References
- Andersen, P. K., and R. D. Gill. 1982. Cox’s regression model for counting processes: A large sample study. The Annals of Statistics 10 (4):1100–20. doi:https://doi.org/10.1214/aos/1176345976.
- Breslow, N. E. 1972. Discussion of the paper by D. R. Cox. Journal of the Royal Statistical Society, Sereies B 34:216–17.
- Buckley, J. D. 1984. Additive and multiplicative models for relative survival data. Biometrics 40 (1):51–62. doi:https://doi.org/10.2307/2530743.
- Bulpitt, C. J. 1988. Subgroup analysis. Lancet (London, England) 2 (8601):31–34. doi:https://doi.org/10.1016/S0140-6736(88)92956-X.
- Cai, J., and D. Zeng. 2004. Sample size/power calculation for case-cohort studies. Biometrics 60 (4):1015–24. doi:https://doi.org/10.1111/j.0006-341X.2004.00257.x.
- Cai, J., and D. Zeng. 2007. Power calculation for case-cohort studies with nonrare events. Biometrics 63 (4):1288–95. doi:https://doi.org/10.1111/j.1541-0420.2007.00838.x.
- Chen, K., and S. H. Lo. 1999. Case-cohort and case-control analysis with Cox’s model. Biometrika 86 (4):755–64. doi:https://doi.org/10.1093/biomet/86.4.755.
- Cox, D. R. 1972. Regression models and life tables. Journal of the Royal Statistical Society: Series B (Methodological) 34 (2):187–220. doi:https://doi.org/10.1111/j.2517-6161.1972.tb00899.x.
- Cox, D. R. 1975. Partial likelihood. Biometrika 62 (2):269–76. doi:https://doi.org/10.1093/biomet/62.2.269.
- Cox, D. R., and D. Oakes. 1984. Analysis of survival data. London: Chapman & Hall.
- Fleming, T. R., and D. P. Harrington. 1991. Counting processes and survival analysis. New York: Wiley-Interscience.
- Gail, M., and R. Simon. 1985. Testing for qualitative interactions between treatment effects and patient subsets. Biometrics 41 (2):361–72. doi:https://doi.org/10.2307/2530862.
- Hansen, L. P. 1982. Large sample properties of generalized method of moments estimators. Econometrica 50 (4):1029–54. doi:https://doi.org/10.2307/1912775.
- Huang, C. Y., J. Qin, and H. T. Tsai. 2016. Efficient estimation of the Cox model with auxiliary subgroup survival information. Journal of the American Statistical Association 111 (514):787–99. doi:https://doi.org/10.1080/01621459.2015.1044090.
- Kalbfleisch, J. D., and J. F. Lawless. 1988. Likelihood analysis of multi-state models for disease incidence and mortality. Statistics in Medicine 7 (1–2):149–60. doi:https://doi.org/10.1002/sim.4780070116.
- Kalbfleisch, J. D., and R. L. Prentice. 2002. The statistical analysis of failure time data. New York: John Wiley and Sons.
- Kong, L., and J. Cai. 2009. Case-cohort analysis with accelerated failure time model. Biometrika 65 (1):135–42. doi:https://doi.org/10.1111/j.1541-0420.2008.01055.x.
- Kulich, M., and D. Y. Lin. 2000. Additive hazard regression for case-cohort studies. Biometrika 87 (1):73–87. doi:https://doi.org/10.1093/biomet/87.1.73.
- Lamm, W., C. Natter, S. Schur, W. J. Kostler, A. Reinthaller, M. Krainer, C. Grimm, R. Horvath, G. Amann, P. Funovics, et al. 2014. Distinctive outcome in patients with non-uterine and uterine leiomyosarcoma. BMC Cancer 14 (1):981. doi:https://doi.org/10.1186/1471-2407-14-981.
- Lin, D. Y., and Z. L. Ying. 1993. Cox regression with incomplete covariate measurements. Journal of the American Statistical Association 88 (424):1341–49. doi:https://doi.org/10.1080/01621459.1993.10476416.
- Lin, D. Y., and Z. L. Ying. 1994. Semiparametric analysis of the additive risk model. Biometrika 81 (1):61–71. doi:https://doi.org/10.1093/biomet/81.1.61.
- Negassa, A., A. Ciampi, M. Abrahamowicz, S. Shapiro, and J. F. Boivin. 2005. Tree-structured subgroup analysis for censored survival data: Validation of computationally inexpensive model selection criteria. Statistics and Computing 15 (3):231–39. doi:https://doi.org/10.1007/s11222-005-1311-z.
- Prentice, R. L. 1986. A case-cohort design for epidemiologic cohort studies and disease prevention trials. Biometrika 73 (1):1–11. doi:https://doi.org/10.1093/biomet/73.1.1.
- Self, S. G., and R. L. Prentice. 1988. Asymptotic distribution theory and efficiency results for case-cohort studies. The Annals of Statistics 16 (1):64–81. doi:https://doi.org/10.1214/aos/1176350691.
- Shang, W. P., and X. Wang. 2017. The generalized moment estimation of the additive-multiplicative hazard model with auxiliary survival information. Computational Statistics & Data Analysis 112:154–59. doi:https://doi.org/10.1016/j.csda.2017.03.013.
- Sun, J., L. Sun, and N. Flournoy. 2004. Additive hazards model for competing risks analysis of the case-cohort design. Communications in Statistics – Theory and Methods 33 (2):351–66. doi:https://doi.org/10.1081/STA-120028378.
- Wu, C., and R. R. Sitter. 2001. A model-calibration approach to using complete auxiliary information from survey data. Journal of the American Statistical Association 96 (453):185–93. doi:https://doi.org/10.1198/016214501750333054.
- Yip, P. F., Y. Zhou, D. Lin, and X. Fang. 1999. Estimation of population size based on additive hazards models for continuous-time recapture experiments. Biometrics 55 (3):904–908. doi:https://doi.org/10.1111/j.0006-341x.1999.00904.x.
- Yusuf, S., J. Wittes, J. Probstfield, and H. A. Tyroler. 1991. Analysis and interpretation of treatment effects in subgroups of patients in randomized clinical trials. JAMA: The Journal of the American Medical Association 266 (1):93–98. doi:https://doi.org/10.1001/jama.1991.03470010097038.