Introduction to Survival Analysis Procedures |
Andersen, P. K., Borgan, O., Gill, R. D., and Keiding, N. (1992), Statistical Models Based on Counting Processes, New York: Springer-Verlag.
Collett, D. (1994), Modeling Survival Data in Medical Research, London: Chapman & Hall.
Cox, D. R. (1972), “Regression Models and Life Tables,” Journal of the Royal Statistical Society, Series B, 20, 187–220, with discussion.
Cox, D. R. (1975), “Partial Likelihood,” Biometrika, 62, 269–276.
Cox, D. R. and Oakes, D. (1984), Analysis of Survival Data, London: Chapman & Hall.
Elandt-Johnson, R. C. and Johnson, N. L. (1980), Survival Models and Data Analysis, New York: John Wiley & Sons.
Fleming, T. R. and Harrington, D. (1991), Counting Processes and Survival Analysis, New York: John Wiley & Sons.
Gelman, A., Carlin, J., Stern, H., and Rubin, D. (2004), Bayesian Data Analysis, Second Edition, London: Chapman & Hall.
Gilks, W. R., Richardson, S., and Spiegelhalter, D. J. (1996), Markov Chain Monte Carlo in Practice, London: Chapman & Hall.
Ibrahim, J. G., Chen, M. H., and Sinha, D. (2001), Bayesian Survival Analysis, New York: Springer-Verlag.
Kalbfleisch, J. D. and Prentice, R. L. (1980), The Statistical Analysis of Failure Time Data, New York: John Wiley & Sons.
Kaplan, E. L. and Meier, P. (1958), “Nonparametric Estimation from Incomplete Observations,” Journal of the American Statistical Association, 53, 457–481.
Lawless, J. F. (1982), Statistical Methods and Methods for Lifetime Data, New York: John Wiley & Sons.
Lee, E. T. (1992), Statistical Methods for Survival Data Analysis, Second Edition, New York: John Wiley & Sons.
Maddala, G. S. (1983), Limited-Dependent and Qualitative Variables in Econometrics, New York: Cambridge University Press.
Meeker, W. Q. and Escobar, L. A. (1998), Statistical Methods for Reliability Data, New York: John Wiley & Sons.
Nelson, W. (1990), Accelerated Testing: Statistical Models, Test Plans, and Data Analyses, New York: John Wiley & Sons.
Therneau, T. M. and Grambsch, P. M. (2000), Modeling Survival Data: Extending the Cox Model, New York: Springer-Verlag.
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