论文信息 - Removing Cancer when it is Correlated with other Causes of Death

Removing Cancer when it is Correlated with other Causes of Death

This paper presents an identifiability theorem in the theory of dependent competing risks and it applies the result by examining the effect of removing cancer from the United States population when cancer is correlated with the other causes of death. The paper shows how dependence can be modeled with copula functions and it shows that calculating the survival probabilities after cancer is removed is equivalent to solving a system of nonlinear differential equations.

Jacques F. Carriere | J. Carriére

[1] Liviu Gr Ixaru,et al. Numerical methods for differential equations and applications , 1984 .

[2] C. Genest,et al. The Joy of Copulas: Bivariate Distributions with Uniform Marginals , 1986 .

[3] A. Yashin,et al. Dependent competing risks: a stochastic process model , 1986, Journal of mathematical biology.

[4] Isha Bagai,et al. Analysis of survival data with two dependent competing risks , 1992 .

[5] A. Sampson,et al. Uniform representations of bivariate distributions , 1975 .

[6] W. Tsai. Estimation of survival curves from dependent censorship models via a generalized self-consistent property with nonparametric Bayesian estimation application , 1986 .

[7] W. Kruskal. Ordinal Measures of Association , 1958 .

[8] James J. Heckman,et al. The identifiability of the competing risks model , 1989 .

[9] N. L. Johnson,et al. Survival Models and Data Analysis , 1982 .

[10] C. Genest. Frank's family of bivariate distributions , 1987 .

[11] A. Sändig,et al. Nonlinear Differential Equations , 1980 .

[12] Kanti V. Mardia,et al. Families of Bivariate Distributions , 1970 .