Dynamic lifetime prediction using a Weibull-based bivariate failure time model: a meta-analysis of individual-patient data

References

• Balakrishnan, N., and S. Pal. 2015. An EM algorithm for the estimation of parameters of a flexible cure rate model with generalized gamma lifetime and model discrimination using likelihood- and information-based methods. Computational Statistics 30 (1):151–89. doi:10.1007/s00180-014-0527-9.
   Web of Science ®Google Scholar
• Barriga, G. D., F. Louzada-Neto, and V. G. Cancho. 2011. The complementary exponential power lifetime model. Computational Statistics & Data Analysis 55 (3):1250–59.
   Web of Science ®Google Scholar
• Burzykowski, T., G. Molenberghs, M. Buyse, H. Geys, and D. Renard. 2001. Validation of surrogate end points in multiple randomized clinical trials with failure time end points. Journal of Applied Statistics 50 (4):405–22. doi:10.1111/1467-9876.00244.
   Web of Science ®Google Scholar
• Collett, D. 2003. Modelling survival data in medical research. London: Chapman & Hall.
   Google Scholar
• Commenges, D., and H. Jacqmin-Gadda. 2015. Dynamical Biostatistical Models. London: CRC Press.
   Google Scholar
• Dörre, A., C. Y. Huang, Y. K. Tseng, and T. Emura. 2020. Likelihood-based analysis of doubly-truncated data under the location-scale and AFT model. Computational Statistics. Advance online publication. doi:10.1007/s00180-020-01027-6.
   Google Scholar
• Emura, T., M. Nakatochi, S. Matsui, H. Michimae, and V. Rondeau. 2018. Personalized dynamic prediction of death according to tumour progression and high-dimensional genetic factors: Meta-analysis with a joint model. Statistical Methods in Medical Research 27 (9):2842–58.
   PubMed Web of Science ®Google Scholar
• Emura, T., M. Nakatochi, K. Murotani, and V. Rondeau. 2017. A joint frailty-copula model between tumour progression and death for meta-analysis. Statistical Methods in Medical Research 26 (6):2649–66.
   PubMed Web of Science ®Google Scholar
• Emura, T., S. Matsui, and V. Rondeau. 2019. Survival analysis with correlated endpoints, joint frailty-copula models, JSS research series in statistics. Singapore: Springer.
   Google Scholar
• Emura, T. 2020. joint.Cox: Joint frailty-copula models for tumour progression and death in meta-analysis. CRAN.
   Google Scholar
• Eryilmaz, S., and C. Kan. 2010. Dynamic reliability evaluation of consecutive-k-within-m-out-of-n: F system. Communications in Statistics – Simulation and Computation 40 (1):58–71. doi:10.1080/03610918.2010.530366.
   Web of Science ®Google Scholar
• Fan, T. H., Y. F. Wang, and S. K. Ju. 2019. A competing risks model with multiply censored reliability data under multivariate Weibull distributions. IEEE Transactions on Reliability 68 (2):462–75.
   Web of Science ®Google Scholar
• Gerds, T. A., and M. Schumacher. 2006. Consistent estimation of the expected Brier score in general survival models with right-censored event times. Biometrical Journal 48 (6):1029–40.
   PubMed Web of Science ®Google Scholar
• Haibe-Kains, B., C. Desmedt, S. Loi, A. C. Culhane, G. Bontempi, J. Quackenbush, and C. Sotiriou. 2012. A three-gene model to robustly identify breast cancer molecular subtypes. Journal of the National Cancer Institute 104 (4):311–25.
   PubMedGoogle Scholar
• Huang, X. W., W. Wang, and T. Emura. 2020. A copula-based Markov chain model for serially dependent event times with a dependent terminal event. Japanese Journal of Statistics and Data Science. Advance online publication. doi:10.1007/s42081-020-00087-8.
   Google Scholar
• Hung, W. L. 2001. Weighted least‐squares estimation of the shape parameter of the Weibull distribution. Quality and Reliability Engineering International 17 (6):467–69. doi:10.1002/qre.423.
   Web of Science ®Google Scholar
• Kayid, M., M. S. Noughabi, and A. M. Abouammoh. 2020. A nonparametric estimator of bivariate quantile residual life model with application to tumor recurrence data set. Journal of Classification 37 (1):237–53. doi:10.1007/s00357-018-9300-z.
   Web of Science ®Google Scholar
• Klein, J. P., and M. L. Moeschberger. 2003. Survival analysis: Techniques for censored and truncated data. New York: Springer.
   Google Scholar
• Kundu, D., and A. M. Sarhan. 2006. Analysis of incomplete data in presence of competing risks among several groups. IEEE Transactions on Reliability 55 (2):262–69.
   Web of Science ®Google Scholar
• Li, Z., V. M. Chinchilli, and M. Wang. 2019. A Bayesian joint model of recurrent events and a terminal event. Biometrical Journal 61 (1):187–202. doi:10.1002/bimj.201700326.
   PubMed Web of Science ®Google Scholar
• Lim, J. Y., and J. H. Jeong. 2017. Cause-specific quantile residual life regression. Statistical Methods in Medical Research 26 (4):1912–24. doi:10.1177/0962280215592426.
   PubMed Web of Science ®Google Scholar
• Nelson, W. 1982. Applied life data analysis. Addison-Wesley.
   Google Scholar
• Noughabi, M. S., and M. Kayid. 2019. Bivariate quantile residual life: A characterization theorem and statistical properties. Statistical Papers 60 (6):2001–12. doi:10.1007/s00362-017-0905-9.
   Web of Science ®Google Scholar
• Olkin, I. 2016. Life distributions: A brief discussion. Communications in Statistics – Simulation and Computation 45 (5):1489–98. doi:10.1080/03610918.2014.966833.
   Web of Science ®Google Scholar
• Pal, S., and N. Balakrishnan. 2017. Likelihood inference for the destructive exponentially weighted Poisson cure rate model with Weibull lifetime and an application to melanoma data. Computational Statistics 32 (2):429–49. doi:10.1007/s00180-016-0660-8.
   Web of Science ®Google Scholar
• Peng, M., L. Xiang, and S. Wang. 2018. Semiparametric regression analysis of clustered survival data with semi-competing risks. Computational Statistics & Data Analysis 124:53–70. doi:10.1016/j.csda.2018.02.003.
   Web of Science ®Google Scholar
• Proust-Lima, C., and P. Blanche. 2014. Dynamic predictions. Wiley StatsRef: Statistics Reference Online 1–6.
   Google Scholar
• Ramsay, J. 1988. Monotone regression spline in action. Statistical Science 3 (4):425–61. doi:10.1214/ss/1177012761.
   Google Scholar
• Rizopoulos, D., G. Molenberghs, and E. M. Lesaffre. 2017. Dynamic predictions with time-dependent covariates in survival analysis using joint modeling and landmarking. Biometrical Journal 59 (6):1261–76. doi:10.1002/bimj.201600238.
   PubMed Web of Science ®Google Scholar
• Rondeau, V., J. P. Pignon, and S. Michiels, MACH-NC collaborative Group. 2015. A joint model for the dependence between clustered times to tumour progression and deaths: A meta-analysis of chemotherapy in head and neck cancer . Statistical Methods in Medical Research 24 (6):711–29.
   PubMed Web of Science ®Google Scholar
• Rotolo, F., X. Paoletti, and S. Michiels. 2018. surrosurv: An R package for the evaluation of failure time surrogate endpoints in individual patient data meta-analyses of randomized clinical trials. Computer Methods and Programs in Biomedicine 155:189–98. doi:10.1016/j.cmpb.2017.12.005.
   PubMed Web of Science ®Google Scholar
• Shih, J. H., and T. Emura. 2020. Penalized Cox regression with a five-parameter spline model. Communications in Statistics – Theory and Methods. Advance online publication. doi:10.1080/03610926.2020.1772305.
   Web of Science ®Google Scholar
• Zhang, L. F., M. Xie, and L. C. Tang. 2006. Bias correction for the least squares estimator of Weibull shape parameter with complete and censored data. Reliability Engineering & System Safety 91 (8):930–39.
   Web of Science ®Google Scholar
• van Houwelingen, H. C., and H. Putter. 2011. Dynamic prediction in clinical survival analysis. New York: CRC Press.
   Google Scholar
• Wu, B. H., H. Michimae, and T. Emura. 2020. Meta-analysis of individual patient data with semi-competing risks under the Weibull joint frailty–copula model. Computational Statistics 35 (4):1525–52. doi:10.1007/s00180-020-00977-1.
   Web of Science ®Google Scholar