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
Amy A. Kirkham, Karen A. Gelmon, Cheri L. Van Patten, Kelcey A. Bland, Holly Wollmann, Donald C. McKenzie, Taryne Landry, and Kristin L. Campbell
Baseline characteristics were compared between groups using independent t tests and chi-square tests. Relative risk (RR; with 95% confidence intervals and number needed to treat [NNT]) was used to compare categorical metrics of treatment tolerance
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
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
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
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
Lindsay M. Sabik, Kirsten Y. Eom, Zhaojun Sun, Jessica S. Merlin, Hailey W. Bulls, Patience Moyo, Jennifer A. Pruskowski, G.J. van Londen, Margaret Rosenzweig, and Yael Schenker
cancer diagnosis using chi-square tests. We then estimated multivariable logistic regression models to assess the association of patient characteristics, previous opioid receipt, treatment category, and year of diagnosis with receipt of an opioid
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
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
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
