scholarly journals Identifying Survival Predictive Factors in Patients with Breast Cancer: A 16-Year Cohort Study Using Cox Maximum Penalized Likelihood Method

2019 ◽  
Vol In Press (In Press) ◽  
Author(s):  
Rezvaneh Alvandi ◽  
Aliakbar Rasekhi ◽  
Mehdi Ariana
Keyword(s):  
Breast Cancer ◽  
Cohort Study ◽  
Predictive Factors ◽  
Penalized Likelihood ◽  
Likelihood Method ◽  
Maximum Penalized Likelihood

Local influence for elliptical partially varying-coefficient model

2017 ◽  
Vol 18 (2) ◽  
pp. 149-174
Author(s):  
Germán Ibacache-Pulgar ◽  
Sebastián Reyes
Keyword(s):  
Penalized Likelihood ◽  
Smoothing Splines ◽  
Local Influence ◽  
Likelihood Method ◽  
Varying Coefficient Models ◽  
Varying Coefficient Model ◽  
Real Dataset ◽  
Varying Coefficient ◽  
Maximum Penalized Likelihood ◽  
Heavy Tailed

In this article, we extend varying-coefficient models with normal errors to elliptical errors in order to permit distributions with heavier and lighter tails than the normal ones. This class of models includes all symmetric continuous distributions, such as Student-t, Pearson VII, power exponential and logistic, among others. Estimation is performed by maximum penalized likelihood method and by using smoothing splines. In order to study the sensitivity of the penalized estimates under some usual perturbation schemes in the model or data, the local influence curvatures are derived and some diagnostic graphics are proposed. A real dataset previously analysed by using varying-coefficient models with normal errors is reanalysed under varying-coefficient models with heavy-tailed errors.

scholarly journals Clinical Predictive Factors for the Efficacy of Everolimus in Patients With Hormone Receptor-Positive, HER2-Negative Advanced Breast Cancer: A Multicenter Retrospective Cohort Study in Japan

2019 ◽  
Vol 13 ◽  
pp. 117822341882513
Author(s):  
Yuichiro Kikawa ◽  
Takeshi Kotake ◽  
Yukiko Kajiwara ◽  
Kazuki Hashimoto ◽  
Hiroyasu Yamashiro ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cohort Study ◽  
Liver Metastases ◽  
Endocrine Therapy ◽  
Predictive Factors ◽  
Metastatic Breast ◽  
Routine Practice ◽  
Hormone Sensitivity ◽  
Hormone Receptor Positive ◽  
Clinical Background

Purpose: To investigate the clinical predictive factors for the efficacy of everolimus (EVE) for advanced/metastatic breast cancer (AMBC). Methods: Routine practice data of consecutive patients with AMBC who received EVE at 5 institutions in western Japan were retrospectively analyzed in this cohort study (study registration no.: UMIN 000032569). The correlation among time to treatment failure (TTF), overall survival (OS), and clinical background was investigated via univariate and multivariate analyses using Cox hazards model for the clinically important variables. Results: A total of 134 patients were included in the analysis. The median TTF and OS were 5.2 months (95% confidence interval [CI]: 4.1-6.4) and 27.3 months (95% CI: 23.7-30.9), respectively. Multivariate analysis showed that dose reduction of EVE from any initial dose was associated with a longer TTF (hazard ratio [HR]: 0.52; 95% CI: 0.32-0.84, P = .007). Meanwhile, very low hormone sensitivity (ie, relapse within the first 2 years during adjuvant endocrine therapy or progression within 3 months of endocrine therapy immediately before EVE) was associated with a shorter TTF (HR: 2.48; 95% CI: 1.49-4.10, P < .001). In the analysis of stratified treatment outcomes, TTF was longer in the group with <3 liver metastases and in groups other than the very low hormone sensitivity group, regardless of the treatment line of EVE. Conclusions: Low hormone sensitivity and ⩾3 liver metastases were important prognostic factors for the efficacy of EVE. EVE may be less effective in patients with AMBC with these factors, and as such, chemotherapy should be administered instead.

Cynical distrust increases breast cancer risk: Evidence from a prospective cohort study

2002 ◽  
Author(s):  
A. R. Aro ◽  
H. J. de Koning ◽  
K. Vehkalahti ◽  
P. Absetz ◽  
M. Schreck ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cohort Study ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Prospective Cohort Study ◽  
Prospective Cohort
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