Abstract:Research on comorbidity across cancer symptoms, including pain, fatigue, and depression, could suggest if crossover effects from symptom-specific interventions are plausible. Secondary analyses were conducted on a survey of 268 cancer patients with recurrent disease from a northeastern U.S. city who were initiating palliative radiation for bone pain. Moderator regression analyses predicted variation in depressive affect that could be attributed to symptom clusters. Patients self-reported difficulty controlling… Show more
“…CES-D somatic items were excluded because they may constitute symptoms of cancer instead of depression. The internal consistency for the eleven items in these data is very good (α = 0.83), which compares favorably to α = 0.85 in the entire CES-D [26].…”
Section: Cancer Symptoms Data and Modelsmentioning
confidence: 58%
“…In Table 2, the resulting SRC-CS models (1B, 2B, and 3B) condition essential multicollinearity related to two secondary variables, Shortness of breath/difficulty breathing and Nausea/vomiting, from the initial, residualizing regression. These two secondary variables are added to these SRC-CS models because in previous analyses with these data, these common symptoms were revealed to be components of symptom interactions also involving Pain or Fatigue/weakness [26], which could overlap those in the current study. With this essential multicollinearity removed, it is optional whether to retain these secondary variables in the subsequent QMMR models that test each three-way interaction separately, Open Access OJS R. B. FRANCOEUR 35 and both variables are dropped from 1B and 2B.…”
Section: Cancer Symptoms Data and Modelsmentioning
confidence: 91%
“…The single-item measures of physical symptoms were initially reported to be common measures derived from previous studies [25]. More recently, a review by Francoeur [26] revealed different lines of converging evidence in the literature that collectively support the reliability and validity of selfreported, ordinal, single-item measures of the degree of control across several physical symptoms.…”
Section: Cancer Symptoms Data and Modelsmentioning
Multicollinearity constitutes shared variation among predictors that inflates standard errors of regression coefficients. Several years ago, it was proven that the common practice of mean centering in moderated regression cannot alleviate multicollinearity among variables comprising an interaction, but merely masks it. Residual centering (orthogonalizing) is unacceptable because it biases parameters for predictors from which the interaction derives, thus precluding interpretation of moderator effects. I propose and validate residual centering in sequential re-estimations of a moderated regression-sequential residual centering (SRC)-by revealing unbiased multicollinearity conditioning across the interaction and its related terms. Across simulations, SRC reduces variance inflation factors (VIF) regardless of distribution shape or pattern of regression coefficients across predictors. For any predictor, the reduced VIF is used to derive a lower standard error of its regression coefficient. A cancer sample illustrates SRC, which allows unbiased interpretations of symptom clusters. SRC can be applied efficiently to alleviate multicollinearity after data collection and shows promise for advancing synergistic frontiers of research.
“…CES-D somatic items were excluded because they may constitute symptoms of cancer instead of depression. The internal consistency for the eleven items in these data is very good (α = 0.83), which compares favorably to α = 0.85 in the entire CES-D [26].…”
Section: Cancer Symptoms Data and Modelsmentioning
confidence: 58%
“…In Table 2, the resulting SRC-CS models (1B, 2B, and 3B) condition essential multicollinearity related to two secondary variables, Shortness of breath/difficulty breathing and Nausea/vomiting, from the initial, residualizing regression. These two secondary variables are added to these SRC-CS models because in previous analyses with these data, these common symptoms were revealed to be components of symptom interactions also involving Pain or Fatigue/weakness [26], which could overlap those in the current study. With this essential multicollinearity removed, it is optional whether to retain these secondary variables in the subsequent QMMR models that test each three-way interaction separately, Open Access OJS R. B. FRANCOEUR 35 and both variables are dropped from 1B and 2B.…”
Section: Cancer Symptoms Data and Modelsmentioning
confidence: 91%
“…The single-item measures of physical symptoms were initially reported to be common measures derived from previous studies [25]. More recently, a review by Francoeur [26] revealed different lines of converging evidence in the literature that collectively support the reliability and validity of selfreported, ordinal, single-item measures of the degree of control across several physical symptoms.…”
Section: Cancer Symptoms Data and Modelsmentioning
Multicollinearity constitutes shared variation among predictors that inflates standard errors of regression coefficients. Several years ago, it was proven that the common practice of mean centering in moderated regression cannot alleviate multicollinearity among variables comprising an interaction, but merely masks it. Residual centering (orthogonalizing) is unacceptable because it biases parameters for predictors from which the interaction derives, thus precluding interpretation of moderator effects. I propose and validate residual centering in sequential re-estimations of a moderated regression-sequential residual centering (SRC)-by revealing unbiased multicollinearity conditioning across the interaction and its related terms. Across simulations, SRC reduces variance inflation factors (VIF) regardless of distribution shape or pattern of regression coefficients across predictors. For any predictor, the reduced VIF is used to derive a lower standard error of its regression coefficient. A cancer sample illustrates SRC, which allows unbiased interpretations of symptom clusters. SRC can be applied efficiently to alleviate multicollinearity after data collection and shows promise for advancing synergistic frontiers of research.
“…Emerging evidence suggests that psychological symptoms contribute to decrements in QOL in patients with advanced cancer. [65][66][67][68] For example, higher depression scores were associated with higher symptom severity scores. 69 In addition, in a study of cancer patients in their last year of life, 68 higher levels of depressive symptoms at enrollment were associated with a worse symptom experience over time.…”
While patients with advanced cancer experience a wide range of symptoms, no work has been done to determine an optimal cutpoint for a low versus a high number of symptoms. Analytic approaches that established clinically meaningful cutpoints for the severity of cancer pain and fatigue provided the foundation for this study. The purpose of this study was to determine the optimal cutpoint for low and high numbers of symptoms using a range of potential cutpoints and to determine if those cutpoints distinguished between the two symptom groups on demographic and clinical characteristics and depression, anxiety, and quality of life (QOL). Patients with advanced cancer (n = 110) completed a symptom assessment scale, and measures of depression, anxiety, and QOL. Combinations of cutpoints were tested to yield one-and two-cutpoint solutions. Using analysis of variance for QOL scores, the F-ratio that indicated the highest between-group difference was determined to be the optimal cutpoint between low and high number of symptoms. A cutpoint of £ 12 symptoms (i.e., 0-12 is low, 13-32 is high) was the optimal cutpoint for total number of symptoms. Significant differences in depression, anxiety, and QOL scores validated this cutpoint. Psychological symptoms had higher occurrence rates in the high symptom group. Findings suggest that a threshold exists between a low and a high number of symptoms in patients with advanced cancer. Psychological symptoms were significantly different between patients in the low versus high symptom groups and may play an important role in QOL outcomes in patients with advanced cancer.
“…En personas con cáncer recurrente que inician radiación paliativa por dolor óseo se identifican los síntomas dolor, disminución de peso, fatiga, fiebre, disminución de apetito y sueño (52).…”
Section: Estudios Con Poblaciones Específicas Por Tipos De Cáncer Y Tunclassified
Objetivo: explorar el estado actual de desarrollo investigativo del área temática de grupos de síntomas en adultos con cáncer. Método: revisión integrativa de producción científica generada entre 2001 y 2016. Se incluyeron 61 artículos por su aporte en la comprensión del área temática. Resultados: el estudio de los grupos de síntomas en personas con cáncer se consolida como un área temática novedosa, pertinente y necesaria para la investigación y práctica de enfermería en oncología, con tendencias y retos que incluyen: (1) El desarrollo de marcos conceptuales que aporten a la fundamentación, atributos y efectos (2) La determinación de métodos y formas de crearlos y clasificarlos (3) La generación de estudios con poblaciones específicas por tipos de cáncer y tratamiento y la consolidación de intervenciones de enfermería. Conclusiones: los pacientes con cáncer experimentan múltiples síntomas de forma simultánea durante las distintas fases de la enfermedad. Es incipiente el estudio de este fenómeno, los retos para la enfermería se centran en la generación de respuestas que alivien la carga de los grupos de síntomas y mejoren la calidad de vida de estos pacientes. Es necesario validar escalas de carga del síntoma y explorar los grupos de síntomas incluyendo variables clínicas ligadas a los tratamientos oncológicos.
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