A Model-Free Way of Representing Hyperthermia Cell Survival Data

1986 ◽  
Vol 107 (3) ◽  
pp. 307 ◽  
Author(s):  
J. H. T. Bates ◽  
E. Przybytkowski ◽  
D. A. Bates ◽  
W. J. MacKillop
Keyword(s):  
Cell Survival ◽  
Survival Data ◽  
Model Free

A Proportional Hazards Neural Network for Performing Reliability Estimates and Risk Prognostics for Mobile Systems Subject to Stochastic Covariates

2005 ◽  
Author(s):  
George M. Lloyd ◽  
Timothy Hasselman ◽  
Thomas Paez
Keyword(s):  
Survival Data ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Mobile Systems ◽  
Linear Network ◽  
Model Free ◽  
Hazards Model ◽  
Reliability Estimates ◽  
Non Linear ◽  
Stochastic Covariates

We present a proportional hazards model (PHM) that establishes a framework suitable for performing reliability estimates and risk prognostics on complex multi-component systems which are transferred at arbitrary times among a discrete set of non-stationary stochastic environments. Such a scenario is not at all uncommon for portable and mobile systems. It is assumed that survival data, possibly interval censored, is available at several “typical” environments. This collection of empirical survival data forms the foundation upon which the basic effects of selected covariates are incorporated via the proportional hazards model. Proportional hazards models are well known in medical statistics, and can provide a variety of data-driven risk models which effectively capture the effects of the covariates. The paper describes three modifications we have found most suitable for this class of systems: development of suitable survival estimators that function well under realistic censoring scenarios, our modifications to the PHM which accommodate time-varying stochastic covariates, and implementation of said model in a non-linear network context which is itself model-free. Our baseline hazard is a parameterized reliability model developed from the empirical reliability estimates. Development of the risk score for arbitrary covariates arising from movement among different random environments is through interaction of the non-linear network and training data obtained from a Markov chain simulation based on stochastic environmental responses generated from Karhunen-Loe`ve models.

Red-cell lifetimes calculated from 51 Cr red-cell survival data

1974 ◽  
Vol 1 (2) ◽  
pp. 58-61 ◽  
Author(s):  
J. C. Widman ◽  
E. R. Powsner
Keyword(s):  
Cell Survival ◽  
Survival Data ◽  
Red Cell ◽  
Red Cell Survival

Binary Methods for the Microdosimetric Analysis of Cell Survival Data from Alpha-Particle Irradiation

2003 ◽  
Vol 18 (3) ◽  
pp. 481-487 ◽  
Author(s):  
Thomas G. Stinchcomb ◽  
Christina Soyland ◽  
Sindre P. Hassfjell ◽  
John Westman ◽  
Steven J. Wang ◽  
...  
Keyword(s):  
Cell Survival ◽  
Alpha Particle ◽  
Survival Data ◽  
Particle Irradiation

Biophysical Studies with Spatially Correlated Ions: 4. Analysis of Cell Survival Data for Diatomic Deuterium

1980 ◽  
Vol 83 (3) ◽  
pp. 511 ◽  
Author(s):  
Albrecht M. Kellerer ◽  
Yuk-Ming P. Lam ◽  
Harald H. Rossi
Keyword(s):  
Cell Survival ◽  
Survival Data ◽  
Spatially Correlated ◽  
Biophysical Studies
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