scholarly journals Parametric models for spatially correlated survival data for individuals with multiple cancers

2008 ◽  
Vol 27 (12) ◽  
pp. 2127-2144 ◽  
Author(s):  
Ulysses Diva ◽  
Dipak K. Dey ◽  
Sudipto Banerjee
Keyword(s):  
Survival Data ◽  
Parametric Models ◽  
Spatially Correlated ◽  
Multiple Cancers

scholarly journals Modelling spatially correlated survival data for individuals with multiple cancers

2007 ◽  
Vol 7 (2) ◽  
pp. 191-213 ◽  
Author(s):  
Ulysses Diva ◽  
Sudipto Banerjee ◽  
Dipak K Dey
Keyword(s):  
Survival Data ◽  
Spatially Correlated ◽  
Multiple Cancers

scholarly journals 1466Vitamin D status before, during and after treatment and ovarian cancer survival

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Tanya Ross ◽  
Penny Webb ◽  
Rachel Neale
Keyword(s):  
Ovarian Cancer ◽  
Vitamin D ◽  
Survival Data ◽  
Cancer Survival ◽  
Cox Regression ◽  
Primary Treatment ◽  
Parametric Models ◽  
Cancer Prognosis ◽  
Ovarian Cancer Survival ◽  
Ovarian Cancer Prognosis

Abstract Background Previous work found higher serum 25-hydroxyvitamin D (25(OH)D) [circulating form of vitamin D] concentrations at diagnosis were associated with longer survival in patients with ovarian cancer (OvCa). There was no evidence for an association with 25(OH)D after primary treatment, but power was limited. Our aim was to reassess this association in a larger sample, including measures collected during treatment and using techniques to deseasonalise 25(OH)D. Methods Participants were diagnosed between 2002-2006 and 2012-2015 from the Australian Ovarian Cancer Study (AOCS) and the Ovarian Cancer, Prognosis and Lifestyle (OPAL) study, respectively. 25(OH)D concentrations were available for 676 at diagnosis (AOCS), 805 during treatment (AOCS:208; OPAL:597) and 861 after completion of primary treatment and before recurrence (AOCS:342; OPAL:519); 1006 AOCS samples were included in the previous analysis. Sociodemographic, diet and lifestyle data came from questionnaires self-completed at recruitment, and clinical/survival data from medical records, supplemented with National Death Index linkage. We will use Cox regression and non-parametric models to examine associations with survival. Results Median 25(OH)D concentrations were lowest during treatment, intermediate at diagnosis and highest after treatment (AOCS 51, 64, and 71 nmol/L, respectively). 5-year survival was 50% in AOCS and 59% in OPAL. Updated survival results will be presented. Conclusions If the association is confirmed in this updated analysis, then increasing vitamin D concentrations may provide a way to improve survival following OvCa. Key messages Higher circulating vitamin D concentrations may improve survival in OvCa.

scholarly journals On Generalized Gamma Distribution and Its Application to Survival Data

2019 ◽  
Vol 8 (5) ◽  
pp. 85 ◽  
Author(s):  
Kiche J ◽  
Oscar Ngesa ◽  
George Orwa
Keyword(s):  
Survival Analysis ◽  
Gamma Distribution ◽  
Survival Data ◽  
Parametric Model ◽  
Parametric Modeling ◽  
Parametric Models ◽  
Generalized Gamma Distribution ◽  
Generalized Gamma ◽  
Special Cases ◽  
Two Parameter

The generalized gamma distribution is a continuous probability distribution with three parameters. It is a generalization of the two-parameter gamma distribution. Since many distributions commonly used for parametric models in survival analysis (such as the Exponential distribution , the Weibull distribution and the Gamma distribution) are special cases of the generalized gamma, it is sometimes used to determine which parametric model is appropriate for a given set of data. Generalized gamma distribution is one of the distributions used in frailty modeling. In this study , it is shown that generalized gamma distribution has three sub-families and its application to the analysis of a survival data has also been explored. The parametric modeling approach has been carried out to find the expected results.

scholarly journals Parametric Model Evaluation in Examining the Survival of Gastric Cancer Patients Andits Influencing Factors

2016 ◽  
Vol 9 (3) ◽  
pp. 260
Author(s):  
Abolfazl Nikpour ◽  
Jamshid Yazdani Charati ◽  
Iraj Maleki ◽  
Hosien Ranjbaran ◽  
Alireza Khalilian
Keyword(s):  
Gastric Cancer ◽  
Survival Data ◽  
Cox Regression ◽  
Weibull Model ◽  
Exponential Model ◽  
Study Data ◽  
Parametric Models ◽  
Risk Of Death ◽  
Log Normal ◽  
Gastric Cancer Patients

<p><strong>BACKGROUND:</strong> Cox proportional hazard model is the most common technique to analysis the variables effect on survival time, but under certain circumstances, parametric models may offer advantages over Cox’s model. In this study we use cox regression and alternative parametric models such as Weibull, exponential, log-normal, logistics and gamma model to evaluate factors affecting survival of patients with gastric cancer. Comparisons were made to find the best model.</p><p><strong>METHOD</strong><strong>:</strong> In this study, data from 643 patients with gastric cancer who were referred to Imam Khomeini hospital with personal details during 2007 to 2013 have been reviewed in order to determine the survival rate of gastric cancer. It was observed that 74 cases were eliminated due to incomplete information and 569 persons were examined. Akaike Information model was used for comparison between models.</p><p><strong>RESULT:</strong> Of a total of 569 patients, 329 (57.8%) died during the period. The figure of Cox-Snell residuals indicates that only the exponential model does not have better fitness. Weibull, log-normal, log-logistic and gamma models show the better fitness because points are on straight line. At the time of diagnosis, stage with (p&lt;0.0008) and metastasis with (p&lt;0.0219) were subjected to higher risk of death.</p><p class="Default"><strong>CONCLUSION:</strong> Based on Akaike's criterion, the Weibull model with Akaike value of 257.165 is the most favorable for survival data.</p>

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