Bibliography

Aalen, O. (1978). Nonparametric inference for a family of counting processes, Annals of Statistics 6: 701–726.

Aalen, O. (1989). A linear regression model for the analysis of life times, Statistics in Medicine 8: 907–925.

Aalen, O. (1993). Further results on the non-parametric linear model in survival analysis, Statistics in Medicine 12: 1569–1588.

Aalen, O., Borgan, Ø. & Gjessing, H. (2008). Survival and Event History Analysis: A Process Point of View, Springer, New York.

Allison, P. (1984). Event History Analysis, Sage University Paper series on Quantitative Applications in the Social Sciences, No. 46, Beverly Hills, CA.

Allison, P. (1995). Survival Analysis Using the SAS System: A Practical Guide, BBU Press, SAS Institute, Cary, NC.

Andersen, P., Borgan, Ø., Gill, R. & Keiding, N. (1993). Statistical Models Based on Counting Processes, Springer-Verlag, Berlin.

Breslow, N. (1974). Covariance analysis of censored survival data, Biometrika 30: 89–99.

Broström, G. (1987). The influence of mother’s mortality on infant mortality: A case study in matched data survival analysis, Scandinavian Journal of Statistics 14: 113–123.

Broström, G. (2002). Cox regression: Ties without tears, Communications in Statistics: Theory and Methods 31: 285–297.

Broström, G. (2012). eha: Event History Analysis. R package version 2.0-7.

Broström, G. & Lindkvist, M. (2008). Partial partial likelihood, Communications in Statistics: Simulation and Computation 37: 679–686.

Collett, D. (2003). Modelling Survival Data in Medical Research, Second edn, Chapman & Hall/CRC.

Cox, D. (1972). Regression models and life tables, Journal of the Royal Statistical Society Series B (with discussion) 34: 187–220.

Cox, D. (1975). Partial likelihood, Biometrika 62: 269–276.

Cox, D. & Oakes, D. (1984). Analysis of Survival Data, Chapman & Hall, London.

Dalgaard, P. (2008). Introductory Statistics with R, Second edn, Springer, Berlin.

Efron, B. (1977). Efficiency of Cox’s likelihood function for censored data, Journal of the American Statistical Association 72: 557–565.

Elandt-Johnson, R. & Johnson, N. (1999). Survival Models and Data Analysis, Wiley Classics Library edn, John Wiley & Sons, New York.

Fine, J. & Gray, R. (1999). A proportional hazards model for the subdistribution of a competing risk, Journal of the American Statistical Association 94: 496–509.

Gompertz, B. (1825). On the nature of the function expressive of the law of human mortality, and on a new mode of determining the value of life contingencies, Philosophical Transactions of the Royal Society of London 115: 513–585.

Granger, C. (1969). Investigating causal relations by econometric models and cross-spectral methods, Econometrica 37: 424–438.

Gray, B. (2011). cmprsk: Subdistribution Analysis of Competing Risks. R package version 2.2-2.
URL: http://CRAN.R-project.org/package=cmprsk

Gray, R. (1988). A class of K-sample tests for comparing the cumulative incidence of a competing risk, Journal of the American Statistical Association 94: 496–509.

Groeneboom, P. & Wellner, J. (1992). Nonparametric Maximum Likelihood Estimatorsfor Interval Censoring and Deconvolution, Birkhäuser, Boston.

Harrell Jr., F. (2011). Hmisc: Harrell Miscellaneous. R package version 3.9-0. URL: http://CRAN.R-project.org/package=Hmisc

Hauck, W. & Donner, A. (1977). Wald’s test as applied to hypotheses in logit analysis, Journal of the American Statistical Association 72: 851–853.

Hernán, M., Brumback, B. & Robins, J. (2002). Estimating the causal effect of zidovudine on cd4 count with a marginal structural modelfor repeated measures, Statistics in Medicine 21: 1689–1709.

Hernán, M., Cole, S., Margolick, J., Cohen, M. & Robins, J. (2005). Structural accelerated failure time models for survival analysis in studies with time-varying treatments, Pharmacoepidemiology and Drug Safety 14: 477–491.

Hernán, M. & Robins, J. (2012). Causal Inference, Chapman & Hall/CRC, London.

Hougaard, P. (2000). Analysis of Multivariate Survival Data, Springer, Berlin.

Johansen, S. (1983). An extension of Cox’s regression model, International Statistical Review 51: 165–174.

Kalbfleisch, J. & Prentice, R. (1980). The Statistical Analysis of Failure Time Data, Wiley, Hoboken, N.J.

Kalbfleisch, J. & Prentice, R. (2002). The Statistical Analysis of Failure Time Data, Second edn, Wiley, Hoboken, N.J.

Klein, J. & Moeschberger, M. (2003). Survival Analysis. Techniques for Censored and Truncated Data, Springer-Verlag, New York.

Langholz, B. & Borgan, Ø. (1995). Counter-matching: A stratified nested case-control sampling method, Biometrika 82: 69–79.

Lauritzen, S. (1996). Graphical Models, Oxford Statistical Science Series No. 17, Oxford University Press, Oxford, UK.

Lawless, J. (2003). Statistical Models and Methods for Lifetime Data, Second edn, John Wiley & Sons, Hoboken, N.J.

Makeham, W. (1860). On the law of mortality and the construction of annuity tables, Journal of the Institute of Actuaries and Assurance Magazine 8: 301–310.

Martinussen, T. & Scheike, T. (2006). Dynamic Regression Models for Survival Data, Springer-Verlag, New York.

Nelson, W. (1972). Theory and applications of hazard plotting for censored failure data, Technometrics 14: 945–965.

Parmar, M. & Machin, D. (1995). Survival Analysis: A Practical Approach, John Wiley & Sons, Chichester.

Pearl, J. (2000). Causality: Models, Reasoning and Inference, Cambridge University Press, New York.

Pinheiro, J. & Bates, D. (2000). Mixed-Effects Models in S and S-Plus, Springer-Verlag, New York.

R Development Core Team (2011). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0.
URL: http://www.R-project.org

Robins, J. (1986). A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect, Mathematical Modeling 7: 1393–1512.

Rubin, D. (1974). Estimating causal effects of treatments in randomized and non-randomized studies, Journal of Educational Psychology 66: 688–701.

Scheike, T. & Zhang, M.-J. (2011). Analyzing competing risk data using the R timereg package, Journal of Statistical Software 38(2): 1–15. URL: http://www.jstatsoft.org/v38/i02/

Schweder, T. (1970). Composable Markov processes, Journal of Applied Probability 7: 400–410.

Silverman, B. (1986). Density Estimation, Chapman & Hall, London.

Therneau, T. (2011). coxme: Mixed Effects Cox Models. R package version 2.2-1.
URL: http://CRAN.R-project.org/package=coxme

Therneau, T. & Grambsch, P. (2000). Modeling Survival Data: Extending the Cox Model, Springer-Verlag, New York.

Therneau, T. & original Splus to R port by T. Lumley (2011). survival: Survival Analysis, Including Penalised Likelihood. R package version 2.36-10.
URL: http://CRAN.R-project.org/package=survival

Vaupel, J., Manton, K. & Stallard, E. (1979). The impact of heterogeneity in individual frailty on the dynamics of mortality, Demography 16: 439–454.

Venables, W. & Ripley, B. (2000). S Programming, Springer-Verlag, New York.

Weibull, W. (1951). A statistical distribution function of wide applicability, Journal of Applied Mechanics, Transactions ASME 18: 293–297.

Wright, S. (1921). Correlation and causation, Journal of Agricultural Research 20: 557–585.

Zeilinger, A. (2005). The message of the quantum, Nature 438: 743.