Shortening the Current Opioid Misuse Measure via computer-based testing: a retrospective proof-of-concept study

BMC Medical Research Methodology

Stephen F Butler¹  Niels Smits²  Driss Zoukhri⁴  Ronald J Kulich³  Matthew D Finkelman⁵ 

[1] Inflexxion, Inc., Newton, MA 02464, USA;Department of Clinical Psychology, VU University Amsterdam, Amsterdam, the Netherlands;Craniofacial Pain Center, Tufts University School of Dental Medicine, Boston, MA 02111, USA;Department of Diagnosis and Health Promotion, Tufts University School of Dental Medicine, Boston, MA 02111, USA;Department of Public Health and Community Service, Tufts University School of Dental Medicine, Boston, MA 02111, USA

关键词: Computer-based testing;    Respondent burden;    Questionnaire;    Opioids;    Chronic pain;    Substance abuse;   

Others  :  866640
DOI  :  10.1186/1471-2288-13-126

 received in 2013-06-26, accepted in 2013-10-15,  发布年份 2013

【 摘 要 】

Background

The Current Opioid Misuse Measure (COMM) is a self-report questionnaire designed to help identify aberrant drug-related behavior in respondents who have been prescribed opioids for chronic pain. The full-length form of the COMM consists of 17 items. Some individuals, especially compromised individuals, may be deterred from taking the full questionnaire due to its length. This study examined the use of curtailment and stochastic curtailment, two computer-based testing approaches that sequentially determine the test length for each individual, to reduce the respondent burden of the COMM without compromising sensitivity and specificity.

Methods

Existing data from n = 415 participants, all of whom had taken the full-length COMM and had been classified via the Aberrant Drug Behavior Index (ADBI), were divided into training (n = 214) and test (n = 201) sets. Post-hoc analysis of the test set was performed to evaluate the screening results and test lengths that would have been obtained, if curtailment or stochastic curtailment had been used. Sensitivity, specificity, and average test length were calculated for each method and compared with the corresponding values of the full-length test.

Results

The full-length COMM had a sensitivity of 0.703 and a specificity of 0.701 for predicting the ADBI. Curtailment reduced the average test length by 22% while maintaining the same sensitivity and specificity as the full-length COMM. Stochastic curtailment reduced the average test length by as much as 59% while always obtaining a sensitivity of at least 0.688 and a specificity of at least 0.701 for predicting the ADBI.

Conclusions

Curtailment and stochastic curtailment have the potential to achieve substantial reductions in respondent burden without compromising sensitivity and specificity. The two sequential methods should be considered for future computer-based administrations of the COMM.

【 授权许可】

   
2013 Finkelman et al.; licensee BioMed Central Ltd.

【 参考文献 】

• [1]Bernabei R, Gambassi G, Lapane K, Landi F, Gatsonis C, Dunlop R, Lipsitz L, Steel K, Mor V: Management of pain in elderly patients with cancer. JAMA 1998, 279:1877-1882.
• [2]Gilson AM, Ryan KM, Joranson DE, Dahl JL: A reassessment of trends in the medical use and abuse of opioid analgesics and implications for diversion control: 1997–2002. J Pain Symptom Manage 2004, 28:176-188.
• [3]Joranson DE, Ryan KM, Gilson AM, Dahl JL: Trends in medical use and abuse of opioid analgesics. JAMA 2000, 283:1710-1714.
• [4]SUPPORT Study Principal Investigators: A controlled trial to improve care for seriously ill hospitalized patients: the Study to Understand Prognoses and Preferences for Outcomes and Risks of Treatments (SUPPORT). JAMA 1995, 274:1591-1598.
• [5]Institute of Medicine (US) Committee on Advancing Pain Research, Care, and Education: Relieving pain in America: a blueprint for transforming prevention, care, education, and research. Washington (DC): National Academies Press (US); 2011.
• [6]Michna E, Ross EL, Hynes WL, Nedeljkovic SS, Soumekh S, Janfaza D, Palombi D, Jamison RN: Predicting aberrant drug behavior in patients treated for chronic pain: importance of abuse history. J Pain Symptom Manage 2004, 28:250-258.
• [7]Butler SF, Budman SH, Fernandez KC, Houle B, Benoit C, Katz N, Jamison RN: Development and validation of the Current Opioid Misuse Measure. Pain 2007, 130:144-156.
• [8]Friedman R, Li V, Mehrotra D: Treating pain patients at risk: evaluation of a screening tool in opioid-treated pain patients with and without addiction. Pain Med 2003, 4:182-185.
• [9]Butler SF, Budman SH, Fanciullo GJ, Jamison RN: Cross validation of the Current Opioid Misuse Measure (COMM) to monitor chronic pain patients on opioid therapy. Clin J Pain 2010, 26:770-776.
• [10]Carpenter JS, Andrykowski MA, Wilson J, Hall LA, Ravens MK, Sachs B, Cunningham LL: Psychometrics for two short forms of the Center for Epidemiologic Studies-Depression Scale. Issues Ment Health Nurs 1998, 19:481-494.
• [11]Adams LM, Gale D: Solving the quandary between questionnaire length and response rate in educational research. Res High Educ 1982, 17:231-240.
• [12]Herzog AR, Bachman JG: Effects of questionnaire length on response quality. Public Opin Quart 1981, 45:549-559.
• [13]Kohout FJ, Berkman LF, Evans DA, Cornoni-Huntley J: Two shorter forms of the CES-D depression symptoms index. J Aging Health 1993, 5:179-193.
• [14]Smits N, Zitman FG, Cuijpers P, den Hollander-Gijsman ME, Carlier IVE: A proof of principle for using adaptive testing in routine outcome monitoring: the efficiency of the mood and anxiety symptoms questionnaire – Anhedonic depression CAT. BMC Med Res Methodol 2012, 12:4.
• [15]Chien TW, Wang WC, Huang SY, Lai WP, Chow JC: A web-based computerized adaptive testing (CAT) to assess patient perception in hospitalization. J Med Internet Res 2011, 13:e61.
• [16]Hung M, Nickisch F, Beals TC, Greene T, Clegg DO, Saltzman CL: New paradigm for patient-reported outcomes assessment in foot & ankle research: computerized adaptive testing. Foot Ankle Int 2012, 33:621-626.
• [17]Vogels AG, Jacobusse GW, Reijneveld SA: An accurate and efficient identification of children with psychosocial problems by means of computerized adaptive testing. BMC Med Res Methodol 2011, 11:111.
• [18]Finkelman MD, He Y, Kim W, Lai AM: Stochastic curtailment of health questionnaires: a method to reduce respondent burden. Stat Med 2011, 30:1989-2004.
• [19]Finkelman MD, Smits N, Kim W, Riley B: Curtailment and stochastic curtailment to shorten the CES-D. Appl Psych Meas 2012, 36:632-658.
• [20]Compton P, Darakjian J, Miotto K: Screening for addiction in patients with chronic pain and “problematic” substance use: evaluation of a pilot assessment tool. J Pain Symptom Manage 1998, 16:355-363.
• [21]Choi SW, Reise SP, Pilkonis PA, Hays RD, Cella D: Efficiency of static and computer adaptive short forms compared to full-length measures of depressive symptoms. Qual Life Res 2010, 19:125-136.
• [22]Fries JF, Cella D, Rose M, Krishnan E, Bruce B: Progress in assessing physical function in arthritis: PROMIS short forms and computerized adaptive testing. J Rheumatol 2009, 36:2061-2066.
• [23]Reeve BB, Hays RD, Bjorner JB, Cook KF, Crane PK, Teresi JA, Thissen D, Revicki DA, Weiss DJ, Hambleton RK, Liu H, Gershon R, Reise SP, Lai JS, Cella D, PROMIS Cooperative Group: Psychometric evaluation and calibration of health-related quality of life item banks: plans for the Patient-Reported Outcomes Measurement Information System (PROMIS). Med Care 2007, 45:S22-S31.
• [24]Eisenberg B, Ghosh BK: Curtailed and uniformly most powerful sequential tests. Ann Stat 1980, 8:1123-1131.
• [25]Eisenberg B, Simons G: On weak admissibility of tests. Ann Stat 1978, 6:319-332.
• [26]Davis BR, Hardy RJ: Data monitoring in clinical trials: the case for stochastic curtailment. J Clin Epidemiol 1994, 47:1033-1042.
• [27]Lan KKG, Simon R, Halperin M: Stochastically curtailed tests in long-term clinical trials. Commun Stat Sequential Anal 1982, 1:207-219.
• [28]Hastie T, Tibshirani R, Friedman J: The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Second Edition). New York: Springer; 2009.
• [29]Cella D, Gershon R, Lai J-S, Choi S: The future of outcomes measurement: item banking, tailored short-forms, and computerized adaptive assessment. Qual Life Res 2007, 16(Suppl 1):133-141.
• [30]Finkelman MD, Kim W, He Y, Lai AM: Item ordering in stochastically curtailed health questionnaires with an observable outcome. J Comp Adapt Test 2013, 1:38-66.