characteristics. Dichotomous variables were compared using a Pearson chi-square test or Fisher exact test when appropriate. Continuous variables with nonparametric distributions were expressed as medians with corresponding interquartile ranges, and categorical
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William Alegria, Bernard L. Marini, Kevin Sellery Gregg, Dale Lee Bixby, Anthony Perissinotti, and Jerod Nagel
characteristics and overall treatment patterns of patients diagnosed with mNETs from a retrospective database analysis of community oncology practices. This report provides additional information on the treatment outcomes for this population. Methods: Clinical
Katy E. Balazy, Cecil M. Benitez, Paulina M. Gutkin, Clare E. Jacobson, Rie von Eyben, and Kathleen C. Horst
treatments, and there was no difference between NES and ES patients ( P =.794). Table 1. Baseline Patient Characteristics Of the 5 time intervals analyzed, the longest interval was from resection to start of RT (mean [SD], 2.8 [1.8] months), and
Katy E. Balazy, Cecil M. Benitez, Paulina M. Gutkin, Clare E. Jacobson, Rie von Eyben, and Kathleen C. Horst
treatments, and there was no difference between NES and ES patients ( P =.794). Table 1. Baseline Patient Characteristics Of the 5 time intervals analyzed, the longest interval was from resection to start of RT (mean [SD], 2.8 [1.8] months), and
P. Connor Johnson, Netana H. Markovitz, Tamryn F. Gray, Sunil Bhatt, Ryan D. Nipp, Nneka Ufere, Julia Rice, Matthew J. Reynolds, Mitchell W. Lavoie, Carlisle E.W. Topping, Madison A. Clay, Charlotta Lindvall, and Areej El-Jawahri
characteristics along with healthcare utilization and mortality. To investigate the relationship between social support and OS, we conducted Cox proportional hazards regression analyses, adjusting for the following covariates that were a priori defined based on
Tejaswi Mudigonda, Daniel J. Pearce, Brad A. Yentzer, Phillip Williford, and Steven R. Feldman
physicians, and $521 for other physicians. 28 Table 2 Treatment Characteristics of Non–Melanoma Skin Cancer Episodes in 1999–2000 by Specialty To further illustrate treatment characteristics according to specialty, Manternach et al. 27
Mei-Chin Hsieh, Lu Zhang, Xiao-Cheng Wu, Mary B. Davidson, Michelle Loch, and Vivien W. Chen
’ sociodemographic and tumor characteristics, and to examine the impact of guideline-concordant treatment status and cancer subtype on survival outcome. Methods Data Source and Study Cohort Data on female breast cancer were obtained from the Louisiana Tumor Registry
Eric D. Miller, Ansel P. Nalin, Dayssy A. Diaz Pardo, Andrea L. Arnett, Emily Huang, Alessandra C. Gasior, Pannaga Malalur, Hui-Zi Chen, Terence M. Williams, and Jose G. Bazan
account for clustering in matched pairs. All statistical analyses were performed using SAS 9.4 (SAS Institute Inc). Results Baseline Characteristics We identified 9,156 elderly patients and 17,640 nonelderly patients who met the study criteria
Sriram Yennurajalingam, Vicente Valero, Zhanni Lu, Diane D. Liu, Naifa L. Busaidy, James M. Reuben, Carolina Diaz Fleming, Janet L. Williams, Kenneth R. Hess, Karen Basen-Engquist, and Eduardo Bruera
interventions, along with tolerability. To compare patient characteristics, we used the chi-square and the Wilcoxon signed rank tests for the FACIT-F (primary outcome), ESAS fatigue item, PROMIS-F, MFSI-SF, ESAS physical distress score, ESAS psychological
Tammy T. Hshieh, Clark DuMontier, Timothy Jaung, Nupur E. Bahl, Chelsea E. Hawley, Lee Mozessohn, Richard M. Stone, Robert J. Soiffer, Jane A. Driver, and Gregory A. Abel
on information extracted from the oncology consultation note. Statistical Analysis Population characteristics were summarized using proportions. Chi-square analyses were performed in the total population to assess for associations between
