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Jeremy D. Kratz, Nataliya V. Uboha, Sam J. Lubner, Daniel L. Mulkerin, Linda Clipson, Yanyao Yi, Menggang Yu, Kristina A. Matkowskyj, Noelle K. LoConte, and Dustin A. Deming

using the Breslow method. Results Sidedness Predicts Outcomes to Anti-EGFR Therapy in the Treatment-Refractory Setting A total of 62 patients were available for analysis, with baseline characteristics stratified by primary tumor sidedness

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Mostafa R. Mohamed, Erika Ramsdale, Kah Poh Loh, Huiwen Xu, Amita Patil, Nikesha Gilmore, Spencer Obrecht, Megan Wells, Ginah Nightingale, Katherine M. Juba, Bryan Faller, Adedayo Onitilo, Thomas Bradley, Eva Culakova, Holly Holmes, and Supriya G. Mohile

the potential for collinearity in multivariate analyses, cognitive impairment was not included as a separate variable in our models. Statistical Approach For each of the 3 outcome measures, we used the receiver operating characteristic (ROC) curve

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Siew Tzuh Tang, Jen-Shi Chen, Wen-Chi Chou, Wen-Cheng Chang, Chiao-En Wu, Chia-Hsun Hsieh, Ming-Chu Chiang, and Mei-Ling Kuo

committee approved the research protocol. All participants signed informed consents. Measures Data were collected on anxiety symptoms and 5 groups of variables (demographics, disease-related characteristics, disease burden, perceived burden to others

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Carme Font, Alberto Carmona-Bayonas, Aranzazu Fernández-Martinez, Carmen Beato, Andrés Vargas, Pere Gascon, and Remedios Otero

negative predictive values (PPV and NPV, respectively); and positive and negative likelihood ratios (pLR and nLR, respectively). The receiver operating characteristic (ROC) curves were calculated, and the method proposed by DeLong et al 23 was used to test

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Catherine Lockhart, Cara McDermott, James Marshall, Aaron Mendelsohn, Pamala Pawloski, and Jeffrey Brown

. Objective: To evaluate patient characteristics and treatment use patterns in patients treated with GCSFs. This analysis will help inform a large-scale, real-world, observational GCSF comparative effectiveness research (CER) study using the Biologics

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Xiaoqin Yang, Kaushal Desai, Neha Agrawal, Kirti Mirchandani, Sagnik Chatterjee, Eric Sarpong, and Shuvayu Sen

describe the characteristics, treatment patterns, healthcare resource use (HRU) and costs for these patients. ​ Methods: A retrospective study of individuals enrolled in the MarketScan ® Commercial Claims and Encounters claims database from 10

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Sumesh Kachroo, Changxia Shao, Kaushal Desai, Jinghua He, Fan Jin, and Shuvayu Sen

Background: This study evaluated the relationship between patients’ clinical and genomic characteristics and high tumor mutational burden (TMB) in the context of small cell lung cancer (SCLC). Methods: This was a retrospective cohort study using

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Mary E. Charlton, Amanda R. Kahl, Alissa A. Greenbaum, Jordan J. Karlitz, Chi Lin, Charles F. Lynch, and Vivien W. Chen

, insurance status, marital status, and residence in metropolitan versus nonmetropolitan/rural area. Tumor characteristics included location (right-sided = cecum, ascending colon, hepatic flexure, transverse; vs left-sided = splenic flexure, descending colon

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Alyson Haslam, Jennifer Gill, and Vinay Prasad

anecdotally described, 5 , 6 but in light of the increasing rate of publication in these types of studies, we seek to describe the characteristics of a systematic sampling of NI studies in oncology from an updated search of recent published oncology trials

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Ning Ning, Jingsheng Yan, Xian-Jin Xie, and David E. Gerber

following characteristics: year, disease under study, phase and type (interventional/noninterventional), and sponsor type (institutional/industrial). Institutional trials included investigator-initiated trials with a local study chair or a study chair at