Format

Send to

Choose Destination
Eur J Cancer. 2012 May;48(7):1038-47. doi: 10.1016/j.ejca.2012.02.058. Epub 2012 Mar 14.

Evaluation of treatment options for patients with advanced renal cell carcinoma: assessment of appropriateness, using the validated semi-quantitative RAND corporation/University of California, Los Angeles methodology.

Author information

1
Department of Oncology, The Royal Marsden Hospital, London, UK. martin.gore@rmh.nhs.uk

Abstract

A diverse range of treatment options and interventions are available for the management of renal cell carcinoma (RCC), allowing clinicians to tailor therapy to best meet their patient's needs and situation. However, choosing from the plethora of options can be problematic. RCC treatment guidelines advise on the most efficacious agents based upon specific clinical trial populations, but these do not always take into account all the patient factors that influence the suitability of treatment options for individual patients. This study used the validated RAND/UCLA (RAND corporation/University of California, Los Angeles) 'appropriateness methodology' to integrate clinical efficacy data with expert opinion concerning the use of specific RCC treatment options for particular patient scenarios, in an attempt to facilitate the widespread implementation of patient-focussed treatment choices. Use of the methodology has allowed us to develop treatment algorithms for patients with locally-advanced RCC and for those with metastatic disease post-nephrectomy or with primary tumour in situ. The algorithms take into account patient-specific characteristics such as tumour histology, prior treatment and known risk factors to advise whether a particular treatment intervention is appropriate, not appropriate or of uncertain appropriateness. Use of this methodology aims to develop a formalised process by which expert opinion can be integrated with clinical data and used as an additional source of information that can provide further guidance concerning difficult treatment decisions when data are absent or sparse.

PMID:
22425264
DOI:
10.1016/j.ejca.2012.02.058
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center