Swipe om te navigeren naar een ander artikel
To investigate the role of symptom clusters in the analysis and utilisation of patient reported outcome measures (PROMs) for data modelling and clinical practice. To compare symptom clusters with scales, and to explore their value in PROMs interpretation and symptom management.
A dataset called RT01 (ISCRTN47772397) of 843 prostate cancer patients was used. PROMs were reported with the University of California, Los Angeles Prostate Cancer Index (UCLA-PCI). Symptom clusters were explored with hierarchical cluster analysis (HCA) and average linkage method (correlation > 0.6). The reliability of the Urinary Function Scale was evaluated with Cronbach’s Alpha. The strength of the relationship between the items was investigated with Spearman’s correlation. Predictive accuracy of the clusters was compared to the scales by receiver operating characteristic (ROC) analysis. Presence of urinary symptoms at 3 years measured with the late effects on normal tissue: subjective, objective, management tool (LENT/SOM) was an endpoint.
Two symptom clusters were identified (urinary cluster and sexual cluster). The grouping of symptom clusters was different than UCLA-PCI Scales. Two items of the urinary function scales (“number of pads” and “urinary leak interfering with sex”) were excluded from the urinary cluster. The correlation with the other items in the scale ranged from 0.20 to 0.21 and 0.31 to 0.39, respectively. Cronbach’s Alpha showed low correlation of those items with the Urinary Function Scale (0.14–0.36 and 0.33–0.44, respectively). All urinary function scale items were subject to a ceiling effect. Clusters had better predictive accuracy, AUC = 0.70 –0.65, while scales AUC = 0.67–0.61.
This study adds to the knowledge on how cluster analysis can be applied for the interpretation and utilisation of PROMs. We conclude that multiple-item scales should be evaluated and that symptom clusters provide a study-specific approach for modelling and interpretation of PROMs.
Cancer Research UK. (2012). Prostate cancer survival statistics. http://www.cancerresearchuk.org/health-professional/cancer-statistics/statistics-by-cancer-type/prostate-cancer/survival. Accessed September 09, 2016.
Horwitz, E. M., Bae, K., Hanks, G. E., Porter, A., Grignon, D. J., Brereton, H. D., et al. (2008). Ten-year follow-up of radiation therapy oncology group protocol 92–02: A phase III trial of the duration of elective androgen deprivation in locally advanced prostate cancer. Journal of Clinical Oncology, 26(15), 2497–2504. CrossRefPubMed
Macmillan Cancer Support. (2013). Throwing light on the consequences of cancer and its treatment. http://www.macmillan.org.uk/documents/aboutus/research/researchandevaluationreports/throwinglightontheconsequencesofcanceranditstreatment.pdf. Accessed September 09, 2016.
Litwin, M. S. (1994). UCLA-PCI including the RAND SF-36 v2 health-related quality of life scoring instructions. https://eprovide.mapi-trust.org/instruments/ucla-prostate-cancer-index/scoring. Accessed March 23, 2017.
Budaus, L., Huland, H., & Graefen, M. (2012). Controversies in the management of localized prostate cancer: Radical prostatectomy still the standard-of-care. Critical Reviews in Oncology Hematology, 84(1), 25.
Barnett, G. C., De Meerleer, G., Gulliford, S. L., Sydes, M. R., Elliott, R. M., & Dearnaley, D. P. (2011). The impact of clinical factors on the development of late radiation toxicity: Results from the Medical Research Council RT01 trial (ISRCTN47772397). Clinical Oncology, 23(9), 613–624. CrossRefPubMed
Litwin, M. S., Pasta, D. J., Yu, J., Stoddard, M. L., & Flanders, S. C. (2000). Urinary function and bother after radical prostatectomy or radiation for prostate cancer: A longitudinal, multivariate quality of life analysis from the cancer of the prostate strategic urologic research endeavor. The Journal of urology, 164(6), 1973–1977. CrossRefPubMed
Dodd, M. J., Miaskowski, C., & Paul, S. M. (2001). Symptom clusters and their effect on the functional status of patients with cancer. Oncology Nursing Forum, 28(3), 465–470. PubMed
Aktas, A., Walsh, D., & Hu, B. (2014). Cancer symptom clusters: An exploratory analysis of eight statistical techniques. Journal of Pain and Symptom Management, 48(6), 1254–1266.
Skerman, H. M., Yates, P. M., & Battistutta, D. (2009). Multivariate methods to identify cancer-related symptom clusters. Research in Nursing & Health, 32(3), 345–360. CrossRef
Kim, H. J., & Abraham, I. L. (2008). Statistical approaches to modeling symptom clusters in cancer patients. Cancer Nursing, 31(5), E1–E10. CrossRef
West, C., Azria, D., Chang-Claude, J., Davidson, S., Lambin, P., Rosenstein, B., et al. (2014). The REQUITE project: Validating predictive models and biomarkers of radiotherapy toxicity to reduce side-effects and improve quality of life in cancer survivors. Clinical Oncology (Royal College of Radiologists), 26(12), 739–742. CrossRef
Sydes, M. R., Stephens, R. J., Moore, A. R., Aird, E. G., Bidmead, A. M., Fallowfield, L. J., et al. (2004). Implementing the UK Medical Research Council (MRC) RT01 trial (ISRCTN 47772397): Methods and practicalities of a randomised controlled trial of conformal radiotherapy in men with localised prostate cancer. Radiotherapy and Oncology, 72(2), 199–211. CrossRefPubMed
Mayles, W. P., Moore, A. R., Aird, E. G., Bidmead, A. M., Dearnaley, D. P., Griffiths, S. E., et al. (2004). Questionnaire based quality assurance for the RT01 trial of dose escalation in conformal radiotherapy for prostate cancer (ISRCTN 47772397). Radiotherapy and Oncology: Journal of the European Society for Therapeutic Radiology and Oncology, 73(2), 199–207. CrossRef
Dearnaley, D. P., Sydes, M. R., Langley, R. E., Graham, J. D., Huddart, R. A., Syndikus, I., et al. (2007). The early toxicity of escalated versus standard dose conformal radiotherapy with neo-adjuvant androgen suppression for patients with localised prostate cancer: results from the MRC RT01 trial (ISRCTN47772397). Radiotherapy and oncology, 83(1), 31–41. CrossRefPubMed
Syndikus, I., Morgan, R. C., Sydes, M. R., Graham, J. D. & Dearnaley, D. P. (2010). Late gastrointestinal toxicity after dose-escalated conformal radiotherapy for early prostate cancer: Results from the UK Medical Research Council RT01 trial (ISRCTN47772397). International Journal of Radiation Oncology* Biology* Physics, 77(3), 773–783. CrossRef
LENT SOMA tables (1995). Radiotherapy and Oncology, 35(1), 17–60. CrossRef
Zelefsky, M. J., Levin, E. J., Hunt, M., Yamada, Y., Shippy, A. M., Jackson, A., et al. (2008). Incidence of late rectal and urinary toxicities after three-dimensional conformal radiotherapy and intensity-modulated radiotherapy for localized prostate cancer. International Journal of Radiation Oncology* Biology* Physics, 70(4), 1124–1129. CrossRef
Denham, J. W., & Hauer-Jensen, M. (2002). The radiotherapeutic injury—a complex ‘wound’. Radiother Oncology, 63(2), 129–145. CrossRef
Little, R. J. A.,& Rubin, D. B. (2002). Statistical analysis with missing data. New York: Wiley. CrossRef
McKnight, P.E., McKnight, K. M., Sidani, S., Figueredo, A. J. (2007). Missing data: A gentle introduction, New York: Guliford Press.
Molenberghs, G., Kenward, M. (2007). Missing data in clinical studies. New York: Wiley. CrossRef
Allison, P. D. (2000). Multiple imputation for missing data. A cautionary tale. Sociol Methods Research, 28(3), 301–309. CrossRef
Horton, N. J., & Lipsitz, S. R. (2001). Multiple imputation in practice. Comparison of software packages for regression models with missing variables. The American Statistician, 55(3), 244–254. CrossRef
Fulekar, M. H. (2009). Bioinformatics: Applications in life and environmental sciences. New Delhi: Cappital Publishing Company. CrossRef
Corey, D. M., Dunlap, W. P., & Burke, M. J. (1998). Averaging correlations: Expected values and bias in combined pearson rs and Fisher’s z transformations. The Journal of General Psychology, 125(3), 245–261. CrossRef
Richard, L., & Gorsuch, C. S. L. (2010). Correlation coefficients: Mean bias and confidence interval distortions. Journal of Methods and Measurement in the Social Sciences, 1(2).
Cronbach, L. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334. CrossRef
Krabbe, P. F. M. (2017). The measurement of health and health status: Concepts, methods and applications from a multidisciplinary Perspective, (1st Edn., pp. 135–150). San Diego: Nikki Levy.
Woodhouse, B., & Jackson, P. H. (1977). Lower bounds for the reliability of the total score on a test composed of non-homogeneous items: II: A search procedure to locate the greatest lower bound. Psychometrika, 42(4), 579–591. CrossRef
Sijtsma, K. (2009). On the use, the misuse, and the very limited usefulness of Cronbach’s Alpha. Psychometrika, 74(1).
Nunnally, J. C. (1978). Psychometric theory. New York: McGraw-Hill.
Lim, C. R., Harris, K., Dawson, J., Beard, D. J., Fitzpatrick, R., & Price, A. J. (2015). Floor and ceiling effects in the OHS: An analysis of the NHS PROMs data set. BMJ Open, 5(7).
Cobb, J., Collins, R., Manning, V., Zannotto, M., Moore, E. & Jones, G. (2016). Avoiding the ceiling effect of PROMs: a patient-centred outcome measure correlates with objective differences in gait that are undetectable using the oxford hip score. Orthopaedic Proceedings, 98-B(SUPPl 1), 89–89.
Hamilton, D. F., Giesinger, J. M., MacDonald, D. J., Simpson, A. H. R. W., Howie, C. R., & Giesinger, K. (2016). Responsiveness and ceiling effects of the forgotten joint score-12 following total hip arthroplasty. Bone & Joint Research, 5(3), 87–91. CrossRef
Lemanska, A., Cox, A., Kirkby, N. F., Chen, T. & Faithfull, S. (2014). Predictive modelling of patient reported radiotherapy-related toxicity by the application of symptom clustering and auto regression. International Journal of Statistics in Medical Research, 3(4), 412–422. CrossRef
- Symptom clusters for revising scale membership in the analysis of prostate cancer patient reported outcome measures: a secondary data analysis of the Medical Research Council RT01 trial (ISCRTN47772397)
David. P. Dearnaley
Matthew. R. Sydes
- Springer International Publishing