Efficacy and safety of tyrosine kinase inhibitors alone or in combination with radiation therapy for metastatic renal cell carcinoma: A protocol for evidence-based systematic review and Bayesian network meta-analysis. Issue 33 (20th August 2021)

• Record Type: Journal Article
• Authors: Wang, Shihua; Jin, Weihua; Jiang, Changqing; Zhang, Yiwen; Deng, Kaiwen
• Is Part Of: Medicine. Volume 100:Issue 33(2021)
• Page Start: e27004
• Publication Date: 2021-08-20
• Subjects: meta -- metastatic renal cell carcinoma -- protocol -- radiation therapy -- tyrosine kinase inhibitor; Medicine -- Periodicals; Medicine -- Periodicals; Médecine -- Périodiques; Geneeskunde; Medicine; Periodicals; Periodicals; 610.5
• DOI: 10.1097/MD.0000000000027004
• Languages: English
• ISSNs: 0025-7974

Abstract

Abstract: Background: Given the lack of evidence for survival benefit in patients with metastatic renal cell carcinoma from the addition of radiation therapy to tyrosine kinase inhibitor therapy, this Bayesian network meta-analysis aimed to evaluate survival outcomes in patients receiving radiation therapy plus tyrosine kinase inhibitor therapy. Methods: The preferred reporting items for systematic reviews and meta-analyses reporting guidelines were followed to conduct this study. The electronic databases of PubMed, Cochrane Library, EMBASE, and Web of Science were searched from the inception to August 2021. All phase III clinical trials that reported the outcomes of tyrosine kinase inhibitor with radiation therapy compared with those of tyrosine kinase inhibitor or radiation therapy alone for patients with metastatic renal cell carcinoma were considered eligible for inclusion in this meta-analysis. Overall survival as the primary outcome of interest, and adverse events as secondary outcome of interest were recorded for meta-analysis. Results: A Bayesian network meta-analysis is an appropriate statistical method to compare all treatment options by statistically simulating the estimated results of a comprehensive trial, and to compare treatments by common and associated comparators. In addition, Bayesian network meta-analysis can produce ranking probabilities of treatments, which may contribute to clinicians' clinical decision-making.