References
- Beyersmann, J., and T. Scheike. 2013. Classical regression models for competing risks. In ed. J. P. Klein, H. C. van Houwelingen, J. G. Ibrahim, and T. H. Scheike, Handbook of survival analysis, 157–77. Boca Raton, FL: Chapman & Hall/CRC.
- Bryson, M. C., and M. E. Johnson. 1981. The incidence of monotone likelihood in the Cox model. Technometrics 23:381–83.
- Chen, M.-H., Q.-M. Shao, and J. G. Ibrahim. 2000. Monte Carlo methods in Bayesian computation. New York, NY: Springer.
- Chen, M.-H., J. G. Ibrahim, and Q.-M. Shao. 2006. Posterior propriety and computation for the Cox regression model with applications to missing covariates. Biometrika 93:791–807.
- Chen, M.-H., J. G. Ibrahim, and Q.-M. Shao. 2009. Maximum likelihood inference for the Cox regression model with applications to missing covariates. Journal of Multivariate Analysis 100: 2018–30.
- Chen, M.-H., M. de Castro, M. Ge, and Y. Zhang. 2013. Bayesian regression models for competing risks. In ed. J. P. Klein, H. C. van Houwelingen, J. G. Ibrahim, and T. H. Scheike, Handbook of survival analysis, 179–98. Boca Raton, FL: Chapman & Hall/CRC.
- Cox, D. R. 1972. Regression models and life-tables. Journal of the Royal Statistical Society B 34:187–220.
- Cox, D. R. 1975. Partial likelihood. Biometrika 62:269–76.
- Fine, J., and B. H. Lindqvist. 2014. Competing risks. Lifetime Data Analysis 20:159–60.
- Firth, D. 1993. Bias reduction of maximum likelihood estimates. Biometrika 80:27–38.
- Gaynor, J. J., E. J. Feuer, C. C. Tan, D. H. Wu, C. R. Little, D. J. Straus, B. D. Clarkson, and M. F. Brennan. 1993. On the use of cause-specific failure and conditional failure probabilities: Examples from clinical oncology data. Journal of the American Statistical Association 88:400–9.
- Ge, M., and M.-H. Chen. 2012. Bayesian inference of the fully specified subdistribution model for survival data with competing risks. Lifetime Data Analysis 18:339–63.
- Greenland, S., and M. A. Mansournia. 2015. Penalization, bias reduction, and default priors in logistic and related categorical and survival regressions. Statistics in Medicine 34:3133–43.
- Heinze, G., and D. Dunkler. 2008. Avoiding infinite estimates of time-dependent effects in small-sample survival studies. Statistics in Medicine 27:6455–69.
- Heinze, G., and M. Ploner. 2002. SAS and SPLUS programs to perform Cox regression without convergence problems. Computer Methods and Programs in Biomedicine 67:217–23.
- Heinze, G., and M. Schemper. 2001. A solution to the problem of monotone likelihood in Cox regression. Biometrics 57:114–19.
- Ibrahim, J. G., and P. W. Laud. 1991. On Bayesian analysis of generalized linear models using Jeffreys’s prior. Journal of the American Statistical Association 86:981–86.
- Kalbfleisch, J. D. 1978. Non-parametric Bayesian analysis of survival time data. Journal of the Royal Statistical Society B 40:214–21.
- Kalbfleisch, J. D., and R. L. Prentice. 2011. The statistical analysis of failure time data, 2nd ed. Hoboken, NJ: Wiley.
- Ploner, M., and G. Heinze. 2010. coxphf: Cox regression with Firth’s penalized likelihood. R package version 1.05 http://CRAN.R-project.org/package=coxphf.
- Roberts, G. O., and J. S. Rosenthal. 2009. Examples of adaptive MCMC. Journal of Computational and Graphical Statistics 18:349–67.
- Roy, V., and J. P. Hobert. 2007. Convergence rates and asymptotic standard errors for Markov chain Monte Carlo algorithms for Bayesian probit regression. Journal of the Royal Statistical Society B, 69:607–623.
- Sinha, D., J. G. Ibrahim, and M.-H. Chen. 2003. A Bayesian justification of Cox’s partial likelihood. Biometrika 90:629–41.
- Tierney, L. 1994. Markov chains for exploring posterior distributions. Annals of Statistics 22:1701–62.
- Zhang, F. 1999. Matrix theory. Basic results and techniques. New York, NY: Springer.