On the role of finite mixture models in survival analysis

McLachlan GJ , Basford KE Mixture models: inference and applications to clustering. New York: Marcel Dekker, 1988.

McGiffin DC , Galbraith AJ , McLachlan GJ et al. Aortic valve infection - risk factors for death and recurrent endocarditis following aortic valve replacement. Journal of Thoracic and Cardiovascular Surgery 1992; 104: 511-20.

Blackstone EH , Naftel DC , Turner ME The decomposition of time-varying hazard into phases, each incorporating a separate stream of concomitant information. Journal of the American Statistical Association 1986; 81: 615-24.

Prentice RL , Kalbfleisch JD , Petersen AV , Flournoy N. , Farewell VT , Breslow NE The analysis of failure times in the presence of competing risks. Biometrics 1978; 34: 541-54.

Larson MG , Dinse GE A mixture model for the regression analysis of competing risks data. Applied Statistics 1985; 34: 201-11.

McLachlan GJ Mixture models and applications. In: Balakrishnan N , Basu AP eds, The exponential distribution: theory, methods and applications, 1994. To appear.

Sinha SK Bayesian estimation of the parameters and reliability function of the mixed exponentially distributed time-censored life test data. Communications in Statistics - Theory and Methods 1983; 12: 141-51.

Sinha SK Bayesian estimation of the parameters and reliability function of a mixture of Weibull life distributions. Journal of Statistical Planning and Inference 1987; 16: 377-87.

Sinha SK , Sloan JA Prediction interval for a mixture of Weibull failure-time distributions - a Bayesian approach. South African Statistical Journal 1989; 23: 119-30.

McLachlan GJ , McGiffin DC , Galbraith AJ , Adams P. Modelling via finite mixtures of time to reoperation following aortic valve replacement. Technical Report. Brisbane: Centre of Statistics, The University of Queensland, 1993.

Farewell VT The use of mixture models for the analysis of survival data with long-term survivors. Biometrics 1982; 38: 1041-46.

Farewell VT Mixture models in survival analysis: are they worth the risk? Canadian Journal of Statistics 1986; 14: 257-62.

Kuk Ayc , Chen C-H. A mixture model combining logistic regression with proportional hazards regression . Biometrica 1992; 79: 531-41.

Kuk Ayc. A semiparametric mixture model for the analysis of competing risks data. Australian Journal of Statistics 1992; 34: 169-80.

Gordon NH Maximum likelihood estimation for mixtures of two Gompertz distributions when censoring occurs. Communications in Statistics - Simulation and Computation 1990; 19: 733-47.

Gordon NH Application of the theory of finite mixtures for the estimation of 'cure' rates of treated cancer patients. Statistics in Medicine 1990; 9: 397-407.

Cantor AB , Shuster JJ Parametric versus non-parametric methods for estimating cure rates based on censored survival data. Statistics in Medicine 1992; 11: 931-37.

Pepe MS , Mori M. Kaplan-Meier, marginal or conditional probability curves in summarizing competing risks failure time data? Statistics in Medicine 1993; 12: 737-51.

Ghitany M. On the information matrix of exponential mixture models with long-term survivors . Biometrical Journal 1993; 35: 15-27.

Boag JQ Maximum likelihood estimates of the proportion of patients cured by cancer therapy. Journal of the Royal Statistical Society B 1949; 11: 15-53.

Berkson J. , Gage RP Survival curve for cancer patients following treatment. Journal of the American Statistical Association 1952; 47: 501-15.

Lui K-J. , Darrow WW , Rutherford GW A model-based estimate of the mean incubation period for AIDS in homosexual men. Science 1988; 240: 1333-35.

Struthers CA , Farewell VT A mixture model for time to AIDS data with left truncation and an uncertain origin. Biometrika 1989; 76: 814-17.

Hjort NL On inference in parametric survival data models. International Statistical Review 1992; 60: 355-87.

Efron B. , Hinkley DV Assessing the accuracy of the maximum likelihood estimator: observed versus expected Fisher information. Biometrika 1978 ; 65: 457-87.

Dempster AP , Laird NM , Rubin DB Maximum likelihood from incomplete data via the EM algorithm (with discussion). Journal of the Royal Statistical Society Series B 1977; 39: 1-38.

Louis TA Finding the observed information matrix when using the EM algorithm. Journal of the Royal Statistical Society B 1982; 44: 226-33.

Meilijson I. A fast improvement to the EM algorithm on its own terms. Journal of the Royal Statistical Society B 1989; 51: 127-38.

Meng XL , Rubin DB Using EM to obtain asymptotic variance-covariance matrices: the SEM algorithm. Journal of the American Statistical Association 1991; 86: 899-909.

Jones PN , McLachlan GJ. Improving the convergence rate of the EM algorithm for a mixture model fitted to grouped truncated data. Journal of Statistical Computation and Simulation 1992; 43: 31-44.

Efron B. Bootstrap methods: another look at the jackknife. Annals of Statistics 1979; 7: 1-26.

Efron B. The jackknife, the bootstrap and other resampling plans. Philadelphia: SIAM, 1982.

Efron B. , Tibshirani RJ An introduction to the bootstrap. London: Chapman and Hall, 1993.

McGiffin DC , O'Brien MF , Galbraith AJ et al. An analysis of risk factors for death and mode-specific death following aortic valve replacement using allograft, xenograft and mechanical valves. Journal of Cardiac and Thoracic Surgery 1993; 106: 895-911.

McLachlan GJ , Adams P. , McGiffin DC , Galbraith AJ Fitting mixtures of Gompertz distributions to censored survival data. Technical Report. Brisbane: Centre for Statistics, The University of Queensland, 1993.