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Jia-Wei Lv, Yu-Pei Chen, Guan-Qun Zhou, Xu Liu, Ying Guo, Yan-Ping Mao, Jun Ma, and Ying Sun

subsequent publications, and to identify the trends of reporting quality and associated characteristics. Methods Data Source and Study Sample Data were obtained through the Aggregate Analysis of ClinicalTrials.gov (AACT) database, reflecting data

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Ami M. Vyas, Hilary Aroke, and Stephen Kogut

explained portion of the gap is the sum of the differences between HR-positive and HR-negative women in terms of observed characteristics weighted by the pooled regression coefficients. It is calculated by multiplying the differences in the average

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Christopher P. Chung, Carolyn Behrendt, Louise Wong, Sarah Flores, and Joanne E. Mortimer

classified according to the IIQ-7 score as major (33–99), moderate (5–29), or no impact (0). Statistical Analyses For this exploratory study, statistical significance ( P <.05) was unadjusted for multiple hypothesis testing. Demographic characteristics (age

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Matthew P. Banegas, Donna R. Rivera, Maureen C. O’Keeffe-Rosetti, Nikki M. Carroll, Pamala A. Pawloski, David C. Tabano, Mara M. Epstein, Kai Yeung, Mark C. Hornbrook, Christine Lu, and Debra P. Ritzwoller

standards. 22 VTR data were obtained via chart review by trained abstractors and included data associated with health service use and clinical, treatment, and tumor characteristics. The VDW pharmacy files provided data on national drug code

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Samuel Martel, Matteo Lambertini, Dominique Agbor-Tarh, Noam F. Ponde, Andrea Gombos, Vicki Paterson, Florentine Hilbers, Larissa Korde, Anna Manukyants, Amylou Dueck, Christian Maurer, Martine Piccart, Alvaro Moreno-Aspitia, Christine Desmedt, Serena Di Cosimo, and Evandro de Azambuja

original ALTTO trial. 10 Statistical Considerations Patient baseline characteristics were compared with respect to BMI category (underweight, normal weight, overweight, obese) using the chi-square test. Weight change was calculated as a percentage (by

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Heidi Ko, Yaser Baghdadi, Charito Love, and Joseph A. Sparano

for categorical variables. Association Between Clinical Characteristics and Upstaging Frequencies Associations between clinical characteristics and upstaging frequencies were assessed using the Fisher exact test for categorical variables and the

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Benjamin M. Parsons, Dipesh Uprety, Angela L. Smith, Andrew J. Borgert, and Leah L. Dietrich

potential associations between sociodemographic, stage, estrogen receptor (ER)/progesterone receptor (PR) status, and tumor and treatment characteristics using Pearson chi-square or Fisher exact tests. Survival analysis using Kaplan-Meier curves Figure 1

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Courtney P. Williams, Andres Azuero, Kelly M. Kenzik, Maria Pisu, Ryan D. Nipp, Smita Bhatia, and Gabrielle B. Rocque

years. 4 , 5 Covariates Patient demographic and clinical characteristics were compared and used for model adjustment, including age at index diagnosis, race (white, black, other) type of MBC (de novo vs treated, secondary metastatic disease), HR status

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Ayal A. Aizer, Xiangmei Gu, Ming-Hui Chen, Toni K. Choueiri, Neil E. Martin, Jason A. Efstathiou, Andrew S. Hyatt, Powell L. Graham, Quoc-Dien Trinh, Jim C. Hu, and Paul L. Nguyen

cancer and a short life expectancy in the United States. Subjects/Patients and Methods Patient Characteristics and Study Design A population-based study was conducted of patients with low-risk prostate cancer identified by the SEER

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Randy C. Miles, Christoph I. Lee, Qin Sun, Aasthaa Bansal, Gary H. Lyman, Jennifer M. Specht, Catherine R. Fedorenko, Mikael Anne Greenwood-Hickman, Scott D. Ramsey, and Janie M. Lee

. Clinical data included age at diagnosis, race, diagnosis year, family history of breast cancer, and breast cancer–specific genetic mutation status. Tumor characteristic data included AJCC stage, grade, size, and estrogen receptor (ER), progesterone receptor