| Literature DB >> 27245079 |
Puey Ling Chia1, Craig Gedye2, Paul C Boutros2, Paul Wheatley-Price2, Thomas John2.
Abstract
Although the measurements of clinical outcomes for cancer treatments have become diverse and complex, there remains a need for clear, easily interpreted representations of patients' experiences. With oncology trials increasingly reporting non-time-to-event outcomes, data visualization has evolved to incorporate parameters such as responses to therapy, duration and degree of response, and novel representations of underlying tumor biology. We review both commonly used and newly developed methods to display outcomes in oncology, with a focus on those that have evolved to represent complex datasets.Entities:
Mesh:
Year: 2016 PMID: 27245079 PMCID: PMC5017943 DOI: 10.1093/jnci/djw031
Source DB: PubMed Journal: J Natl Cancer Inst ISSN: 0027-8874 Impact factor: 13.506