Handling missingness value on jointly measured time-course and time-to-event data

References

• Armero, C., C. Forné, M. Rué, A. Forte, H. Perpiñán, G. Gómez, and M. Baré. 2016. Bayesian joint ordinal and survival modeling for breast cancer risk assessment. Statistics in Medicine 35 (28):5267–82. doi: 10.1002/sim.7065.
   PubMed Web of Science ®Google Scholar
• Bacon, K. B., B. A. Premack, P. Gardner, and T. J. Schall. 1995. Activation of dual T cell signaling pathways by the chemokine RANTES. Science (New York, N.Y.) 269 (5231):1727–30. doi: 10.1126/science.7569902.
   PubMed Web of Science ®Google Scholar
• Bell, M. L., M. G. Kenward, D. L. Fairclough, and N. J. Horton. 2013. Differential dropout and bias in randomised controlled trials: When it matters and when it may not. BMJ (Clinical Research ed.) 346:E8668.
   PubMed Web of Science ®Google Scholar
• Bhattacharjee, A., and G. K. Vishwakarma. 2019. Time-course data prediction for repeatedly measured gene expression data. International Journal of Biomathematics 12 (04):1950033. doi: 10.1142/S1793524519500335.
   Google Scholar
• Bhattacharjee, A., G. K. Vishwakarma, and A. Thomas. 2018. Bayesian state-space modeling in gene expression data analysis: An application with biomarker prediction. Mathematical Biosciences 305:96–101. doi: 10.1016/j.mbs.2018.08.011.
   PubMed Web of Science ®Google Scholar
• Buuren, S. V., and K. Groothuis-Oudshoorn. 2010. mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software 45 (3):1–67.
   Web of Science ®Google Scholar
• Chen, Q., R. C. May, J. G. Ibrahim, H. Chu, and S. R. Cole. 2014. Joint modeling of longitudinal and survival data with missing and left-censored time-varying covariates. Statistics in Medicine 33 (26):4560–76. doi: 10.1002/sim.6242.
   PubMed Web of Science ®Google Scholar
• Cox, D. R. 1972. Regression models and life–tables. Journal of the Royal Statistical Society: Series B (Methodological) 34 (2):187–202.
   Web of Science ®Google Scholar
• Doove, L. L., S. V. Buuren, and E. Dusseldorp. 2014. Recursive partitioning for missing data imputation in the presence of interaction effects. Computational Statistics & Data Analysis 72:92–104.
   Web of Science ®Google Scholar
• Faucett, C. L., and D. C. Thomas. 1996. Simultaneously modeling censored survival data and repeatedly measured covariates: A Gibbs sampling approach. Statistics in Medicine 15 (15):1663–85. doi: 10.1002/(SICI)1097-0258(19960815)15:15<1663::AID-SIM294>3.0.CO;2-1.
   PubMed Web of Science ®Google Scholar
• Henderson, R., P. Diggle, and A. Dobson. 2000. Joint modelling of longitudinal measurements and event time data. Biostatistics (Oxford, England) 1 (4):465–80. doi: 10.1093/biostatistics/1.4.465.
   PubMedGoogle Scholar
• Hickey, G. L., P. Philipson, A. Jorgensen, and R. Kolamunnage-Dona. 2018. joineRML: A joint model and software package for time-to-event and multivariate longitudinal outcomes. BMC Medical Research Methodology 18 (1):50. doi: 10.1186/s12874-018-0502-1.
   PubMedGoogle Scholar
• Hogan, J. W., J. Roy, and C. Korkontzelou. 2004. Handling drop-out in longitudinal studies. Statistics in Medicine 23 (9):1455–97. doi: 10.1002/sim.1728.
   PubMed Web of Science ®Google Scholar
• Honaker, J., G. King, and M. Blackwell. 2011. Amelia II: A program for missing data. Journal of Statistical Software 45 (7):1–47. [Database] doi: 10.18637/jss.v045.i07.
   Web of Science ®Google Scholar
• Hui, D., I. Glitza, G. Chisholm, S. Yennu, and E. Bruera. 2013. Attrition rates, reasons, and predictive factors in supportive care and palliative oncology clinical trials. Cancer 119 (5):1098–105. doi: 10.1002/cncr.27854.
   PubMed Web of Science ®Google Scholar
• Ibrahim, J. G., H. Chu, and L. M. Chen. 2010. Basic concepts and methods for joint models of longitudinal and survival data. Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology 28 (16):2796–801. doi: 10.1200/JCO.2009.25.0654.
   PubMed Web of Science ®Google Scholar
• Ibrahim, J. G., and G. Molenberghs. 2009. Missing data methods in longitudinal studies: A review. Test 18 (1):1–43. doi: 10.1007/s11749-009-0138-x.
   PubMed Web of Science ®Google Scholar
• Kowarik, A., and M. Templ. 2016. Imputation with the R Package VIM. Journal of Statistical Software 74 (7):1–16. doi: 10.18637/jss.v074.i07.
   Web of Science ®Google Scholar
• Little, R. J., and D. B. Rubin. 2014. Statistical analysis with missing data. Vol. 333. Hoboken, NJ: John Wiley & Sons.
   Google Scholar
• Lu, T. 2017. Jointly modeling skew longitudinal survival data with missingness and mismeasured covariates. Journal of Applied Statistics 44 (13):2354–67. doi: 10.1080/02664763.2016.1254728.
   Web of Science ®Google Scholar
• Meltzer, S., E. Kalanxhi, H. H. Hektoen, S. Dueland, K. Flatmark, K. R. Redalen, and A. H. Ree. 2016. Systemic release of osteoprotegerin during oxaliplatin-containing induction chemotherapy and favorable systemic outcome of sequential radiotherapy in rectal cancer. Oncotarget 7 (23):34907. doi: 10.18632/oncotarget.8995.
   Google Scholar
• Parzen, M., S. R. Lipsitz, G. M. Fitzmaurice, J. G. Ibrahim, and A. Troxel. 2006. Pseudo-likelihood methods for longitudinal binary data with non-ignorable missing responses and covariates. Statistics in Medicine 25 (16):2784–96. doi: 10.1002/sim.2435.
   PubMed Web of Science ®Google Scholar
• Philipson, P., I. Sousa, P. Diggle, P. Williamson, R. Kolamunnage-Dona, R. Henderson, and G. Hickey. 2018. joineR: Joint modelling of repeated measurements and time-to-event data. R package version 1.2.4, https://github.com/graemeleehickey/joineR/.
   Google Scholar
• Rizopoulos, D. 2010. JM: An R package for the joint modelling of longitudinal and time-to-event data. Journal of Statistical Software (Online) 35 (9):1–33.
   PubMed Web of Science ®Google Scholar
• Rizopoulos, D. 2012. Joint models for longitudinal and time-to-event data: With applications in R. New York: Chapman and Hall/CRC.
   Google Scholar
• Rizopoulos, D. 2016. The R package JMbayes for fitting joint models for longitudinal and time-to-event data using MCMC. Journal of Statistical Software 72 (7):1–46. doi: 10.18637/jss.v072.i07.
   Web of Science ®Google Scholar
• Robins, J. M., A. Rotnitzky, and L. P. Zhao. 1995. Analysis of semiparametric regression models for repeated outcomes in the presence of missing data. Journal of the American Statistical Association 90 (429):106–21. doi: 10.1080/01621459.1995.10476493.
   Web of Science ®Google Scholar
• Rubin, D. B. 1976. Inference and missing data. Biometrika 63 (3):581–92. doi: 10.1093/biomet/63.3.581.
   Web of Science ®Google Scholar
• Rubin, D. B. 2004. Multiple imputation for nonresponse in surveys. Vol. 81. Hoboken, NJ: John Wiley & Sons.
   Google Scholar
• Sattar, A., and S. K. Sinha. 2019. Joint modeling of longitudinal and survival data with a covariate subject to a limit of detection. Statistical Methods in Medical Research 28 (2):486–502. doi: 10.1177/0962280217729573.
   PubMed Web of Science ®Google Scholar
• Song, H., Y. Peng, and D. Tu. 2016. Recent development in the joint modeling of longitudinal quality of life measurements and survival data from cancer clinical trials. In Advanced statistical methods in data science, ICSA Book Series in Statistics, ed. D. G. Chen, J. Chen, X. Lu, G. Yi, and H. Yu, 153–68. Singapore: Springer.
   Google Scholar
• Sun, Y., L. Ma, J. Mathew, W. Wang, and S. Zhang. 2006. Mechanical systems hazard estimation using condition monitoring. Mechanical Systems and Signal Processing 20 (5):1189–201. doi: 10.1016/j.ymssp.2004.10.009.
   Web of Science ®Google Scholar
• Templ, M., A. Kowarik, and P. Filzmoser. 2011. Iterative stepwise regression imputation using standard and robust methods. Computational Statistics & Data Analysis 55 (10):2793–806.
   Web of Science ®Google Scholar
• Tsiatis, A. 2007. Semiparametric theory and missing data. New York: Springer Science & Business Media.
   Google Scholar
• Tsiatis, A. A., and M. Davidian. 2001. A semiparametric estimator for the proportional hazards model with longitudinal covariates measured with error. Biometrika 88 (2):447–58. doi: 10.1093/biomet/88.2.447.
   Web of Science ®Google Scholar
• Tsiatis, A. A., V. Degruttola, and M. S. Wulfsohn. 1995. Modeling the relationship of survival to longitudinal data measured with error. Applications to survival and CD4 counts in patients with AIDS. Journal of the American Statistical Association 90 (429):27–37. doi: 10.1080/01621459.1995.10476485.
   Web of Science ®Google Scholar
• Vishwakarma, G. K., A. Bhattacharjee, J. Jose, and V. Ramesh. 2016. Cancer patients missing pain score information: Application with imputation techniques. Epidemiology, Biostatistics and Public Health 13 (4):e11916.
   Web of Science ®Google Scholar
• Weiss, R. E. 2005. Modeling longitudinal data. New York: Springer Science & Business Media.
   Google Scholar
• Wulfsohn, M. S., and A. A. Tsiatis. 1997. A joint model for survival and longitudinal data measured with error. Biometrics 53 (1):330–39.
   PubMed Web of Science ®Google Scholar