Classical competing risks

Classical Competing Risks

Volumn , Issue , 2001, Pages 1-186

  Crowder, Martin J ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

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EID: 85057226406     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Book

References (223)

1. Aalen, O.O. (1975) Statistical Inference for a Family of Counting Processes. Ph.D. thesis, University of California, Berkeley.
2. Aalen, O.O. (1976) Nonparametric inference in connection with multiple decrement models. Scand. J. Statist. 3, 15-27.
3. Aalen, O. (1995) Phase type distributions in survival analysis. Scand. J. Statist. 22, 447-463.
4. Albert, J.R.G. and Baxter, L.A. (1995) Applications of the EM algorithm to the analysis of life length data. Appl. Statist. 44, 323-348.
5. Ali, M.M., Mikhail, N.N., and Haq, M.S. (1978) A class of bivariate distributions including the bivariate logistic. J. Multivar. Anal. 8, 405-412.
6. Allen, W.R. (1963) A note on the conditional probability of failure when hazards are proportional. Oper. Res. 11, 658-659.
7. Altshuler, B. (1970) Theory for the measurement of competing risks in animal experiments. Math. Biosci. 6, 1-11.
8. Andersen, P.K. and Gill, R.D. (1982) Cox’s regression model for counting processes: a large sample study. Ann. Statist. 10, 1100-1120.
9. Andersen, P.K. and Borgan, O. (1985) Counting process models for life history data: a review. Scand. J. Statist. 12, 97-158.
10. Andersen, P.K., Borgan, O., Gill, R.D., and Keiding, N. (1993) Statistical Models Based on Counting Processes. Springer-Verlag, New York.
11. Anderson, T.W. and Ghurye, S.G. (1977) Identification of parameters by the distribution of a maximum random variable. J. R. Statist. Soc. B 39, 337-342.
12. Arjas, E. and Greenwood, P. (1981) Competing risks and independent minima: a marked point process approach. Adv. Appl. Probab. 13, 669-680.
13. Arjas, E. and Haara, P. (1984) A marked point process approach to censored failure data with complicated covariates. Scand. J. Statist. 11, 193-209.
14. Arjas, E. (1985) Contribution to Discussion of Andersen and Borgan (1985).
15. Armitage, P. (1959) The comparison of survival curves. J. R. Statist. Soc. A 122, 279-300.
16. Arnold, B. and Brockett, P. (1983) Identifiability for dependent multiple decrement/competing risk models. Scand. Actuar. J. 10, 117-127.
17. Atkinson, A.C. (1985) Plots, Transformations and Regression. Clarendon Press, Oxford.
18. Bagai, I., Deshpande, J.V., and Kochar, S.C. (1989a) A distribution-free test for the equality of failure rates due to two competing risks. Commun. Statist. Theory Methods 18, 107-120.
19. Bagai, I., Deshpande, J.V., and Kochar, S.C. (1989b) Distribution-free tests for stochastic ordering in the competing risks model. Biometrika 76, 775-781.
20. Bancroft, G.A. and Dunsmore, I.R. (1976) Predictive distributions in life tests under competing causes of failure. Biometrika 63, 195-217.
21. Barlow, W.E. and Prentice, R.L. (1988) Residuals for relative risk regression. Biometrika 75, 65-74.
22. Barlow, R.E. and Proschan, F. (1975) Importance of system components and fault tree events. Stoch. Proc. Appl. 3, 153-173.
23. Basu, A.P. (1981) Identifiability problems in the theory of competing and complementary risks — a survey. In Statistical Distributions in Scientific Work, Vol. 2, Taillie, Patil, and Baldesari, Eds., Reidel, Dortrecht, Holland, 335-348.
24. Basu, A.P. and Ghosh J.K. (1978) Identifiability of the multinormal and other distributions under competing risks model. J. Multivar. Anal. 8, 413-429.
25. Basu, A.P. and Ghosh J.K. (1980) Identifiability of distributions under competing risks and complementary risks model. Commun. Statist. Theory Methods A9, 1515-1525.
26. Basu, A.P. and Klein, J.P. (1982) Some recent results in competing risks theory. In Survival Analysis, Vol. 2, J. Crowley and R.A. Johnson, Eds., IMS Monograph Series, Hayward, CA.
27. Beck, G. (1979) Stochastic survival models with competing risks and covariates. Biometrics 35, 427-438.
28. Bedford, T. and Cooke, R.M. (2001) Probabilistic Risk Analysis: Foundations and Methods. Cambridge University Press, Cambridge.
29. Berkson, J. and Elveback, L. (1960) Competing exponential risks, with particular reference to the study of smoking and lung cancer. J. Am. Statist. Assoc. 55, 415-428.
30. Berman, S.M. (1963) Note on extreme values, competing risks and semi-Markov processes. Ann. Math. Stat. 34, 1104-1106.
31. Bernoulli, D. (1760, 1765) Essai d’une nouvelle analyse de la mortalite causee par la petite Verole, et des avantages de l’inoculation pour la prevenir. Mem. Acad. R. Sci., 1760, 1-45.
32. Boag, J.W. (1949) Maximum likelihood estimates of the proportion of patients cured by cancer therapy. J. R. Statist. Soc. B11, 15-44.
33. Boardman, T.J. and Kendell, P.J. (1970) Estimation in compound exponential failure models. Technometrics 12, 891-900.
34. Borgan, O. (1984) Maximum likelihood estimation in parametric counting process models, with applications to censored survival data. Scand. J. Statist. 11, 1-16 (Correction 11, 275).
35. Breslow, N. and Crowley, J. (1974) A large sample study of the life table and product limit estimates under random censorship. Ann. Statist. 2, 437-453.
36. Brooks, S.P. and Morgan, B.J.T. (1995) Optimisation using simulated annealing. Statistician 44, 241-257.
37. Cheng, R.C.H. and Stevens, M.A. (1989) A goodness-of-fit test using Moran’s statistic with estimated parameters. Biometrika 76, 385-392.
38. Chiang, C.L. (1961a) A stochastic study of the life table and its applications: III. The follow-up study with the consideration of competing risks. Biometrics 17, 57-78.
39. Chiang, C.L. (1961b) On the probability of death from specific causes in the presence of competing risks. Proc. 4th Berkeley Symp. 4, 169-180.
40. Chiang, C.L. (1964) A stochastic model of competing risks of illness and competing risks of death. In Stochastic Models in Medicine and Biology, J. Gurland, Ed., University of Wisconsin Press, Madison, 323-354.
41. Chiang, C.L. (1968) Introduction to Stochastic Processes in Biostatics. John Wiley & Sons, New York.
42. Chiang, C.L. (1970) Competing risks and conditional probabilities. Biometrics 26, 767-776.
43. Clayton, D. (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.
44. Clayton, D. and Cuzick, J. (1985) Multivariate generalisations of the proportional hazards model (with discussion). J. R. Statist. Assoc. A 148, 82-117.
45. Collett, D. (1994) Modelling Survival Data in Medical Research. Chapman & Hall, London.
46. Cook, R.D. and Weisburg, S. (1982) Residuals and Influence in Regression. Chapman & Hall, London.
47. Copas, J.B. (1983) Plotting p against x. Appl. Statist. 32, 25-31.
48. Cornfield, J. (1957) The estimation of the probability of developing a disease in the presence of competing risks. Am. J. Public Health 47, 601-607.
49. Couper, D. and Pepe, M.S. (1997) Modelling prevalence of a condition: chronic graft-versus-host disease after bone marrow transplantation. Statist. Med. 16, 1551-1571.
50. Cox, D.R. (1959) The analysis of exponentially distributed lifetimes with two types of failure. J. R. Statist. Soc. B 21, 411-421.
51. Cox, D.R. (1962) Renewal Theory. Methuen, London.
52. Cox, D.R. (1972) Regression models and life tables (with discussion). J. R. Statist. Soc. B 34, 187-220.
53. Cox, D.R. (1975) Partial likelihood. Biometrika 62, 269-276.
54. Cox, D.R. and Hinkley, D.V. (1974) Theoretical Statistics. Chapman & Hall, London.
55. Cox, D.R. and Oakes, D. (1984) Analysis of Survival Data. Chapman & Hall, London.
56. Cox, D.R. and Reid, N. (1987) Parameter orthogonality and approximate conditional inference (with discussion). J. R. Statist. Soc. B 49, 1-39.
57. Crowder, M.J. (1985) A distributional model for repeated failure time measurements. J. R. Statist. Soc. B 47, 447-452.
58. Crowder, M.J. (1986) On consistency and inconsistency of estimating equations. Econ. Theory 2, 305-330.
59. Crowder, M.J. (1989) A multivariate distribution with Weibull connections. J. R. Statist. Soc. B 51, 93-107.
60. Crowder, M.J. (1990) On some nonregular tests for a modified Weibull distribution. Biometrika 77, 499-506.
61. Crowder, M.J. (1991) On the identifiability crisis in competing risks analysis. Scand. J. Statist. 18, 223-233.
62. Crowder, M.J. (1994) Identifiability crises in Competing Risks. Int. Statist. Rev. 62, 379-391.
63. Crowder, M.J., Kimber, A.C., Smith, R.L., and Sweeting, T.J. (1991) Statistical Analysis of Reliability Data. Chapman & Hall, London.
64. Cutler, S. and Ederer, F. (1958) Maximum utilization of the life table method in analyzing survival. J. Chron. Dis. 8, 699-712.
65. D’Alembert, J. (1761) Sur l’application du Calcul des Probabilites a l’inoculation de la petite Verole. Opuscules II, 26-95.
66. David, H.A. (1957) Estimation of means of normal populations from observed minima. Biometrika 44, 282-286.
67. David, H.A. (1970) On Chiang’s proportionality assumption in the theory of competing risks. Biometrics 26, 336-339.
68. David, H.A. (1974) Parametric approaches to the theory of competing risks. In Reliability and Biometry: Statistical Analysis of Lifelength, F. Proschan and R.J. Serfling, Eds., SIAM, Philadelphia, 275-290.
69. David, H.A. and Moeschberger, M.L. (1978) The Theory of Competing Risks. Griffin, London.
70. Davis, T.P. and Lawrance, A.J. (1989) The likelihood for competing risk survival analysis. Scand. J. Statist. 16, 23-28.
71. Desu, M.M. and Narula, S.C. (1977) Reliability estimation under competing causes of failure. In The Theory and Applications of Reliability, Vol. 2, C.P. Tsokos and I.N. Shimi, Eds., Academic Press, New York, 471-481.
72. Dinse, G.E. (1982) Nonparametric estimation for partially-complete time and type of failure. Biometrics 38, 417-431.
73. Dinse, G.E. (1986) Nonparametric prevalence and mortality estimators for animal experiments with incomplete cause-of-death. J. Am. Statist. Assoc. 81, 328-336.
74. Dinse, G.E. (1988) Estimating tumour incidence rates in animal carcinogeneity experiments. Biometrics 44, 405-415.
75. Doganaksoy, N. (1991) Interval estimation from censored and masked failure data. IEEE Trans. Reliab. 40, 280-285.
76. Doob, J.L. (1940) Regularity properties of certain families of chance variables. Trans. Am. Math. Soc. 47, 455-486.
77. Doob, J.L. (1953) Stochastic Processes. John Wiley & Sons, New York.
78. Doyle, A.C. (1950) Silver Blaze. In The Memoirs of Sherlock Holmes. Penguin, London.
79. Ebrahimi, N. (1996) The effects of misclassification of the actual cause of death in competing risks analysis. Statist. Med. 15, 1557-1566.
80. Efron, B. and Hinkley, D.V. (1978) Assessing the accuracy of the maximum likelihood estimator: observed versus expected Fisher information. Biometrika 65, 457-487.
81. Elandt-Johnson, R. (1976) Conditional failure time distributions under competing risk theory with dependent failure times and proportional hazard rates. Scand. Actuar. J. 59, 37-51.
82. Elandt-Johnson, R.C. and Johnson, N.L. (1980) Survival Models and Data Analysis. John Wiley & Sons, New York.
83. Elveback, L. (1958) Estimation of survivorship in chronic disease; the actuarial method. J. Am. Statist. Assoc. 53, 420-440.
84. Farragi, D. and Korn, E.L. (1996) Competing risks with frailty models when treatment affects only one failure type. Biometrika 83, 467-471.
85. Faulkner, J.E. and McHugh, R.B. (1972) Bias in observable cancer age and life-time of mice subject to spontaneous mammary carcinomas. Biometrics 28, 489-498.
86. Finch, P.D. (1977) On the crude analysis of survivorship data. Austr. J. Statist. 19, 1-21.
87. Fisher, L. and Kanarek, P. (1974) Presenting censored survival data when censoring and survival times may not be independent. In Reliability and Biometry: Statistical Analysis of Lifelength, F. Proschan and R.J. Serfling, Eds., SIAM, Philadelphia, 303-326.
88. Fix, E. and Neyman, J. (1951) A simple stochastic model of recovery, relapse, death and loss of patients. Hum. Biol. 23, 205-241.
89. Fleming, T.R. and Harrington, D.P. (1991) Counting Processes and Survival Models. John Wiley & Sons, New York.
90. Flinn, C.J. and Heckman, J.J. (1982) New methods for analyzing structural models of labor force dynamics. J. Econometrics 18, 115-168.
91. Flinn, C.J. and Heckman, J.J. (1983a) Are unemployment and out of the labor force behaviourally distinct labor force states?, J. Labor Econ. 1, 28-42.
92. Flinn, C.J. and Heckman, J.J. (1983b) The likelihood function for the multistate-multi-episode model in ’models for the analysis of labor force dynamics.’ In Advances in Econometrics, Vol. 3, R. Bassman and G. Rhodes, Eds., JAI Press, Greenwich, CT, 115-168.
93. Freund, J.E. (1961) A bivariate extension of the exponential distribution. J. Am. Statist. Assoc. 56, 971-977.
94. Gail, M. (1975) A review and critique of some models used in competing risks analysis. Biometrics 31, 209-222.
95. Gail, M., Lubin, J.H., and Rubenstein, L.V. (1981) Likelihood calculations for matched case-control studies and survival studies with tied death times. Biometrika 68, 703-707.
96. Galambos, J. (1978) The Asymptotic Theory of Extreme Order Statistics. John Wiley & Sons, New York.
97. Gasemyr, J. and Natvig, B. (1994) Bayesian Estimation of Component Lifetimes Based on Autopsy Data. Statistical Research Report No. 8, Institute of Mathematics, University of Oslo.
98. Gelfand, A.E. and Smith, A.F.M. (1990) Sampling-based approaches to calculating marginal densities. J. Am. Statist. Assoc. 85, 398-409.
99. Geyer, C.J. (1992) Practical Markov chain Monte Carlo. Statist. Sci. 7, 473-511.
100. Gilks, W.R., Richardson, S., and Spiegelhalter, D.J. (1996) Markov Chain Monte Carlo in Practice. Chapman & Hall, London.
101. Gill, R.D. (1984) Understanding Cox’s regression model: a martingale approach. J. Am. Statist. Assoc. 79, 441-447.
102. Gill, R.D. and Johansen, S. (1990) A survey of product integration with a view towards application in survival analysis. Ann. Statist. 18, 1501-1555.
103. Gill, P.E., Murray, W., and Wright, M.H. (1981) Practical Optimization. Academic Press, London.
104. Goetghebeur, E. and Ryan, L. (1990) A modified log rank test for competing risks with missing failure type. Biometrika 77, 207-211.
105. Goetghebeur, E. and Ryan, L. (1995) Analysis of competing risks survival data when some failure types are missing. Biometrika 82, 821-833.
106. Gompertz, B. (1825) On the nature of the function expressive of the law of human mortality. Philos. Trans. R. Soc. London 115, 513-583.
107. Grambsch, P.M. and Therneau, T.M. (1994) Proportional hazards tests and diagnostics based on weighted residuals. Biometrika 81, 515-526.
108. Greenwood, M. (1926) The errors of sampling of the survivorship tables. In Reports on Public Health and Statistical Subjects, No. 33, Appendix 1. Her Majesty’s Stationary Office, London.
109. Greville, T.N.E. (1948) Mortality tables analyzed by cause of death. Rec. Am. Inst. Actuaries 37, 283-294.
110. Greville, T.N.E. (1954) On the formula for the L-function in a special mortality table eliminating a given cause of death. Trans. Soc. Actuar. 6, 1-5.
111. Gross, A.J. (1970) Minimization of misclassification of component failure in a two-component system. IEEE Trans. Reliab. 19, 120-122.
112. Gross, A.J. (1973) A competing risk model: a one organ subsystem plus a two organ subsystem. IEEE Trans. Reliab. 22, 24-27.
113. Gross, A.J., Clark, V.A., and Liu, V. (1971) Estimation of survival parameters when one of two organs must function for survival. Biometrics 27, 369-377.
114. Guess, F.M., Usher, J.S., and Hodgson, T.J. (1991) Estimating system and component reliabilities under partial information on cause of failure. J. Statist. Plann. Inf. 29, 75-85.
115. Gumbel, E.J. (1960) Bivariate exponential distributions. J. Am. Statist. Assoc. 55, 698-707.
116. Guttman, I., Lin, D.K.J., Reiser, B., and Usher, J.S. (1995) Dependent masking and system life data: Bayesian inference for two-component systems. Lifetime Data Anal. 1, 87-100.
117. Heckman, J.J. and Honore, B.E. (1989) The identifiability of the competing risks model. Biometrika 76, 325-330.
118. Herman, R.J. and Patell, R.K.N. (1971) Maximum likelihood estimation for multi-risk model. Technometrics 13, 385-396.
119. Hoel, D.G. (1972) A representation of mortality data by competing risks. Biometrics 28, 475-488.
120. Holt, J.D. (1978) Competing risk analyses with special reference to matched pair experiments. Biometrika 65, 159-165.
121. Hougaard, P. (1984) Life table methods for heterogeneous populations: distributions describing the heterogeneity. Biometrika 71, 75-83.
122. Hougaard, P. (1986a) Survival models for heterogeneous populations derived from stable distributions. Biometrika 73, 387-396.
123. Hougaard, P. (1986b) A class of multivariate failure time distributions. Scand. J. Statist 14, 291-304.
124. Johnson, N.L. and Kotz, S. (1972) Distributions in Statistics: Continuous Multivariate Distributions. John Wiley & Sons, New York.
125. Kalbfleisch, J.D. and Sprott, D.A. (1970) Application of likelihood methods to models involving large numbers of parameters (with discussion). J. R. Statist. Soc. B 32, 175-208.
126. Kalbfleisch, J.D. and Mackay, R.J. (1979) On constant-sum models for censored survival data. Biometrika 66, 87-90.
127. Kalbfleisch, J.D. and Prentice, R.L. (1980) The Statistical Analysis of Failure Time Data. John Wiley & Sons, New York.
128. Kaplan, E.L. and Meier, P. (1958) Nonparametric estimation from incomplete observations. J. Am. Statist. Assoc. 53, 457-481.
129. Karia, S.R. and Deshpande, J.V. (1997) Bounds for Hazard Gradients and Rates in the Competing Risks Set Up. Technical Report 285, Department of Statistics, University of Michigan, Ann Arbor.
130. Karn, M.N. (1931) An inquiry into various death-rates and the comparative influence of certain diseases on the duration of life. Ann. Eugenics 4, 279-326.
131. Karn M.N. (1933) A further study of methods of constructing life tables when certain causes of death are eliminated. Biometrika 25, 91.
132. Kay, R. (1986) Treatment effects in Competing Risks analysis of prostate cancer data. Biometrics 42, 203-211.
133. Kimball, A.W. (1958) Disease incidence estimation in populations subject to multiple causes of death. Bull. Int. Statist. Inst. 36, 193-204.
134. Kimball, A.W. (1969) Models for the estimation of competing risks from grouped data. Biometrics 25, 329-337.
135. Kimball, A.W. (1971) Model I vs. Model II in competing risk theory. Biometrics 27, 462-465.
136. King, J.R. (1971) Probability Charts for Decision Making. Industrial Press, New York.
137. Klein, J.P. and Basu, A.P. (1981) Weibull accelerated life tests when there are competing causes of failure. Commun. Statist. Theory Methods A 10, 2073-2100.
138. Kochar, S.C. and Proschan, F. (1991) Independence of time and cause of failure in the multiple dependent competing risks model. Statistica Sin. 1, 295-299.
139. Kodell, R.L. and Chen, J.J. (1987) Handling cause of death in equivocal cases using the EM algorithm. With rejoinder. Commun. Statist. A 16, 2565-2585.
140. Krall, J.M. and Hickman, J.C. (1970) Adjusting multiple decrement tables. Trans. Soc. Actuar. 22, 163-179.
141. Lagakos, S.W. (1978) A covariate model for partially censored data subject to competing causes of failure. Appl. Statist. 27, 235-241.
142. Lagakos, S.W. and Louis, T.A. (1988) Use of tumour lethality to interpret tumorige-nicity experiments lacking cause-of-death data. Appl. Statist. 37, 169-179.
143. Lagakos, S.W. and Williams, J.S. (1978) Models for censored survival analysis: a cone class of variable-sum models. Biometrika 65, 181-189.
144. Langberg, N.A. and Proschan, F. (1979) A reliability growth model involving dependent components. Ann. Probab. 7, 1082-1087.
145. Langberg, N., Proschan, F., and Quinzi, A.J. (1978) Converting dependent models into independent ones, preserving essential features. Ann. Probab. 6, 174-181.
146. Langberg, N., Proschan, F., and Quinzi, A.J. (1981) Estimating dependent life lengths, with applications to the theory of competing risks. Ann. Statist. 9, 157-167.
147. Langberg, N. and Shaked, M. (1982) On the identifiability of multivariate life distribution functions. Ann. Probab. 10, 773-779.
148. Larson, M.G. and Dinse, G.E. (1985) A mixture model for the regression analysis of competing risks data. Appl. Statist. 34, 201-211.
149. Lawless, J.F. (1982) Statistical Models and Methods for Lifetime Data. John Wiley & Sons, New York.
150. Lawless, J.F. (1983) Statistical methods in reliability (with comments). Technometrics 25, 305-335.
151. Lee, L. and Thompson, W.A., Jr. (1974) Results on failure time and pattern for the series system. In Reliability and Biometry: Statistical Analysis of Lifelength, F. Proschan and R.J. Serfling, Eds., SIAM, Philadelphia, 291-302
152. Lin, D.K.J. and Guess, F.M. (1994) System life data analysis with dependent partial knowledge on the exact cause of system failure. Microelec. Reliab. 34, 535-544.
153. Lodge, D. (1990) Famous Cricketers Series No. 15: W.G. Grace. Association of Cricket Statisticians and Historians, West Bridgford, Nottingham, U.K.
154. Magaluri, G. and Zhang, C.-H. (1994) Estimation in the mean residual lifetime regression model. J. R. Statist. Soc. 56, 477-489.
155. Makeham, W.M. (1860) On the law of mortality and the construction of annuity tables. J. Inst. Actuaries 8, 301-310.
156. Makeham, W.M. (1874) On an application of the theory of the composition of decremental forces. J. Inst. Actuaries 18, 317-322.
157. Marshall, A.W. and Olkin, I. (1967a) A generalized bivariate exponential distribution. J. Appl. Probab. 4, 291-302.
158. Marshall, A.W. and Olkin, I. (1967b) A multivariate exponential distribution. J. Am. Statist. Assoc. 62, 30-44.
159. Martz, H.F. and Waller, R.A. (1982) Bayesian Reliability Analysis. John Wiley & Sons, New York.
160. Matthews, D.E. (1984) Efficiency considerations in the analysis of a competing risk problem. Can. J. Statist. 12, 207-210.
161. Meier, P. (1975) Estimation of a distribution function from incomplete observations. In Perspectives in Probability and Statistics, J. Gani, Ed., Applied Probability Trust, Sheffield, 67-87.
162. Mendenhall, W. and Hader, R.J. (1958) Estimation of parameters of mixed exponentially distributed failure time distributions from censored life test data. Biometrika 45, 504-520.
163. Miller, D.R. (1977) A note on independence of multivariate lifetimes in competing risk models. Ann. Statist. 5, 576-579.
164. Miyakawa, M. (1984) Analysis of incomplete data in a competing risks model. IEEE Trans. Reliab. 33, 293-296.
165. Moeschberger, M.L. (1974) Life tests under dependent competing causes of failure. Technometrics 16, 39-47.
166. Moeschberger, M.L. and David, H.A. (1971) Life tests under competitive causes of failure and the theory of competing risks. Biometrics 27, 909-933.
167. Morgan, B.J.T. (1995) Elements of Simulation. Chapman & Hall, London.
168. Nadas, A. (1970a) On proportional hazard functions. Technometrics 12, 413-416.
169. Nadas, A. (1970b) On estimating the distribution of a random vector when only the smallest co-ordinate is observable. Technometrics 12, 923-924.
170. Nadas, A. (1971) The distribution of the identified minimum of a normal pair determines the distribution of the pair. Technometrics 13, 201-202.
171. NAG (1988) Library Manual Mark 13. Numerical Algorithms Group, Oxford.
172. Nair, V.J. (1993) Bounds for reliability estimation under dependent censoring. Int. Statist. Rev. 61, 169-182.
173. Nelson, W.B. (1970) Hazard plotting methods for analysis of life data with different failure modes. J. Qual. Technol. 2, 126-149.
174. Nelson, W. (1982) Applied Life Data Analysis. John Wiley & Sons, New York.
175. Nelson, W. (1990) Accelerated Testing: Statistical Models, Test Plans and Data Analysis. John Wiley & Sons, New York.
176. Oakes, D. (1981) Survival times: aspects of partial liklihood. Int. Statist. Rev. 49, 235-264.
177. Oakes, D. (1989) Bivariate survival models induced by frailties. J. Am. Statist. Assoc. 84, 487-493.
178. Oakes, D. and Dasu, T. (1990) A note on residual life. Biometrika 77, 409-410.
179. Peacock, S.T., Keller, A.Z., and Locke, N.J. (1985) Reliability predictions for diesel generators. Reliability ’85, 4C/5/1–4C/5/12.
180. Pepe, M.S. (1991) Inference for events with dependent risks in multiple endpoint studies. J. Am. Statist. Assoc. 86, 770-778.
181. Pepe, M.S. and Mori, M. (1993) Kaplan-Meier, marginal or conditional probability curves in summarizing competing risks failure time data? Statist. Med. 12, 737-751.
182. Peterson, A.V. (1976) Bounds for a joint distribution function with fixed sub-distribution functions: application to competing risks. Proc. Natl. Acad. Sci. U.S.A. 73, 11-13.
183. Peterson, A.V. (1977) Expressing the Kaplan-Meier estimator as a function of empirical sub-survival functions. J. Am. Statist. Assoc. 72, 854-858.
184. Peto, R. (1972) Contribution to discussion of paper by D.R. Cox. J. R. Statist. Soc. B 34, 205-207.
185. Peto, R. and Lee, P. (1973) Weibull distributions for continuous carcinogenesis experiments. Biometrics 29, 457-470.
186. Peto, R. and Peto, J. (1972) Asymptotically efficient rank invariant test procedures (with discussion). J. R. Statist. Soc. 135, 185-206.
187. Pike, M.C. (1966) A method of analysis of a certain class of experiments in carcinogenesis. Biometrics 22, 142-161.
188. Pike, M.C. (1970) A note on Kimball’s paper “Models for the estimation of competing risks from grouped data.” Biometrics 26, 579-581.
189. Pike, M.C. and Roe, F.J.C. (1963) An actuarial method of analysis in an experiment in two-stage carcinogenesis. Br. J. Cancer 17, 605-610.
190. Prentice, R.L. and El Shaarawi, A. (1973) A model for mortality rates and a test of fit for the Gompertz force of mortality. Appl. Statist. 22, 301-314.
191. Prentice, R.L., Kalbfleisch, J.D., Peterson, A.V., Flournoy, N., Farewell, V.T., and Breslow, N.E. (1978) The analysis of failure times in the presence of competing risks. Biometrics 34, 541-554.
192. Proschan, F. and Sullo, P. (1974) Estimating parameters of a bivariate exponential distribution in several sampling situations. In Reliability and Biometry: Statistical Analysis of Lifelength. SIAM, Philadelphia, 423-440.
193. Rachev, S.T. and Yakovlev, A.Y. (1985) Some problems of the competing risks theory. In 5th Int. Summer School on Probability Theory and Math. Statist., Varna, 1985. Bulgarian Academy of Science, Sofia.
194. Racine-Poon, A.H. and Hoel, D.G. (1984) Nonparametric estimation of the survival function when the cause of death is uncertain. Biometrics 40, 1151-1158.
195. Reiser, B., Guttman, I., Lin, D.K.J., Guess, F.M., and Usher, J.S. (1995) Bayesian inference for masked system lifetime data. Appl. Statist. 44, 79-90.
196. Ripley, B.D. (1987) Stochastic Simulation. John Wiley & Sons, New York.
197. Ross, G.J.S. (1970) The efficient use of function minimization in nonlinear maximum likelihood estimation. Appl. Statist. 19, 205-221.
198. Sampford, M.R. (1952) The estimation of response-time distributions. II. Multi-stimulus distributions. Biometrics 8, 307-369.
199. Schoenfeld, D. (1982) Partial residuals for the proportional hazards regression model. Biometrika 69, 239-241.
200. Seal, H.L. (1954) The estimation of mortality and other decremental probabilities. Skand. Aktuarietidskr. 37, 137-162.
201. Seal, H.L. (1977) Studies in the history of probability and statistics. XXXV. Multiple decrements or competing risks. Biometrika 64, 429-439.
202. Self, S.G. and Liang, K. (1987) Asymptotic properties of maximum likelihood estimators and likelihood ratio tests under nonstandard conditions. J. Am. Statist. Assoc. 82, 605-610.
203. Sen, P.K. (1974) Nonparametric Tests for Interchangeability Under Competing Risks. Inst. Statist. Mimeo Ser. No. 905, University of North Carolina, Chapel Hill.
204. Sethuraman, J. (1965) On a characterization of the three limiting types of the extreme. Sankhya A 27, 357-364.
205. Shaked, M. (1982) A general theory of some positive dependence notions. J. Multivar. Anal. 12, 199-218.
206. Slud, E.V. and Rubenstein, L.V. (1983). Dependent competing risks and summary survival curves. Biometrika 70, 643-649.
207. Smith, A.F.M. (1991) Bayesian computational methods. Philos. Trans. R. Soc. London Ser. A 337, 369-386.
208. Smith, A.F.M. and Roberts, G.O. (1993) Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods. J. R. Statist. Soc. B 55, 3-23.
209. Takahasi, K. (1965) Note on the multivariate Burr’s distribution. Ann. Inst. Statist. Math. 17, 257-260.
210. Therneau, T.M., Grambsch, P.M., and Fleming, T.R. (1990) Martingale-based residuals for survival models. Biometrika 77, 147-160.
211. Tsiatis, A. (1975) A nonidentifiability aspect of the problem of competing risks. Proc. Natl. Acad. Sci. U.S.A. 72, 20-22.
212. Usher, J.S. and Hodgson, T.J. (1988) Maximum likelihood analysis of component reliability using masked system life-test data. IEEE Trans. Reliab. 37, 550-555.
213. Usher, J.S. and Guess, F.M. (1989) An iterative approach for estimating component reliability from masked system life data. Qual. Reliab. Eng. Inst. 5, 257-261.
214. 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.
215. Walker, S.G., Damien, P., Land, P.W., and Smith, A.F.M. (1999) Bayesian nonparametric inference for random distributions and related functions (with discussion). J. R. Statist. Soc. B 61, 485-527.
216. Wang, O. (1977) A competing risk model based on the life table procedure in epidemiologic studies. Int. J. Epidemiol. 6, 153-159.
217. Watson, A.S. and Smith, R.L. (1985) An examination of statistical theories for fibrous materials in the light of experimental data. J. Mater. Sci. 20, 3260-3270.
218. Williams, D. (1991) Probability with Martingales. Cambridge University Press, Cambridge.
219. Williams, J.S. and Lagakos, S.W. (1976) Independent and dependent censoring mechanisms. Proc. 9th Int. Biometric Conf., 1, 408-427.
220. Williams, J.S. and Lagakos, S.W. (1977) Models for censored survival analysis: constant-sum and variable-sum models. Biometrika 64, 215-224.
221. Wong, W.H. (1986) Theory of partial likelihood. Ann. Statist. 14, 88-123.
222. Yashin, A.I., Manton, K.G., and Stallard, E. (1986) Dependent competing risks: a stochastic process model. J. Math. Biol. 24, 119-164.
223. Zheng, M. and Klein, J.P. (1995) Estimates of marginal survival for dependent competing risks based on an assumed copula. Biometrika 82, 127-138.