Impact factor and citation metrics in phase III cancer trials.

Journal impact factor (IF) is often used to measure research quality and importance. We assessed trial factors associated with the publication of cancer trials in journals with higher IF and publications receiving higher citations. Cancer-specific phase III RCTs were screened through https://clinicaltrials.gov. We identified trials with published primary endpoints, along with their corresponding journal IF and relative citation ratio (RCR). Seven-hundred ninety manuscripts were included in our study. Trials that met their primary endpoint were more commonly published in journals with higher IF (Median IF: positive trials 35.4 vs. negative trials 26.3, Positive trials are commonly published in journals with high IF, but do not necessarily lead to increased citations. Moreover, trials published in journals with higher IF are more likely to receive increased citations.

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Copyright: © 2021 Abi Jaoude et al.

Déclaration de conflit d'intérêts

CONFLICTS OF INTEREST Dr. Fuller received/receives funding and salary support unrelated to this project during the period of study execution from: the National Institutes of Health (NIH) National Institute of Biomedical Imaging and Bioengineering (NIBIB) Research Education Programs for Residents and Clinical Fellows Grant (R25EB025787-01); the National Institute for Dental and Craniofacial Research Establishing Outcome Measures Award (1R01DE025248/R56DE025248) and Academic Industrial Partnership Grant (R01DE028290); NCI Early Phase Clinical Trials in Imaging and Image-Guided Interventions Program (1R01CA218148); an NIH/NCI Cancer Center Support Grant (CCSG) Pilot Research Program Award from the UT MD Anderson CCSG Radiation Oncology and Cancer Imaging Program (P30CA016672); an NIH/NCI Head and Neck Specialized Programs of Research Excellence (SPORE) Developmental Research Program Award (P50 CA097007); NIH Big Data to Knowledge (BD2K) Program of the National Cancer Institute (NCI) Early Stage Development of Technologies in Biomedical Computing, Informatics, and Big Data Science Award (1R01CA2148250; National Science Foundation (NSF), Division of Mathematical Sciences, Joint NIH/NSF Initiative on Quantitative Approaches to Biomedical Big Data (QuBBD) Grant (NSF 1557679); NSF Division of Civil, Mechanical, and Manufacturing Innovation (CMMI) grant (NSF 1933369); and the Sabin Family Foundation. Direct infrastructure support is provided to Dr. Fuller by the multidisciplinary Stiefel Oropharyngeal Research Fund of the University of Texas MD Anderson Cancer Center Charles and Daneen Stiefel Center for Head and Neck Cancer and the Cancer Center Support Grant (P30CA016672) and the MD Anderson Program in Image-guided Cancer Therapy. Dr. Fuller has received direct industry grant support, honoraria, and travel funding from Elekta AB unrelated to this project. Dr. Taniguchi is supported by funding from NIH under award R01CA227517-01A1, Cancer Prevention & Research Institute of Texas (CPRIT) grant RR140012, V Foundation (V2015-22), the Kimmel Foundation, Sabin Family Foundation Fellowship, and the McNair Foundation. All other authors report no financial disclosures or conflicts of interests related to this work.