important to guide health authorities and practitioners to provide a personalized cancer survivorship message (according to individual characteristics) instead of the currently available generic messages and advice that do not take into consideration the
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Omar Abdel-Rahman
Jeffrey M. Martin, Tianyu Li, Matthew E. Johnson, Colin T. Murphy, Alan G. Howald, Marc C. Smaldone, Alexander Kutikov, David Y.T. Chen, Rosalia Viterbo, Richard E. Greenberg, Robert G. Uzzo, and Eric M. Horwitz
univariate analyses were performed. Results From 2003 to 2011, 475 patients received PPRT at FCCC (83 adjuvant and 392 salvage). The patient characteristics and descriptives are listed in Table 1 . Patients were more likely to receive adjuvant RT if
Arif Kamal, Tian Zhang, Steve Power, and P. Kelly Marcom
patients with cancer and the noncancer control population, a chi-square test was performed. Univariate logistic regression was used to determine patient and disease characteristics associated with the ordering of imaging. For determination of the attribute
Eric J. Roeland, Kathryn J. Ruddy, Thomas W. LeBlanc, Ryan D. Nipp, Gary Binder, Silvia Sebastiani, Ravi Potluri, Luke Schmerold, Eros Papademetriou, Lee Schwartzberg, and Rudolph M. Navari
We analyzed patient characteristics, HEC courses, and clinician practice patterns using descriptive statistics. Clinician HEC CINV prophylaxis adherence was categorized into deciles for each HEC, noting the clinicians with ≤90% adherence. Variability
Emily C. Harrold, Ahmad F. Idris, Niamh M. Keegan, Lynda Corrigan, Min Yuen Teo, Martin O’Donnell, Sean Tee Lim, Eimear Duff, Dearbhaile M. O’Donnell, M. John Kennedy, Sue Sukor, Cliona Grant, David G. Gallagher, Sonya Collier, Tara Kingston, Ann Marie O’Dwyer, and Sinead Cuffe
independent predictors of insomnia syndrome. Results Demographic and Clinical Characteristics Of the 337 patients invited to participate, 87% consented to study inclusion (n=294); 12 declined without explanation, 15 were too unwell to participate, 8 declined
into clinical care paths. Further, cancer treatment is becoming increasingly personalized to the patient and tumor characteristics, thus increasing the complexity of decision-making. Methods: We developed a method to model guideline recommendations
Kah Poh Loh, Maya Abdallah, Meng-Shiou Shieh, Mihaela S. Stefan, Penelope S. Pekow, Peter K. Lindenauer, Supriya G. Mohile, Dilip Babu, and Tara Lagu
are reliably coded. Patient and Clinical Characteristics We collected demographics including age, sex, race, insurance provider, comorbidities (modified combined comorbidity score derived from the Elixhauser and Charlson comorbidity index
Stephen J. Bagley, Suzanna Talento, Nandita Mitra, Neal J. Meropol, Roger B. Cohen, Corey J. Langer, and Anil Vachani
mortality data with results from published literature and the SEER database. Flatiron’s mortality data are estimated to have a sensitivity of 85% to 90%, specificity of 97% to 98%, and accuracy of 95% to 97%. 15 Baseline Characteristics Demographic and
Xiang Gao, Amanda R. Kahl, Paolo Goffredo, Albert Y. Lin, Praveen Vikas, Imran Hassan, and Mary E. Charlton
2014. In addition to the traditional demographic and tumor characteristics collected by SEER, the SEER POC study collected additional variables in a sample of patients from various SEER registries. For stage IV colon cancer, these variables included the
Matthew P. Banegas, Linda C. Harlan, Bhupinder Mann, and K. Robin Yabroff
regions. Currently, SEER covers approximately 28% of the US population. 7 Information for each patient in SEER is primarily obtained from hospital records and includes tumor characteristics, treatment, and select demographic characteristics. Given that
