A new approach to regression analysis of linear transformation model with interval-censored data

References

• Austin, P. C. 2011. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behavioral Research 46 (3):399–424. doi:10.1080/00273171.2011.568786.
   PubMed Web of Science ®Google Scholar
• Brookhart, M. A., R. Wyss, J. B. Layton, and T. Stürmer. 2013. Propensity score methods for confounding control in nonexperimental research. Circulation. Cardiovascular Quality and Outcomes 6 (5):604–11. doi:10.1161/CIRCOUTCOMES.113.000359.
   PubMed Web of Science ®Google Scholar
• Cheng, S. C., L. J. Wei, and Z. Ying. 1995. Analysis of transformation models with censored data. Biometrika 82 (4):835–46. doi:10.1093/biomet/82.4.835.
   Web of Science ®Google Scholar
• Feng, C. Y., H. Y. Wang, T. Chen, and X. M. Tu. 2014. On exact forms of Taylor’s theorem for vector-valued functions. Biometrika 101 (4):1003. doi:10.1093/biomet/asu061.
   Web of Science ®Google Scholar
• Finkelstein, D. M. 1986. A proportional hazards model for interval censored failure time data. Biometrics 42 (4):845–854.
   PubMed Web of Science ®Google Scholar
• Goedert, J. J., C. M. Kessler, L. M. Aledort, R. J. Biggar, W. A. Andes, G. C. White, J. E. Drummond, K. Vaidya, D. L. Mann, and M. E. Eyster. 1989. A prospective study of human immunodeficiency virus type 1 infection and the development of AIDS in subjects with hemophilia. The New England Journal of Medicine 321 (17):1141–1148. doi:10.1056/NEJM198910263211701.
   PubMed Web of Science ®Google Scholar
• Gu, M. G., L. Sun, and G. Zuo. 2005. A baseline-free procedure for transformation models under interval censorship. Lifetime Data Analysis 11 (4):473–488. doi:10.1007/s10985-005-5235-x.
   PubMed Web of Science ®Google Scholar
• Huang, J. 1995. Maximum likelihood estimation for proportional odds regression model with current status data. Analysis of Censored Data (IMS Lecture Notes Monograph Series) 27:129–145.
   Google Scholar
• Huang, J. 1996. Efficient estimation for the proportional hazards model with interval censoring. The Annals of Statistics 24 (2):540–568. doi:10.1214/aos/1032894452.
   Web of Science ®Google Scholar
• Huang, J., and A. Rossini. 1997. Sieve estimation for the proportional odds failure time regression model with interval censoring. Journal of the American Statistical Association 92 (439):960–967. doi:10.1080/01621459.1997.10474050.
   Web of Science ®Google Scholar
• Imai, K., and M. Ratkovic. 2014. Covariate balancing propensity score. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 76 (1):243–263. doi:10.1111/rssb.12027.
   Web of Science ®Google Scholar
• Kalbfleisch, J. D., and R. L. Prentice. 2002. The statistical analysis of failure time data. New York: Wiley.
   Google Scholar
• Kroner, B. L., P. S. Rosenberg, L. M. Aledort, W. G. Alvord, and J. J. Goedert. 1994. HIV–1 infection incidence among people with hemophilia in the United States and Western Europe, 1978–1990. Journal of Acquired Immune Deficiency Syndromes 7 (3):279–286.
   PubMed Web of Science ®Google Scholar
• Li, L., and Z. Pu. 2003. Rank estimation of log-linear regression with interval-censored data. Lifetime Data Analysis 9 (1):57–70. doi:10.1023/a:1021882122257.
   PubMed Web of Science ®Google Scholar
• Liang, K. Y., and S. L. Zeger. 1986. Longitudinal data analysis using generalized linear models. Biometrika 73 (1):13–22. doi:10.1093/biomet/73.1.13.
   Web of Science ®Google Scholar
• Ratkovic, M., K. Imai, and C. Fong. 2012. Cbps: R package for covariate balancing propensity score. http://CRAN.R-project.org/package=CBPS.
   Google Scholar
• Rosenbaum, P. R., and D. B. Rubin. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70 (1):41–55. doi:10.1093/biomet/70.1.41.
   Web of Science ®Google Scholar
• Sun, J. 2006. The statistical analysis of interval-censored failure time data. New York: Springer.
   Google Scholar
• Sun, J., and L. Sun. 2005. Semiparametric linear transformation models for current status data. Canadian Journal of Statistics 33 (1):85–96. doi:10.1002/cjs.5540330107.
   Web of Science ®Google Scholar
• Wei, L. J., and W. E. Johnson. 1985. Combining dependent tests with incomplete repeated measurements. Biometrika 72 (2):359–364. doi:10.1093/biomet/72.2.359.
   Web of Science ®Google Scholar
• Zeng, D., L. Mao, and D. Lin. 2016. Maximum likelihood estimation for semiparametric transformation models with interval-censored data. Biometrika 103 (2):253–271. doi:10.1093/biomet/asw013.
   PubMed Web of Science ®Google Scholar
• Zhang, Z., L. Sun, X. Zhao, and J. Sun. 2005. Regression analysis of interval-censored failure time data with linear transformation models. Canadian Journal of Statistics 33 (1):61–70. doi:10.1002/cjs.5540330105.
   Web of Science ®Google Scholar
• Zhang, Z., and Y. Zhao. 2013. Empirical likelihood for linear transformation models with interval-censored failure time data. Journal of Multivariate Analysis 116:398–409. doi:10.1016/j.jmva.2013.01.003.
   Web of Science ®Google Scholar
• Zhao, H., C. C. Ma, J. Li, and J. Sun. 2018. Regression analysis of clustered interval-censored failure time data with linear transformation models in the presence of informative cluster size. Journal of Nonparametric Statistics 30 (3):703–715. doi:10.1080/10485252.2018.1469755.
   Web of Science ®Google Scholar