Skip to main content Accessibility help
×
Hostname: page-component-78c5997874-lj6df Total loading time: 0 Render date: 2024-11-01T21:10:29.441Z Has data issue: false hasContentIssue false

25 - Bayesian adaptive design: a novel approach to test the effectiveness of symptom-reducing agents using patient-reported outcomes

from Section 4 - Symptom Measurement

Published online by Cambridge University Press:  05 August 2011

Valen E. Johnson
Affiliation:
The University of Texas M. D. Anderson Cancer Center
Tito R. Mendoza
Affiliation:
The University of Texas M. D. Anderson Cancer Center
Charles S. Cleeland
Affiliation:
University of Texas, M. D. Anderson Cancer Center
Michael J. Fisch
Affiliation:
University of Texas, M. D. Anderson Cancer Center
Adrian J. Dunn
Affiliation:
University of Hawaii, Manoa
Get access

Summary

Better symptom management, in cancer as well as in other diseases, has been hampered by the lack of a strong clinical-trial evidence base for guiding symptom management practice. The 2001 Institute of Medicine report Improving Palliative Care for Cancer reviewed the paucity of clinical research that might present a basis for evidence-based symptom management. A 2003 NIH State of the Science review concluded that symptoms, especially pain, fatigue, and depression, were undermanaged in cancer care and recommended an increased effort to develop evidence that would support the rational use of both biological and behavioral interventions for symptom management.

Various barriers have hindered the development of evidence-based methods for controlling treatment-related symptom burden, despite the availability of adequate symptom measurement methods. For example, the control of treatment-related symptoms almost always involves the use of combined treatment modalities, which are difficult to evaluate using traditional randomized clinical trial methods, where single symptoms are typically managed with single agents (eg, pain controlled with a single analgesic). When clinicians do treat multiple symptoms, they are likely to prescribe multiple agents based on anecdotal experience or the patient's perceived needs, rather than on evidence-based research. Further, many of the agents that might be effective in the control of treatment-related symptom burden are generic or off-patent drugs that will never receive clinical research support from the pharmaceutical industry because there is no financial incentive to support clinical trials testing their effectiveness for symptom control.

Type
Chapter
Information
Cancer Symptom Science
Measurement, Mechanisms, and Management
, pp. 293 - 303
Publisher: Cambridge University Press
Print publication year: 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Foley, KM, Gelband, H. Improving Palliative Care for Cancer. Washington DC: National Academy Press, 2001.Google Scholar
,National Institutes of Health. Symptom management in cancer: pain, depression and fatigue: State-of-the-Science Conference Statement. J Pain Palliat Care Pharmacother 17(1):77–97, 2003.Google Scholar
Thall, PF, Wathen, JK. Practical Bayesian adaptive randomisation in clinical trials. Eur J Cancer 43(5):859–866, 2007.CrossRefGoogle ScholarPubMed
Spiegelhalter, DJ, Abrams, KR, Myles, JP. Bayesian Approaches to Clinical Trials and Health Care Evaluation. Chichester: Wiley, 2004.Google Scholar
,US Food and Drug Administration, Center for Devices and Radiological Health. Draft guidance for industry and FDA staff. Guidance for the use of Bayesian statistics in medical device clinical trials. Available from: URL: http://www.fda.gov/downloads/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocuments/ucm071121.pdf. Accessed Jun 9, 2009.Google Scholar
Biswas, S, Liu, DD, Lee, JJ, Berry, DA. Bayesian clinical trials at the University of Texas M. D. Anderson Cancer Center. Clin Trials 6(3):205–216, 2009.Google ScholarPubMed
Ji, Y, Bekele, BN. Adaptive randomization for multiarm comparative clinical trials based on joint efficacy/toxicity outcomes. Biometrics:e-pub ahead of print, 2009.CrossRefGoogle ScholarPubMed
Krams, M, Lees, KR, Hacke, W, Grieve, AP, Orgogozo, JM, Ford, GA. Acute Stroke Therapy by Inhibition of Neutrophils (ASTIN): an adaptive dose-response study of UK-279,276 in acute ischemic stroke. Stroke 34(11):2543–2548, 2003.CrossRefGoogle ScholarPubMed
Cleeland, CS, Mendoza, TR, Wang, XS, et al. Assessing symptom distress in cancer patients: the M. D. Anderson Symptom Inventory. Cancer 89(7):1634–1646, 2000.3.0.CO;2-V>CrossRefGoogle Scholar
Rosenthal, DI, Mendoza, TR, Chambers, MS, et al. Measuring head and neck cancer symptom burden: the development and validation of the M. D. Anderson Symptom Inventory, head and neck module. Head Neck 29(10): 923–931, 2007.CrossRefGoogle Scholar
Rosenthal, DI, Mendoza, TR, Chambers, MS, et al. The M. D. Anderson symptom inventory-head and neck module, a patient-reported outcome instrument, accurately predicts the severity of radiation-induced mucositis. Int J Radiat Oncol Biol Phys 72(5): 1355–1361, 2008.CrossRefGoogle Scholar
Berry, DA, Fristedt, B. Bandit Problems: Sequential Allocation of Experiments (Monographs on Statistics and Applied Probability). London: Chapman and Hall, 1985.CrossRefGoogle Scholar
Maki, RG, Wathen, JK, Patel, SR, et al. Randomized phase II study of gemcitabine and docetaxel compared with gemcitabine alone in patients with metastatic soft tissue sarcomas: results of sarcoma alliance for research through collaboration study 002 [corrected]. J Clin Oncol 25(19):2755–2763, 2007.CrossRefGoogle Scholar
Giles, FJ, Kantarjian, HM, Cortes, JE, et al. Adaptive randomized study of idarubicin and cytarabine versus troxacitabine and cytarabine versus troxacitabine and idarubicin in untreated patients 50 years or older with adverse karyotype acute myeloid leukemia. J Clin Oncol 21(9):1722–1727, 2003.CrossRefGoogle ScholarPubMed
Kantarjian, H, Oki, Y, Garcia-Manero, G, et al. Results of a randomized study of 3 schedules of low-dose decitabine in higher-risk myelodysplastic syndrome and chronic myelomonocytic leukemia. Blood 109(1):52–57, 2007.CrossRefGoogle ScholarPubMed
Zhou, X, Liu, S, Kim, ES, Herbst, RS, Lee, JJ. Bayesian adaptive design for targeted therapy development in lung cancer – a step toward personalized medicine. Clin Trials 5(3):181–193, 2008.CrossRefGoogle ScholarPubMed
Huang, X, Ning, J, Li, Y, Estey, E, Issa, JP, Berry, DA. Using short-term response information to facilitate adaptive randomization for survival clinical trials. Stat Med 28(12):1680–1689, 2009.CrossRefGoogle ScholarPubMed

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×