scholarly journals Basic Equations and Computing Procedures for Frailty Modeling of Carcinogenesis: Application to Pancreatic Cancer Data

2013 ◽  
Vol 12 ◽  
pp. CIN.S8063 ◽  
Author(s):  
Tengiz Mdzinarishvili ◽  
Simon Sherman
Keyword(s):  
Pancreatic Cancer ◽  
At Risk ◽  
Hazard Function ◽  
Survival Function ◽  
Population Level ◽  
Time Shift ◽  
Cumulative Hazard ◽  
Cancer Data ◽  
Weibull Pdf ◽  
Basic Equations

Modeling of cancer hazards at age t deals with a dichotomous population, a small part of which (the fraction at risk) will get cancer, while the other part will not. Therefore, we conditioned the hazard function, h( t), the probability density function (pdf), f( t), and the survival function, S( t), on frailty α in individuals. Assuming α has the Bernoulli distribution, we obtained equations relating the unconditional (population level) hazard function, hU( t), cumulative hazard function, HU( t), and overall cumulative hazard, H0, with the h( t), f( t), and S( t) for individuals from the fraction at risk. Computing procedures for estimating h( t), f( t), and S( t) were developed and used to ft the pancreatic cancer data collected by SEER9 registries from 1975 through 2004 with the Weibull pdf suggested by the Armitage-Doll model. The parameters of the obtained excellent fit suggest that age of pancreatic cancer presentation has a time shift about 17 years and five mutations are needed for pancreatic cells to become malignant.

scholarly journals A Weibull-Gompertz Makeham Distribution with Properties and Application to Cancer Data

2019 ◽  
pp. 1-17
Author(s):  
Peter O. Koleoso ◽  
Angela U. Chukwu
Keyword(s):  
Bladder Cancer ◽  
Maximum Likelihood ◽  
Order Statistics ◽  
Hazard Function ◽  
Probability Distributions ◽  
Survival Function ◽  
Statistical Properties ◽  
Cancer Data ◽  
Method Of Maximum Likelihood ◽  
Flexible Model

The article presents an extension of the Gompertz Makeham distribution using the Weibull-G family of continuous probability distributions proposed by Tahir et al. (2016a). This new extension generates a more flexible model called Weibull-Gompertz Makeham distribution. Some statistical properties of the distribution which include the moments, survival function, hazard function and distribution of order statistics were derived and discussed. The parameters were estimated by the method of maximum likelihood and the distribution was applied to a bladder cancer data. Weibull-Gompertz Makeham distribution performed best (AIC = -6.8677, CAIC = -6.3759, BIC = 7.3924) when compared with other existing distributions of the same family to model bladder cancer data.

scholarly journals Monitoring the newly infected cases of COVID-19 data weekly: A Survival Data Analysis (SDA) perspective

2021 ◽  
pp. 1-16
Author(s):  
Ramachandran Ramasamy ◽  
Maniam Kaliannan
Keyword(s):  
Data Analysis ◽  
Survival Data ◽  
Hazard Function ◽  
Survival Function ◽  
Shape Parameters ◽  
Cumulative Hazard ◽  
Short Term ◽  
Single Measure ◽  
Model Distribution ◽  
Cordon Sanitaire

This paper attempts to fit the best survival model distribution for the Malaysian COVID-19 new infections experience of Wave I/II and Wave III using the well-known Survival Data Analysis (SDA) procedures. The purpose of fitting such models is to reduce the complexity and frequency of the COVID-19 new infections data into a single measure of scale and shape parameters to enable monitoring of weekly trends, undertake short term forecasts and estimate duration when the virality will be contained. The analysis showed a Weibull distribution is the best statistical fit for Malaysia’s new infections COVID-19 data. The estimates of scale and shape parameters for Wave I/II was 0.05901 and 2.48956 and for Wave III was 0.06463 and 2.5693, respectively. Much higher hazard force in Wave III is due to weaker control in the implementation of cordon sanitaire measures imposed in containing the virality spread. Based on the survival function the short-term forecasts showed that the number of new infections projected to decline from 23,282 cases in 28th week to 22,017 cases in 31st week. Similarly, based on the cumulative hazard function the duration estimated for containing the virality completely projected to stretch over another 19.6 weeks under the prevailing conditions.

Melanoma-prone kindreds with p16 mutation are also at risk of pancreatic cancer

1996 ◽  
Vol 3 (4) ◽  
pp. 250
Author(s):  
Goldstein AM ◽  
Eraser MC ◽  
Struewing JP ◽  
Whelan AJ ◽  
Bartsch D ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
At Risk

scholarly journals Variations in cancer centers’ use of cytology for the diagnosis of unresectable pancreatic cancer in the National Cancer Data Base

2016 ◽  
Vol 124 (11) ◽  
pp. 791-800 ◽  
Author(s):  
Ted Gansler ◽  
Stacey A. Fedewa ◽  
Chun Chieh Lin ◽  
Ahmedin Jemal ◽  
Elizabeth M. Ward
Keyword(s):  
Pancreatic Cancer ◽  
Data Base ◽  
National Cancer Data Base ◽  
Unresectable Pancreatic Cancer ◽  
Cancer Centers ◽  
Cancer Data

scholarly journals Patterns of chemotherapy for recurrent pancreatic cancer following resection: A population-level analysis

HPB ◽  
2016 ◽  
Vol 18 ◽  
pp. e132
Author(s):  
E. Walser ◽  
D. Kagedan ◽  
C. Earle ◽  
Q. Li ◽  
L. Paszat ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Population Level ◽  
Recurrent Pancreatic Cancer ◽  
Level Analysis

scholarly journals Predicting how populations decline to extinction

2011 ◽  
Vol 366 (1577) ◽  
pp. 2577-2586 ◽  
Author(s):  
Ben Collen ◽  
Louise McRae ◽  
Stefanie Deinet ◽  
Adriana De Palma ◽  
Tharsila Carranza ◽  
...  
Keyword(s):  
At Risk ◽  
Extinction Risk ◽  
Mammalian Species ◽  
Local Population ◽  
Population Decline ◽  
Scale Up ◽  
Population Level ◽  
Biological Traits ◽  
Population Declines ◽  
Trend Data

Global species extinction typically represents the endpoint in a long sequence of population declines and local extinctions. In comparative studies of extinction risk of contemporary mammalian species, there appear to be some universal traits that may predispose taxa to an elevated risk of extinction. In local population-level studies, there are limited insights into the process of population decline and extinction. Moreover, there is still little appreciation of how local processes scale up to global patterns. Advancing the understanding of factors which predispose populations to rapid declines will benefit proactive conservation and may allow us to target at-risk populations as well as at-risk species. Here, we take mammalian population trend data from the largest repository of population abundance trends, and combine it with the PanTHERIA database on mammal traits to answer the question: what factors can be used to predict decline in mammalian abundance? We find in general that environmental variables are better determinants of cross-species population-level decline than intrinsic biological traits. For effective conservation, we must not only describe which species are at risk and why, but also prescribe ways to counteract this.

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