Short and Long Term Survival Analysis in Oncological Research

  • Ettore Marubini
Part of the Boston Studies in the Philosophy of Science book series (BSPS, volume 122)


Chronic diseases and cancer among these are, nowadays, the main concern of medical research in developed countries. Chronic disease has several characteristics which lead to its complexity and make it difficult to trace its ‘natural history’ and to design clinical trials aiming at evaluating the effectivness of therapeutical strategies. Among these characteristics there are a multifactorial aetiology, nearly non specific and often exogenous, and a long time course with continuous interactions between the host biological system and the surrounding environment. As a consequence, clinical researches accomplished to study cancer treatment usually last a considerable time period and sometimes, as for instance in breast cancer, several years. Their duration may be partitioned into a recruitment stage in which different treatments are administered and a stage of continued observation after accrual has stopped. During both these stages a great amount of information concerning variables possibly capable of influencing the duration, course and outcome of the disease (i.e. prognostic factors) has to be collected ad hoc or has to be attained from data gathered as a subsidiary aspect of an ongoing study. Due allowance for these factors must be made in the statistical analysis performed to evaluate the treatments effectivness in order to enlighten their prognostic role and to make the analysis more sensitive (Armitage and Gehan, 1974; Marubini et al., 1983; Harrel et al., 1985).


Breast Cancer Hazard Function Relative Survival Multiplicative Model Partial Likelihood 
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  1. Aalen, O. 1987. ‘Nonparametric Inference for a Family of Counting Processes’. Ann. Stat. 6 701–726.MathSciNetCrossRefGoogle Scholar
  2. Andersen, P. K., Borch-Johnsen, K., Deckert, T., Green, A., Hongaard, P., Keiding, N., and Kreiner, S. 1985. ‘A Cox Regression Model for the Relative Mortality and its Application to Diabetes Mellitus Survival Data’. Biometrics 41 921–932.CrossRefGoogle Scholar
  3. Aranda-Ordaz, F. J. 1983. ‘An Extension of the Proportional-Hazards Model for Grouped Data’. Biometrics 39 109–117.MathSciNetzbMATHCrossRefGoogle Scholar
  4. Armitage, P. and Gehan, E. A. 1974. ‘Statistical Methods for the Identification and Use of Prognostic Factors’, Cancer 13 16–36.Google Scholar
  5. Berkson, J. and Gage, R. P. 1950. ‘Calculation of Survival Rates for Cancer’. Staff Meet. Mayo Clin 25 270–286.Google Scholar
  6. Breslow N. E. 1974. ‘Covariance Analysis of Censored Survival Data’. Biometrics 30 89–99.CrossRefGoogle Scholar
  7. Breslow, N. E., Lubin, J. H., Marek, P., and Langholz, B. 1983. ‘Multiplicative Models and Cohort Analysis. J.A.S.A. 78 1–12.zbMATHGoogle Scholar
  8. Cox, D. R. 1972. ‘Regression Models and Life Tables (with Discussion)’. J.R.S.S.B 34 187–220.zbMATHGoogle Scholar
  9. Cox, D. R. 1975. ‘Partial Likelihood. Biometrika 62 269–276.MathSciNetzbMATHCrossRefGoogle Scholar
  10. Efron B. 1977. ‘The Efficiency of Cox’s Likelihood Function for Censored Data. J.A.S.A. 72 555–565.MathSciNetGoogle Scholar
  11. Gehan, E. A. 1965. ‘A Generalized Wilcoxon Test for Comparing Arbitrarily Singly-Censored Samples’. Biometrika 52 203–223.MathSciNetzbMATHGoogle Scholar
  12. Gore, S., Pocock, S., and Kerr, G. R. 1984. ‘Regression Models and Non-Proportional Hazards in the Analysis of Breast Cancer Survival’. Appl Statistics 33 176–195.CrossRefGoogle Scholar
  13. Hakulinen, T. and Tenkanen, L. 1987. ‘Regression Analysis of Relative Survival Rates’. Appl. Statistics 36 309–317.CrossRefGoogle Scholar
  14. Hakulinen, T., Tenkanen, L., Abeywickrama K., and Paivarinta L. 1987. ‘Testing Equality of Relative Survival Patterns Based on Aggregated Data’. Biometrics 43 313–325.zbMATHCrossRefGoogle Scholar
  15. Harrel, F. E., Lee, K. L., Matchar, D. B., and Reichert, T. A. 1985. ‘Regression Models for Prognostic Prediction: Advantages, Problems and Suggested Solutions’. Canc. Treat Rep. 69 1071–1077.Google Scholar
  16. Hill, C., Laplanche, A., and Rezvani, A. 1985. ‘Comparison of the Mortality of a Cohort with the Mortality of a Reference Population in a Prognostic Study’. Stat. Med. 4 295–302.CrossRefGoogle Scholar
  17. Kalbfleisch, J. D. and Prentice, R. L. 1980. The Statistical Analysis of Failure Time Data. New York: J. Wiley and Sons.zbMATHGoogle Scholar
  18. Kaplan, E. L. and Meier, P. 1958. ‘Nonparametric Estimation from Incomplete Observations’. J.A.S.A. 53 457–481.MathSciNetzbMATHGoogle Scholar
  19. Kay, R. 1984. ‘Goodness of Fit Methods for the Proportional Hazards Regression Model: A Review’. Rev. Epidém. Sante Pub. 32 185–198.Google Scholar
  20. Lawless, J. F. 1982. Statistical Models and Methods for Lifetime Data. New York: J. Wiley and Sons.zbMATHGoogle Scholar
  21. LIFEREG procedure (The) (1985). In: SAS User’s Guide: Statistics. 507–528.Google Scholar
  22. Mantel, N. 1966. ‘Evaluation of Survival Data and Two New Rank Order Statistics Arising in its Consideration;Canc. Chemother. Rep. 50 163–170.Google Scholar
  23. Marubini, E., Morabito, A., and Valsecchi, M. G. 1983. ‘Prognostic Factors and Risk Groups: Some Results Given by Using an Algorithm Suitable for Censored Survival Data’. Stat. Med. 2 295–303.CrossRefGoogle Scholar
  24. Marubini E. and Valsecchi M. G. 1987. Analisi della sopravvivenza in sperimentazioni cliniche controllate e nelle osservazioni pianificate. Monografia N 9 del Centro Zambon dell’Università degli studi di Milano.Google Scholar
  25. Mezzanotte, G., Boracchi P., Valagussa, P., and Marubini, E. 1987a. ‘Analisi della sopravvivenza a lungo termine nel carcinoma mammario’ Rivista di Stai Appi 20 251–267.Google Scholar
  26. Mezzanotte G., Boracchi, P., Marubini E., Valagussa P. and Veronesi, U. 1987b. ‘Long Term Prognosis and Curability of Breast Cancer’. Proceedings of the 8th International Meeting on Clinical Bio statistics, Gotheborg, 7–11 September.Google Scholar
  27. Oakes, D. 1972. ‘Contribution to the Discussion of Paper by D. R. Cox.’ J.R.S.S. B, 34 202–205.Google Scholar
  28. Oakes, D. 1981. ‘Survival Times: Aspects of Partial Likelihood’Int. Stat. Rev. 49 235–264.MathSciNetzbMATHCrossRefGoogle Scholar
  29. O’Quigley, J. 1982. ‘Regression Models and Survival Prediction’. The Statistician 31 107–116.CrossRefGoogle Scholar
  30. Pocock, S. J., Gore, S. M., and Kerr, G. R. 1982. ‘Long Term Survival Analysis: the Curability of Breast Cancer’. Stat. Med. 1 93–104.CrossRefGoogle Scholar
  31. Tibshirani, R. J. and Ciampi, A. 1983. ‘A Family of Proportional and Additive-Hazards Models for Survival Data’ Biometrics 39 141–147.MathSciNetCrossRefGoogle Scholar
  32. Union International Contre Le Cancer 1978. TNM Classification of Tumors. Geneva.Google Scholar
  33. Valagussa, P., Bonadonna, G., and Veronesi, U. 1978. ‘Patterns of Relapse and Survival Following Radical Mastectomy. Analysis of 716 Consecutive Patients’. Cancer 41 1171–1178.CrossRefGoogle Scholar
  34. Veronesi, U. and Valagussa, P. 1981. ‘Inefficacy of Internal Mammary Nodes Dissection in Breast Cancer Surgery. Cancer 47 170–175.CrossRefGoogle Scholar

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© Kluwer Academic Publishers 1990

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  • Ettore Marubini

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