subtypes. 14 Decisions about treatment are increasingly being tailored to individual patient characteristics, such as tumor subtype. This clinical classification identifies targets with established data on treatment efficacy; hormonal therapies for HR
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Davinia S.E. Seah, Ines Vaz Luis, Erin Macrae, Jessica Sohl, Georgia Litsas, Eric P. Winer, Nancy U. Lin, and Harold J. Burstein
Lindsay J. Collin, Ming Yan, Renjian Jiang, Keerthi Gogineni, Preeti Subhedar, Kevin C. Ward, Jeffrey M. Switchenko, Joseph Lipscomb, Jasmine Miller-Kleinhenz, Mylin A. Torres, Jolinta Lin, and Lauren E. McCullough
cancer outcomes. 10 – 13 Nonadherence to guidelines could arise from multiple factors, including structural racism, barriers to access, tumor and patient characteristics, or clinician and patient preferences. 11 Therefore, nonadherence to clinical
Eva Battaglini, David Goldstein, Peter Grimison, Susan McCullough, Phil Mendoza-Jones, and Susanna B. Park
were calculated for demographic and clinical characteristics. For the comparison of respondents reporting current CIPN symptoms to those without symptoms, t tests were used for continuous variables and chi-square tests were used for categorical
James Sun, Brittany J. Mathias, Christine Laronga, Weihong Sun, Jun-Min Zhou, William J. Fulp, John V. Kiluk, and M. Catherine Lee
%) had additional nodal disease. Table 1. Demographic and Clinicopathologic Characteristics In terms of adjuvant therapy, 176 (53%) of 329 patients received RT, 245 (75%) received chemotherapy, and 272 (83%) received endocrine therapy ( Table 1 ). Records
Zhong Ye, Chun Wang, Limin Guo, Juan P. Palazzo, Zhixing Han, Yinzhi Lai, Jing Jiang, James A. Posey, Atrayee Basu Mallick, Bingshan Li, Li Jiang, and Hushan Yang
characteristic (ROC) curves and calculating the area under the curve (AUC). The prediction power was also estimated using Concordance-index (C-index), which works as an extension of AUC to the case of censored survival data. Patients with CRC were divided into
Daniëlle D. Huijts, Onno R. Guicherit, Jan Willem T. Dekker, Julia T. van Groningen, Leti van Bodegom-Vos, Esther Bastiaannet, Johannes A. Govaert, Michel W. Wouters, and Perla J. Marang-van de Mheen
considered colon and rectal cancer together, whereas patient characteristics, complication risks, and clinical outcomes are different in colon versus rectal cancer. 10 Furthermore, to provide clues for possible quality improvement, other outcomes require
Barbara Dull, Andrew Linkugel, Julie A. Margenthaler, and Amy E. Cyr
recorded, as were demographic data and tumor characteristics, such as pathology size and tumor marker profile. Statistical analysis was performed using Fisher exact test and Student unpaired t -test, with a P value <.05 being considered statistically
Jeremy P. Harris, Mehr Kashyap, Jessi N. Humphreys, Daniel T. Chang, and Erqi L. Pollom
additional treatments. Demographic and Clinical Characteristics Using data from the SEER registry, we determined age, sex, race, Hispanic ethnicity, marital status, area income, cancer stage, and disease subsite. 13 Dual Medicaid insurance and Charlson
Owen Tan, Deborah J. Schofield, and Rupendra Shrestha
Patient Characteristics Hospital and ED Costs Hospital treatment costs were estimated using Australian Refined Diagnosis Related Groups (AR-DRGs) and episode of care LoS. Cost estimates were obtained from the National Hospital Cost Data
Nina N. Sanford, David J. Sher, Xiaohan Xu, Chul Ahn, Anthony V. D’Amico, Ayal A. Aizer, and Brandon A. Mahal
Southwestern Medical Center deemed the study exempt given the use of public deidentified data. Statistical Analysis Baseline Characteristics Baseline characteristics, including demographic and socioeconomic variables, were reported for the entire cohort (N=34
