Statistics 533          
         Some Books on Survival Analysis and Some other References  
                                   W.Q. Meeker

Cox, D.R. and Oakes, D. (1984), Analysis of Survival Data, London:
Chapman and Hall.

	Concise introduction to some of the more interesting and
        modern topics in survival data analysis.  Good mix of parametric and
        nonparametric techniques.

David, H.A., and Moeschberger, M.L. (1978), The Theory of Competing
Risks, London:  Charles Griffin & Company Ltd.

	Basic theory for models and data analysis when there is more
        than one cause of failure.

Elandt-Johnson, R.C. and Johnson, N.L. (1980), Survival Models and 
Data Analysis, New York:  John Wiley and Sons.

	Emphasizes biomedical applications and nonparametric techniques.


Fleming, T. R. and Harrington, D. P. (1992), Counting Processes and
Survival Analysis, New York: John Wiley & Sons, Inc.

        Large sample theory for nonparametric methods for
        point-process and censored data, including the Cox 
        proportional hazards model. Very theoretical. 
        Requires knowledge of martingale theory.


Gross, A.J. and Clark, V.A. (1975) Survival Distributions: Reliability
Applications in Biomedical Sciences, New York:  John Wiley and Sons.

	Introductory level text, focus in on inference in parametric
        models for homogeneous populations. Rather dated now.

Hosmer, D. W., and Lemeshow, S. (1999), Applied Survival
Analysis. Regression Modeling of Time to Event Data,  
New York: John Wiley & Sons, Inc.

        Proportional hazards regression models for survival data.

Kalbfleisch, J.D. and Prentice, R.L. (1980), The Statistical
Analysis of Failure Time Data, New York: John Wiley and Sons.

	Concentrates on inference for regression models, including
        techniques applicable to accelerated life testing.  Classic reference
        for the Cox proportional hazards model. A bit dated, but the authors
        tell me that a new version is in preparation.

Klein, J. P., and   Moeschberger, M. L. (1997),
Survival Analysis : Techniques for Censored and Truncated Data
(Statistics for Biology and Health), Springer.

        Broad coverage of modern statistical methods used for
        survival analysis in the biomedical sciences.


Lawless, J.F. (1982).  Statistical Models and Methods for 
Lifetime Data, New York:  John Wiley and Sons.

        A classic. Broad coverage of topics with many examples from both
        survival analysis and engineering problems.

Lee, Elisa T. (1980), Statistical Methods for Survival 
Data Analysis, Cal.:  Wadsworth, Inc.

        Relatively non-technical introduction to basic methods for
        making inferences about a survival population and for comparing
        populations.  Emphasis is on nonparametric methods.

Miller, R.G. (1981) Survival Analysis, 
New York: John Wiley and Sons.

        Course notes from Rupert Miller's course.  Emphasis on nonparametric
        methods. Paperback. 

OTHER REFERENCES


Cox, D.R., (1972), "Regression Models and Life Tables," Journal of the
Royal Statistical Society, Series B, Volume 34, pp.  187-220.

        Original classic paper developing the widely used
        nonparametric model for survival data analysis with nonhomogeneous
        populations.  This model is extensively treated in the text by
        Kalbfleisch and Prentice.


Kaplan, E.L. and Meier, P. (1958), "Nonparametric Estimation from
Incomplete Observations," Journal of the American Statistical
Association, Volume 53, pp. 457-481.

        Original, classic paper developing Kaplan-Meier estimate for right
        censored observations.

Peto, R. (1973), "Experimental Survival Curves for Interval Censored
Data," Appl. Statist., 22, pp. 86-91.

        Develops the nonparametric maximum likelihood estimator
        (N.P.M.L.E.) for arbitrarily censored data.  This paper suggests using
        the Newton-Raphson algorithm to maximize the likelihood.

Turnbull, B.W. (1976), "The Empirical Distribution Function with
Arbitrarily Grouped, Censored and Truncated Data," Journal of the
Royal Statistical Society, Series 13, Volume 38, pp. 290-295.

        Extends Peto's work to arbitrarily censored and truncated data
        and suggests an EM algorithm to find the N.P.M.L.E.