Regression analysis of informatively interval-censored failure time data with semiparametric linear transformation model

References

• Betensky, R.A., and Finkelstein, D.M. (2002), ‘Testing for Dependence between Failure Time and Visit Compliance with Interval-censored Data’, Biometrics, 58, 58–63. doi: 10.1111/j.0006-341X.2002.00058.x
   PubMed Web of Science ®Google Scholar
• Chen, X., Fan, Y., and Tsyrennikov, V. (2006), ‘Efficient Estimation of Semiparametric Multivariate Copula Models’, Journal of the American Statistical Association, 101, 1228–1240. doi: 10.1198/016214506000000311
   Web of Science ®Google Scholar
• Chen, D.G.D., Sun, J., and Peace, K.E. (2012), Interval-Censored Time-To-Event Data: Methods and Applications, Boca Raton: Chapman & Hall/CRC.
   Google Scholar
• Finkelstein, D.M. (1986), ‘A Proportional Hazards Model for Interval-Censored Failure Time Data’, Biometrics, 42, 845–854. doi: 10.2307/2530698
   PubMed Web of Science ®Google Scholar
• Finkelstein, D.M., Goggins, W.B., and Schoenfeld, D.A. (2002), ‘Analysis of Failure Time Data with Dependent Interval Censoring’, Biometrics, 58, 298–304. doi: 10.1111/j.0006-341X.2002.00298.x
   PubMed Web of Science ®Google Scholar
• Goggins, W.B., Finkelstein, D.M., Schoenfeld, D.A., and Zaslavsky, A.M. (1998), ‘A Markov Chain Monte Carlo EM Algorithm for Analyzing Interval Censored Data under The Cox Proportional Hazards Model’, Biometrics, 54, 1498–1507. doi: 10.2307/2533674
   PubMed Web of Science ®Google Scholar
• Hougaard, P (2000), Analysis of Multivariate Survival Data, New York: Springer-Verlag.
   Google Scholar
• Huang, J., and Rossini, A.J. (1997), ‘Sieve Estimation for the Proportional Odds Failure-Time Regression Model with Interval Censoring’, Journal of the American Statistical Association, 92, 960–967. doi: 10.1080/01621459.1997.10474050
   Web of Science ®Google Scholar
• Kalbfleisch, J.D., and Prentice, R.L (2002), The Statistical Analysis of Failure Time Data (2nd ed.), New York: John Wiley.
   Google Scholar
• Lu, M., Zhang, Y., and Huang, J. (2007), ‘Estimation of the Mean Function with Panel Count Data Using Monotone Polynomial Splines’, Biometrika, 94, 705–718. doi: 10.1093/biomet/asm057
   Web of Science ®Google Scholar
• Ma, L., Hu, T., and Sun, J. (2016), ‘Cox Regression Analysis of Dependent Interval-Censored Failure Time Data’, Computational Statistics and Data Analysis, 103, 79–90. doi: 10.1016/j.csda.2016.04.011
   Web of Science ®Google Scholar
• Nelsen, R.B (2006), An Introduction to Copulas (2nd ed.), New York: Springer.
   Google Scholar
• Pollard, D. (1984), Convergence of Stochastic Processes, New York: Springer-Verlag.
   Google Scholar
• Schumaker, L.L (1981), Spline Functions: Basic Theory, New York: John Wiley.
   Google Scholar
• Shen, X. (1997), ‘On Methods of Sieves and Penalization’, Annals of Statistics, 25, 2555–2591. doi: 10.1214/aos/1030741085
   Web of Science ®Google Scholar
• Shen, X., and Wong, W.H. (1994), ‘Convergence Rate of Sieve Estimates’, Annals of Statistics, 22, 580–615. doi: 10.1214/aos/1176325486
   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 Sun, L (2005), ‘Semiparametric Linear Transformation Models for Current Status Data’, The Canadian Journal of Statistics, 33(1), 85–96. doi: 10.1002/cjs.5540330107
   Web of Science ®Google Scholar
• van de Geer, S. (2000), Empirical Process in M-Estimation, New York: Cambridge University Press.
   Google Scholar
• van der Vaart, A.W., and Wellner, J.A. (1996), Weak Convergence and Empirical Processes, Berlin: Springer.
   Google Scholar
• Wang, P., Zhao, H., and Sun, J. (2016), ‘Regression Analysis of Case K Interval-Censored Failure Time Data in the Presence of Informative Censoring’, Biometrics, 72, 1103–1112. doi: 10.1111/biom.12527
   PubMed Web of Science ®Google Scholar
• Xu, D., Zhao, S., Hu, T., Yu, M., and Sun, J. (2019), ‘Regression Analysis of Informative Current Status Data with the Semiparametric Linear Transformation Model’, Journal of Applied Statistics, 46, 187–202. doi: 10.1080/02664763.2018.1466870
   Web of Science ®Google Scholar
• Zhang, Y., Hua, L., and Huang, J. (2010), ‘A Spline-Based Semiparametric Maximum Likelihood Estimation Method for the Cox Model with Interval-Censored Data’, Scandinavian Journal of Statistics, 37(2), 338–354. doi: 10.1111/j.1467-9469.2009.00680.x
   Web of Science ®Google Scholar
• Zhang, Z., Sun, L., Sun, J., and Finkelstein, D.M. (2007), ‘Regression Analysis of Failure Time Data with Informative Interval Censoring’, Statistics In Medicine, 26, 2533–2546. doi: 10.1002/sim.2721
   PubMed Web of Science ®Google Scholar
• Zhao, S., Hu, T., Ma, L., Wang, P., and Sun, J. (2015), ‘Regression Analysis of Interval-Censored Failure Time Data with the Additive Hazards Model in the Presence of Informative Censoring’, Statistics and its Interface, 8(3), 367–377. doi: 10.4310/SII.2015.v8.n3.a10
   Web of Science ®Google Scholar
• Zheng, M., and Klein, J.P. (1995), ‘Estimates of Marginal Survival for Dependent Competing Risk Based on an Assumed Copula’, Biometrika, 82, 127–138. doi: 10.1093/biomet/82.1.127
   Web of Science ®Google Scholar