| Li, N., Elashoff, R. M., Li, G., and Saver, J. | Joint modeling of longitudinal ordinal data and competing risks survival times and analysis of the NINDS rt‐PA stroke trial. Statistics in medicine, 29(5), 546-557. | C | Download C code |
| Rizopoulos, D. [138] | Joint models for longitudinal and time-to-event data with applications in R. CRC Press, 2012 | The R package JM | Download JM package |
| Li, Z., Tosteson, T.D. and Bakitas, M.A. [108] | Joint modeling quality of life and survival using a terminal decline model in palliative care studies. Statistics in Medicine (2013) 32, 1394-1406. | SAS | The Enable II Study (Example 4.7) |
| Diggle, P. and Kenward, M.G. [37] | Informative drop-out in longitudinal data analysis. Applied Statistics (1994) 43, 49-93 | SAS | The Milk Protein Trial (Example 4.8) |
| Sun, J., Sun, L. and Liu, D. [155] | Regression analysis of longitudinal data in the presence of informative observation and censoring times. Journal of the American Statistical Association (2007) 102, 1397-1406. | Matlab and Fortran | The Bladder Cancer Study (Example 4.16) |
| Elashoff, R., Li, G. and Li, N. [43] | A joint model for longitudinal measurements and survival data in the presence of multiple failure types. Biometrics (2008) 64, 762-771. | C | The Scleroderma Lung Study (Example 5.1) |
| Rizopoulos, D. and Ghosh, P. [139] | A Bayesian semiparametric multivariate joint model for multiple longitudinal outcomes and a time-to-event. Statistics in Medicine (2011) 30, 1366-1380. | R and WinBUGS/JAGS | The Renal Graft Failure Study (Example 6.1) |
| Proust-Lima, C., et al. [132] | Joint modelling of multivariate longitudinal outcomes and a time-to-event: A nonlinear latent class approach. Computational Statistics and Data Analysis (2009) 53, 1142-1154. | Fortran90 | The PAQUID Study (Example 6.2) |
| Xu, C., Hadjipantelis, P. and Wang, J. | Semiparametric Joint Modeling of Survival and Longitudinal Data | The R package JSM | Download JSM package |
