References

Principal texts

1.

Collett, D. (2015), Modelling Survival Data in Medical Research (3rd ed.), Boca Raton, Chapman & Hall/CRC.

2.

Kleinbaum, D. G., and Klein, M. (2012), Survival Analysis: A Self-Learning Text (3rd ed.), New York, Springer.

3.

Klein, J. P., and Moeschberger, M. L. (2003), Survival Analysis: Techniques for Censored and Truncated Data (2nd ed.), New York, Springer.

Recommended texts

1.

Kalbfleisch, J. D., and Prentice, R. L. (2002), The Statistical Analysis of Failure Time Data (2nd ed.), Hoboken, Wiley.

2.

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

3.

Hosmer, D. W., Lemeshow, S., and May, S. (2008), Applied Survival Analysis (2nd ed.), Hoboken, Wiley.

Software

1.

R Core Team (2024), R: A Language and Environment for Statistical Computing, Vienna, R Foundation for Statistical Computing.

2.

Therneau, T. M. (2024), A Package for Survival Analysis in R, R package survival.

3.

Davidson-Pilon, C. (2019), lifelines: survival analysis in Python, Journal of Open Source Software, 4(40), 1317.

Further reading

1.

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

2.

Aalen, O. O., and Johansen, S. (1978), An empirical transition matrix for non-homogeneous Markov chains based on censored observations, Scandinavian Journal of Statistics, 5, 141–150.

3.

Akaike, H. (1974), A new look at the statistical model identification, IEEE Transactions on Automatic Control, 19, 716–723.

4.

Andersen, P. K., and Gill, R. D. (1982), Cox’s regression model for counting processes: a large sample study, Annals of Statistics, 10, 1100–1120.

5.

Bennett, S. (1983), Log-logistic regression models for survival data, Applied Statistics, 32, 165–171.

6.

Breslow, N. E. (1972), Contribution to the discussion of the paper by D. R. Cox, Journal of the Royal Statistical Society B, 34, 216–217.

7.

Brookmeyer, R., and Crowley, J. (1982), A confidence interval for the median survival time, Biometrics, 38, 29–41.

8.

Chiang, C. L. (1984), The Life Table and Its Applications, Malabar, Krieger.

9.

Clayton, D. G. (1978), A model for association in bivariate life tables and its application in epidemiological studies of familial tendency in chronic disease incidence, Biometrika, 65, 141–151.

10.

Cohen, A. C. (1965), Maximum likelihood estimation in the Weibull distribution based on complete and on censored samples, Technometrics, 7, 579–588.

11.

Cox, D. R. (1972), Regression models and life-tables, Journal of the Royal Statistical Society B, 34, 187–220.

12.

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

13.

Cox, D. R., and Snell, E. J. (1968), A general definition of residuals, Journal of the Royal Statistical Society, Series B, 30, 248–275.

14.

Cutler, S. J., and Ederer, F. (1958), Maximum utilization of the life table method in analyzing survival, Journal of Chronic Diseases, 8, 699–712.

15.

Dickson, E. R., Grambsch, P. M., Fleming, T. R., Fisher, L. D., and Langworthy, A. (1989), Prognosis in primary biliary cirrhosis: model for decision making, Hepatology, 10, 1–7.

16.

Edmonson, J. H., Fleming, T. R., Decker, D. G., et al. (1979), Different chemotherapeutic sensitivities and host factors affecting prognosis in advanced ovarian carcinoma versus minimal residual disease, Cancer Treatment Reports, 63, 241–247.

17.

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

18.

Epstein, B., and Sobel, M. (1953), Life testing, Journal of the American Statistical Association, 48, 486–502.

19.

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

20.

Fleming, T. R., and Harrington, D. P. (1984), Nonparametric estimation of the survival distribution in censored data, Communications in Statistics: Theory and Methods, 13, 2469–2486.

21.

Freedman, L. S. (1982), Tables of the number of patients required in clinical trials using the logrank test, Statistics in Medicine, 1, 121–129.

22.

Gehan, E. A. (1965), A generalized Wilcoxon test for comparing arbitrarily singly-censored samples, Biometrika, 52, 203–223.

23.

Gompertz, B. (1825), On the nature of the function expressive of the law of human mortality, Philosophical Transactions of the Royal Society of London, 115, 513–583.

24.

Grambsch, P. M., and Therneau, T. M. (1994), Proportional hazards tests and diagnostics based on weighted residuals, Biometrika, 81, 515–526.

25.

Gray, R. J. (1988), A class of \(K\)-sample tests for comparing the cumulative incidence of a competing risk, Annals of Statistics, 16, 1141–1154.

26.

Greenwood, M. (1926), The natural duration of cancer, Reports on Public Health and Medical Subjects, 33, London, His Majesty’s Stationery Office.

27.

Halley, E. (1693), An estimate of the degrees of the mortality of mankind, Philosophical Transactions of the Royal Society of London, 17, 596–610.

28.

Harrell, F. E., Califf, R. M., Pryor, D. B., Lee, K. L., and Rosati, R. A. (1982), Evaluating the yield of medical tests, Journal of the American Medical Association, 247, 2543–2546.

29.

Harrington, D. P., and Fleming, T. R. (1982), A class of rank test procedures for censored survival data, Biometrika, 69, 553–566.

30.

International Chronic Granulomatous Disease Cooperative Study Group (1991), A controlled trial of interferon gamma to prevent infection in chronic granulomatous disease, New England Journal of Medicine, 324, 509–516.

31.

Kalbfleisch, J. D., and Prentice, R. L. (1973), Marginal likelihoods based on Cox’s regression and life model, Biometrika, 60, 267–278.

32.

Kaplan, E. L., and Meier, P. (1958), Nonparametric estimation from incomplete observations, Journal of the American Statistical Association, 53, 457–481.

33.

Karnofsky, D. A., and Burchenal, J. H. (1949), The clinical evaluation of chemotherapeutic agents in cancer, in C. M. MacLeod (ed.), Evaluation of Chemotherapeutic Agents, New York, Columbia University Press, 191–205.

34.

Kyle, R. A., Therneau, T. M., Rajkumar, S. V., Offord, J. R., Larson, D. R., Plevak, M. F., and Melton, L. J. (2002), A long-term study of prognosis in monoclonal gammopathy of undetermined significance, New England Journal of Medicine, 346, 564–569.

35.

Leung, K.-M., Elashoff, R. M., and Afifi, A. A. (1997), Censoring issues in survival analysis, Annual Review of Public Health, 18, 83–104.

36.

Loprinzi, C. L., et al. (1994), Prospective evaluation of prognostic variables from patient-completed questionnaires, Journal of Clinical Oncology, 12, 601–607.

37.

Mantel, N. (1966), Evaluation of survival data and two new rank order statistics arising in its consideration, Cancer Chemotherapy Reports, 50, 163–170.

38.

Mantel, N., and Haenszel, W. (1959), Statistical aspects of the analysis of data from retrospective studies of disease, Journal of the National Cancer Institute, 22, 719–748.

39.

McGilchrist, C. A., and Aisbett, C. W. (1991), Regression with frailty in survival analysis, Biometrics, 47, 461–466.

40.

Nelson, W. (1969), Hazard plotting for incomplete failure data, Journal of Quality Technology, 1, 27–52.

41.

Peduzzi, P., Concato, J., Feinstein, A. R., and Holford, T. R. (1995), Importance of events per independent variable in proportional hazards regression analysis, Journal of Clinical Epidemiology, 48, 1503–1510.

42.

Peto, R., and Peto, J. (1972), Asymptotically efficient rank invariant test procedures, Journal of the Royal Statistical Society A, 135, 185–207.

43.

Pocock, S. J., Clayton, T. C., and Altman, D. G. (2002), Survival plots of time-to-event outcomes in clinical trials: good practice and pitfalls, The Lancet, 359, 1686–1689.

44.

Prentice, R. L. (1973), Exponential survivals with censoring and explanatory variables, Biometrika, 60, 279–288.

45.

Prentice, R. L., Williams, B. J., and Peterson, A. V. (1981), On the regression analysis of multivariate failure time data, Biometrika, 68, 373–379.

46.

Schemper, M., and Smith, T. L. (1996), A note on quantifying follow-up in studies of failure time, Controlled Clinical Trials, 17, 343–346.

47.

Schoenfeld, D. (1982), Partial residuals for the proportional hazards regression model, Biometrika, 69, 239–241.

48.

Schoenfeld, D. A. (1983), Sample-size formula for the proportional-hazards regression model, Biometrics, 39, 499–503.

49.

Therneau, T. M., Grambsch, P. M., and Fleming, T. R. (1990), Martingale-based residuals for survival models, Biometrika, 77, 147–160.

50.

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

51.

Wei, L. J. (1992), The accelerated failure time model: a useful alternative to the Cox regression model in survival analysis, Statistics in Medicine, 11, 1871–1879.

52.

Wei, L. J., Lin, D. Y., and Weissfeld, L. (1989), Regression analysis of multivariate incomplete failure time data by modeling marginal distributions, Journal of the American Statistical Association, 84, 1065–1073.

53.

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