PROMIS depression measures perform similarly to legacy measures relative to a structured diagnostic interview for depression in cancer patients
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To assess the convergent validity of the Patient-Reported Outcomes Measurement Information System (PROMIS) depression measures relative to legacy measures and criterion validity against a structured diagnostic interview for depression in an oncology sample.
132 oncology/haematology outpatients completed the PROMIS Depression Computer Adaptive Test (PROMIS-D-CAT) and PROMIS Depression Short Form (PROMIS-D-SF) along with seven legacy measures: Beck Depression Inventory (BDI); Centre for Epidemiological Studies Depression (CES-D); Depression, Anxiety and Stress Scale; Hospital Anxiety and Depression Scale; Patient Health Questionnaire; Distress Thermometer and PSYCH-6. Correlations, area under the curve (AUC) and diagnostic accuracy statistics were calculated with Structured Clinical Interview as the gold standard.
Both PROMIS measures correlated with all legacy measures at p < .001 (ρ = 0.589–0.810) and all AUCs (> 0.800) were comparable. At the cut-off points for mild depression of 53, the PROMIS measures had sensitivity (0.83 for PROMIS-D-CAT and 0.80 for PROMIS-D-SF) similar to or better than 6/7 legacy measures with high negative predictive value (> 90%). At cut-off points of 60 for moderate depression, PROMIS measures had specificity > 90%, similar to or better than all legacy measures and positive predictive value ≥ 0.50 (similar to 5/7 legacy measures).
The convergent and criterion validity of the PROMIS depression measures in cancer populations was confirmed, although the optimal cut-off points are not established. PROMIS measures were briefer than BDI-II and CES-D but do not offer any advance in terms of diagnostic accuracy, reduced response burden or cost over other legacy measures of depression in oncology patients.
KeywordsPsycho-oncology Depression Questionnaire development Cancer
We would like to thank our Psychologist interviewers and all of the participants who gave so generously of their time. Thanks also to Jessica Searston for assistance with manuscript preparation.
This study was funded by Calvary Mater Newcastle (Grant Number 11-09) and the Centre for Translational Neuroscience and Mental Health of the University of Newcastle (Australia) provided funding for statistical analysis. Professor King is supported by the Australian Government through Cancer Australia. Dr Lambert was initially supported by a National Health and Medical Research Council Research Fellowship (APP1012869) during data collection and by an FRQS Junior 1 Research Scholar Award subsequently.
Compliance with ethical standards
Conflict of interest
Kerrie Clover declares that she has no conflict of interest. Sylvie D. Lambert declares that she has no conflict of interest. Christopher Oldmeadow declares that he has no conflict of interest. Madeleine T. King declares that she has no conflict of interest Benjamin Britton declares that he has no conflict of interest. Alex J Mitchell declares that he has no conflict of interest. Gregory L. Carter declares that he has no conflict of interest.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or National Research Committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
Informed consent was obtained from all individual participants included in the study.
- 1.Institute of Medicine Report 2008, (IOM) (2008). Cancer care for the whole patient: Meeting psychosocial health needs. The National Academies Press: Washington, DC.Google Scholar
- 4.American College of Surgeons Commission on Cancer. Cancer Program Standards 2012 Version 1.1: Ensuring patient-centered care. Available at: http://www.facs.org/cancer/coc/programstandards2012.html. Accessed March 2017.
- 6.Lambert, S. D., Clover, K., Pallant, J. F., Britton, B., King, M. T., Mitchell, A. J., & Carter, G. (2015). Making sense of variations in prevalence estimates of depression in cancer: A co-calibration of commonly used depression scales using Rasch Analysis. Journal of the National Comprehensive Cancer Network, 13(10), 1203–1211.CrossRefPubMedGoogle Scholar
- 7.Mitchell, A. J., Meader, N., Davies, E., Clover, K., Carter, G. L., Loscalzo, M. J., et al. (2012). Meta-analysis of screening and case finding tools for depression in cancer: evidence based recommendations for clinical practice on behalf of the Depression in Cancer Care consensus group. Journal of Affective Disorders, 140(2), 149–160.CrossRefPubMedGoogle Scholar
- 8.Cella, D., Riley, W., Stone, A., Rothrock, N., Reeve, B., Yount, S., et al. (2010). Initial adult health item banks and first wave testing of the patient-reported outcomes measurement information system (PROMIS™) network: 2005–2008. Journal of Clinical Epidemiology, 63(11), 1179.CrossRefPubMedPubMedCentralGoogle Scholar
- 11.Cella, D., Choi, S., Garcia, S., Cook, K. F., Rosenbloom, S., Lai, J. S., Tatum, D. S., & Gershon, R. (2014). Setting standards for severity of common symptoms in oncology using the PROMIS item banks and expert judgment. Quality of Life Research, 23(10), 2651–2661.CrossRefPubMedPubMedCentralGoogle Scholar
- 12.Lazenby, M., Ercolano, E., Grant, M., Holland, J. C., Jacobsen, P. B., & McCorkle, R. (2015). Supporting commission on cancer–mandated psychosocial distress screening with implementation strategies. Journal of Oncology Practice, 11(3), e413–e420. https://doi.org/10.1200/JOP.2014.002816.CrossRefPubMedPubMedCentralGoogle Scholar
- 17.Stone, A. A., Broderick, J. E., Junghaenel, D. U., Schneider, S., & Schwartz, J. E. (2016). PROMIS fatigue, pain intensity, pain interference, pain behavior, physical function, depression, anxiety, and anger scales demonstrate ecological validity. Journal of Clinical Epidemiology, 74, 194–206.CrossRefPubMedGoogle Scholar
- 18.Beck, A. T., Steer, R. A., & Brown, G. K. (1996). Manual for The Beck Depression Inventory Second Edition (BDI-II). San Antonio: Psychological Corporation.Google Scholar
- 21.Zigmond, A. S., & Snaith, R. P. (1983). The hospital anxiety and depression scale. Acta Psychiatrica Scandinavica, 67(6), 361–370. https://doi.org/10.1111/j.1600-0447.1983.tb09716.x.CrossRefPubMedGoogle Scholar
- 24.First, M. B., Gibbon, M., Spitzer, R. L., & Williams, J. B. (2002). Structured Clinical Interview for DSM-IV-TR Axis I Disorders: Research Version. Biometrics Research Department, New York State Psychiatric Institute.Google Scholar
- 25.Clover, K. A., Rogers, K., Britton, B., Oldmeadow, C., Attia, J., & Carter, G. L. (2017) Reduced prevalence of pain and distress during four years of screening with QUICATOUCH in Australian oncology patients. European Journal of Cancer Care.Google Scholar
- 26.Dancy, C. P., & Reidy, J. (2004). Statistics without maths for psychology. Harlow: Pearson Education Limited.Google Scholar
- 28.Pilkonis, P. A., Choi, S. W., Reise, S. P., Stover, A. M., Riley, W. T., & Cella, D. (2011). Item banks for measuring emotional distress from the Patient-Reported Outcomes Measurement Information System (PROMIS®): Depression, anxiety, and anger. Assessment, 18(3), 263–283.CrossRefPubMedPubMedCentralGoogle Scholar
- 29.Centre for Epidemiologic Studies. (1970) Centre for Epidemiologic Studies Depression Scale (CES-D). Rockville: National Institute of Mental Health.Google Scholar
- 32.Health Measures (2016). Assessment Centre Pricing Information. http://www.healthmeasures.net/images/LearnMore/Assessment_Center_Pricing_Information_07252016.pdf. Accessed 29 May 2017.