive Behaviors: Journal of the Society of Psychologists in Addictive Behaviors, 31(3), 265–275. https://doi.org/10.1037/adb0000256
   PubMed Web of Science ®Google Scholar
• Chalmers, R. P. (2012). mirt: A multidimensional item response theory package for the r environment. Journal of Statistical Software, 48(6), 1–29. v048.i06. https://doi.org/10.18637/jss
   Web of Science ®Google Scholar
• Chang, H. H., & Ying, Z. (1996). A global information approach to computerized adaptive testing. Applied Psychological Measurement, 20(3), 213–229. https://doi.org/10.1177/014662169602000303
   Web of Science ®Google Scholar
• Choi, S. W., Gibbons, L. E., & Crane, P. K. (2011). Lordif: An R package for detecting differential item functioning using iterative hybrid ordinal logistic regression/item response theory and Monte Carlo simulations. Journal of Statistical Software, 39(8), 1. https://doi.org/10.18637/jss.v039.i08
   PubMed Web of Science ®Google Scholar
• Cox, L. S., Tiffany, S. T., & Christen, A. G. (2001). Evaluation of the brief questionnaire of smoking urges (QSU-brief) in laboratory and clinical settings. Nicotine & Tobacco Research, 3(1), 7–16. https://doi.org/10.1080/14622200124218
   PubMedGoogle Scholar
• Dargan, P. I., & Wood, D. M. (2012). Recreational drug use in the Asia Pacific region: Improvement in our understanding of the problem through the UNODC programmes. Journal of Medical Toxicology: Official Journal of the American College of Medical Toxicology, 8(3), 295–299. https://doi.org/10.1007/s13181-012-0240-4
   PubMedGoogle Scholar
• Davis, A. K., Osborn, L. A., Rosenberg, H., Cross, N., Lauritsen, K. J., Ashrafioun, L., Bradbury, S., Feuille, M., Lackey, J. H., Hawley, A., & Leith, J. (2014). Psychometric evaluation of the marijuana reduction strategies self-efficacy scale with young recreational marijuana users. Addictive Behaviors, 39(12), 1750–1754. https://doi.org/10.1016/j.addbeh.2014.07.005
   PubMed Web of Science ®Google Scholar
• Edelen, M. O., Tucker, J. S., Shadel, W. G., Stucky, B. D., Cerully, J., Li, Z., Hansen, M., & Cai, L. (2014). Development of the PROMIS health expectancies of smoking item banks. Nicotine & Tobacco Research, 16(Suppl. 3), S223–S231. https://doi.org/10.1093/ntr/ntu053
   PubMedGoogle Scholar
• Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists. Mahway.
   Google Scholar
• Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874. https://doi.org/10.1016/j.patrec.2005.10.010
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Flens, G., Smits, N., Carlier, I., van Hemert, A. M., & de Beurs, E. (2016). Simulating computer adaptive testing with the Mood and Anxiety symptom questionnaire. Psychological Assessment, 28(8), 953–962. https://doi.org/10.1037/pas0000240
   PubMed Web of Science ®Google Scholar
• Flens, G., Smits, N., Terwee, C. B., Dekker, J., Huijbrechts, I., & de Beurs, E. (2017). Development of a computer adaptive test for depression based on the Dutch-Flemish version of the PROMIS item bank. Evaluation & the Health Professions, 40(1), 79–105. https://doi.org/10.1177/0163278716684168
   PubMed Web of Science ®Google Scholar
• Forbey, J. D., & Ben-Porath, Y. S. (2007). Computerized adaptive personality testing: A review and illustration with the MMPI-2 computerized adaptive version. Psychological Assessment, 19(1), 14–24. https://doi.org/10.1037/1040-3590.19.1.14
   PubMed Web of Science ®Google Scholar
• French, A. W., & Miller, T. R. (1996). Logistic regression and its use in detecting differential item functioning in polytomous items. Journal of Educational Measurement, 33(3), 315–332. 1996.tb00495.x. https://doi.org/10.1111/j.1745-3984
   Web of Science ®Google Scholar
• Geng, L. N., Qian, B. J., & Shen, H. (2010). Initial revision of drug relapse risk scale. Chinese Journal of Clinical Psychology, 18(3), 301–303. https://doi.org/10.16128/j.cnki.1005-3611.2010.03.012.
   Google Scholar
• Gershon, R. C. (2005). Computer adaptive testing. Journal of Applied Measurement, 6(1), 109–127.
   PubMedGoogle Scholar
• Gibbons, R. D., Hooker, G., Finkelman, M. D., Weiss, D. J., Pilkonis, P. A., Frank, E., Moore, T., & Kupfer, D. J. (2013). The computerized adaptive diagnostic test for major depressive disorder (CAD-MDD): a screening tool for depression. The Journal of Clinical Psychiatry, 74(7), 669–674. https://doi.org/10.4088/JCP.12m08338
   PubMed Web of Science ®Google Scholar
• Gossop, M. (1990). The development of a short opiate withdrawal scale (SOWS). Addictive Behaviors, 15(5), 487–490. https://doi.org/10.1016/0306-4603(90)90036-W
   PubMed Web of Science ®Google Scholar
• Green, B. F., Bock, R. D., Humphreys, L. G., Linn, R. L., & Reckase, M. D. (1984). Technical guidelines for assessing computerized adaptive tests. Journal of Educational Measurement, 21(4), 347–360. tb01039. x. https://doi.org/10.1111/j.1745-3984.1984
   Web of Science ®Google Scholar
• Hansen, M., Cai, L., Stucky, B. D., Tucker, J. S., Shadel, W. G., & Edelen, M. O. (2014). Methodology for developing and evaluating the PROMIS smoking item banks. Nicotine & Tobacco Research, 16(Suppl. 3), S175–S189. https://doi.org/10.1093/ntr/ntt123
   PubMedGoogle Scholar
• Heishman, S. J., Singleton, E. G., & Liguori, A. (2001). Marijuana craving questionnaire: Development and initial validation of a self-report instrument. Addiction (Abingdon, England), 96(7), 1023–1034. doi: 10.1046/j.1360-0443.2001.967102312.x.
   PubMed Web of Science ®Google Scholar
• Heishman, S. J., Singleton, E. G., & Moolchan, E. T. (2003). Tobacco craving questionnaire: Reliability and validity of a new multifactorial instrument. Nicotine & Tobacco Research: Official Journal of the Society for Research on Nicotine and Tobacco, 5(5), 645–654. https://doi.org/10.1080/1462220031000158681
   PubMed Web of Science ®Google Scholar
• Johnston, K. L., Lawrence, S. M., Dodds, N. E., Yu, L., Daley, D. C., & Pilkonis, P. A. (2016). Evaluating PROMIS® instruments and methods for patient-centered outcomes research: Patient and provider voices in a substance use treatment setting. Quality of Life Research: An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation, 25(3), 615–624. https://doi.org/10.1007/s11136-015-1131-3
   PubMed Web of Science ®Google Scholar
• Kang, T., & Chen, T. T. (2008). Performance of the generalized S‐X2 item fit index for polytomous IRT models. Journal of Educational Measurement, 45(4), 391–406. 00071.x. https://doi.org/10.1111/j.1745-3984.2008
   Web of Science ®Google Scholar
• Kraemer, H. C., & Kupfer, D. J. (2006). Size of treatment effects and their importance to clinical research and practice. Biological Psychiatry, 59(11), 990–996. https://doi.org/10.1016/j.biopsych.2005.09.014
   PubMed Web of Science ®Google Scholar
• Kreitzberg, C. B., & Jones, D. H. (1980). An empirical study of the broad range tailored test of verbal ability. ETS Research Report Series, 1980(1), i–232. 1980.tb01195. x. https://doi.org/10.1002/j.2333-8504
   Google Scholar
• Lian, Z., & Liu, Z. M. (2003). Preliminary development and reliability test of opioid addiction severity scale. Journal of Pharmaceutical Epidemiology, 12(2), 85–88.
   Google Scholar
• Liu, K. J., & Hao, W. (2003). The male heroin addict’s community rehabilitation efficacy research and preliminary preparation of heroin craving questionnaire [Doctoral dissertation]. Central South University PhD thesis.
   Google Scholar
• Liu, Z. M., Cao, J. Q., Shi, F., & Cai, Z. J. (1995). Epidemiological study on the abuse of dihydroetorphine hydrochloride. China Bull Drug Depend, 4(4), 223–231. https://doi.org/1013936/j.cnki.cjdd1992.1995.04.007.
   Google Scholar
• Magis, D., & Barrada, J. R. (2017). Computerized adaptive testing with R: Recent updates of the package catR. Journal of Statistical Software, 76(Code Snippet 1), 1–19. v076.c01. https://doi.org/10.18637/jss
   Google Scholar
• Magis, D., & Raîche, G. (2012). Random generation of response patterns under computerized adaptive testing with the R package catR. Journal of Statistical Software, 48(8), 1–31. v048.i08. https://doi.org/10.18637/jss
   Web of Science ®Google Scholar
• McLellan, A. T., Luborsky, L., Woody, G. E., & O'Brien, C. P. (1980). An improved diagnostic evaluation instrument for substance abuse patients: The addiction severity index. Journal of Nervous and Mental Disease, 168(1), 26–33. https://doi.org/10.1097/00005053-198001000-00006
   Google Scholar
• Metrik, J., Farris, S. G., Aston, E. R., & Kahler, C. W. (2017). Development and initial validation of a marijuana cessation expectancies questionnaire. Drug and Alcohol Dependence, 177, 163–170. https://doi.org/10.1016/j.drugalcdep.2017.04.005
   PubMed Web of Science ®Google Scholar
• Mossman, D. (1999). Three-way rocs. Medical Decision Making: An International Journal of the Society for Medical Decision Making, 19(1), 78–89. https://doi.org/10.1177/0272989X9901900110
   PubMed Web of Science ®Google Scholar
• Muthén, B. (1984). A general structural equation model with dichotomous, ordered categorical, and continuous latent variable indicators. Psychometrika, 49(1), 115–132. https://doi.org/10.1007/BF02294210
   Web of Science ®Google Scholar
• Muthen, L. K., & Muthen, B. O. (2002). Mplus user’s guide (2nd ed.). Muthen & Muthen.
   Google Scholar
• Nakas, C. T., & Yiannoutsos, C. T. (2004). Ordered multiple‐class ROC analysis with continuous measurements. Statistics in Medicine, 23(22), 3437–3449. https://doi.org/10.1002/sim.1917
   PubMed Web of Science ®Google Scholar
• Nunally, J. C., & Bernstein, I. (1978). Psychometric theory. McGraw-Hill.
   Google Scholar
• Oosterveld, P., Vorst, H. C., & Smits, N. (2019). Methods for questionnaire design: A taxonomy linking procedures to test goals. Quality of Life Research: An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation, 28(9), 2501–2512. https://doi.org/10.1007/s11136-019-02209-6
   PubMed Web of Science ®Google Scholar
• Petersen, M. A., Aaronson, N. K., Chie, W.-C., Conroy, T., Costantini, A., Hammerlid, E., Hjermstad, M. J., Kaasa, S., Loge, J. H., Velikova, G., Young, T., & Groenvold, M. (2016). Development of an item bank for computerized adaptive test (CAT) measurement of pain. Quality of Life Research: An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation, 25(1), 1–11. https://doi.org/10.1007/s11136-015-1069-5
   PubMed Web of Science ®Google Scholar
• Pilkonis, P. A., Yu, L., Colditz, J., Dodds, N., Johnston, K. L., Maihoefer, C., Stover, A. M., Daley, D. C., & McCarty, D. (2013). Item banks for alcohol use from the Patient-Reported Outcomes Measurement Information System (PROMIS): Use, consequences, and expectancies. Drug and Alcohol Dependence, 130(1–3), 167–177. https://doi.org/10.1016/j.drugalcdep.2012.11.002
   PubMed Web of Science ®Google Scholar
• Pilkonis, P. A., Yu, L., Dodds, N. E., Johnston, K. L., Lawrence, S. M., Hilton, T. F., Daley, D. C., Patkar, A. A., & McCarty, D. (2015). Item banks for substance use from the Patient-Reported Outcomes Measurement Information System (PROMIS(®)): Severity of use and positive appeal of use. Drug and Alcohol Dependence, 156, 184–192. https://doi.org/10.1016/j.drugalcdep.2015.09.008
   PubMed Web of Science ®Google Scholar
• Pilkonis, P. A., Yu, L., Dodds, N. E., Johnston, K. L., Lawrence, S. M., Hilton, T. F., Daley, D. C., Patkar, A. A., & McCarty, D. (2016). Corrigendum to “Item banks for substance use from the Patient-Reported Outcomes Measurement Information System (PROMIS®): Severity of use and positive appeal of use. Drug and Alcohol Dependence, 159, 285–286. https://doi.org/10.1016/j.drugalcdep.2015.12.003
   Web of Science ®Google Scholar
• Reise, S. P., & Revicki, D. A. (2015). Handbook of item response theory modeling: Applications to typical performance assessment. Routledge.
   Google Scholar
• Samejima, F. (1994). Estimation of reliability coefficients using the test information function and its modifications. Applied Psychological Measurement 18, 229–244. https://doi.org/10.1177/014662169401800304
   Web of Science ®Google Scholar
• Samejima, F. (1997). Graded response model. In Handbook of modern item response theory (pp. 85-100). Springer, New York, NY.
   Google Scholar
• Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461–464. https://doi.org/10.1214/aos/1176345415
   Web of Science ®Google Scholar
• Shadel, W. G., Edelen, M. O., Tucker, J. S., Stucky, B. D., Hansen, M., & Cai, L. (2014). Development of the PrOMis® nicotine dependence item banks. Nicotine & Tobacco Research, 16(Suppl_3), S190–S201. https://doi.org/10.1093/ntr/ntu032
   PubMedGoogle Scholar
• Sing, T., Sander, O., Beerenwinkel, N., & Lengauer, T. (2005). ROCR: Visualizing classifier performance in R. Bioinformatics, 21(20), 3940–3941. https://doi.org/10.1093/bioinformatics/bti623
   PubMed Web of Science ®Google Scholar
• Skinner, H. A. (1982). The drug abuse screening test. Addictive Behaviors, 7(4), 363–371. https://doi.org/10.1016/0306-4603(82)90005-3
   PubMed Web of Science ®Google Scholar
• Spiegelhalter, D. J., Best, N. G., Carlin, B. P., & Van der Linde, A. (1998). Bayesian deviance, the effective number of parameters, and the comparison of arbitrarily complex models (pp. 98–009, Research Report, 98-009).
   Google Scholar
• Sun, H. Q., Bao, Y. P., Zhou, S. J., Meng, S. Q., & Lu, L. (2014). The new pattern of drug abuse in China. Current Opinion in Psychiatry, 27(4), 251–255. https://doi.org/10.1097/YCO.0000000000000073
   PubMed Web of Science ®Google Scholar
• Swaminathan, H., & Rogers, H. J. (1990). Detecting differential item functioning using logistic regression procedures. Journal of Educational Measurement, 27(4), 361–370. 1990.tb00754. x. https://doi.org/10.1111/j.1745-3984
   Web of Science ®Google Scholar
• Tan, Q., Cai, Y., Li, Q., Zhang, Y., & Tu, D. (2018). Development and validation of an item bank for depression screening in the Chinese population using computer adaptive testing: A simulation study. Frontiers in Psychology, 9, 1225. https://doi.org/10.3389/fpsyg.2018.01225
   PubMed Web of Science ®Google Scholar
• Tiffany, S. T., Singleton, E., Haertzen, C. A., & Henningfield, J. E. (1993). The development of a cocaine craving questionnaire. Drug and Alcohol Dependence, 34(1), 19–28. https://doi.org/10.1016/0376-8716(93)90042-O
   PubMed Web of Science ®Google Scholar
• Wainer, H., Dorans, N. J., Flaugher, R., Green, B. F., & Mislevy, R. J. (2014). Computerized adaptive testing: A primer (2nd ed.). Routledge.
   Google Scholar
• Wan, C. H., Fang, J. Q., & Chen, L. Y. (1997). Development of quality of life scale for drug addicts determination and evaluation. Chinese Journal of Behavioral Medicine, 6 (6), 169–171.
   Google Scholar
• Wang, Z., Li, W. X., & Zhi-Min, L. (2017). Similarity and difference in drug addiction process between heroin- and methamphetamine-dependent users. Substance Use & Misuse, 52(4), 459–467. https://doi.org/10.1080/10826084.2016.1245331
   PubMed Web of Science ®Google Scholar
• West, R., & Miller, P. (2011). What is the purpose of diagnosing addiction or dependence and what does this mean for establishing diagnostic criteria? Addiction (Abingdon, England), 106(5), 863–865. doi: 10.1111/j.1360-0443.2011.03377.x.
   PubMed Web of Science ®Google Scholar
• Yang, D. S. (1993). Schedule of clinical interview diagnose: Drug dependence (SCID-DD). In The manual of assessment in mental health (pp. 232-234). Hunan Scientific & Technology Press.
   Google Scholar
• Zumbo, B. D. (1999). A handbook on the theory and methods of differential item functioning (DIF). National Defense Headquarters.
   Google Scholar
• Zweig, M. H., & Campbell, G. (1993). Receiver-operating characteristic (ROC) plots: A fundamental evaluation tool in clinical medicine. Clinical Chemistry, 39(4), 561–577. https://doi.org/10.1093/clinchem/39.4.561
   PubMed Web of Science ®Google Scholar