Random-effects models for joint analysis of repeated-measurement and time-to-event outcomes

Longitudinal Data Analysis

Volumn , Issue , 2008, Pages 349-366

  Diggle, Peter ^a   Henderson, Robin ^b   Philipson, Peter ^b  

^a LANCASTER UNIVERSITY   (United Kingdom)

^b NEWCASTLE UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84862025942     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Chapter

References (57)

1. Altham, P. M. E. (1984). Improving the precision of estimation by fitting a model. Journal of the Royal Statistical Society, Series B 46, 118–119.
2. Andersen, P. K., Borgan, Ø., Gill, R. D., and Keiding, N. (1993). Statistical Models Based on Counting Processes. New York: Springer.
3. Berzuini, C. and Larizza, C. (1996). A unified approach for modelling longitudinal and failure time data, with application in medical monitoring. IEEE Transactions on Pattern Analysis and Machine Intelligence 18, 109–123.
4. Billingham L. J. and Abrams K. R. (2002). Simultaneous analysis of quality of life and survival data. Statistical Methods in Medical Research 11, 25–48.
5. Borgan, Ø., Fiaccone, R. L., Henderson, R., and Barreto, M. L. (2007). Dynamic analysis of recurrent event data with missing observations, with application to infant diarrhoea in Brazil. Scandinavian Journal of Statistics 34, 53–69.
6. Brown, E. R. and Ibrahim, J. G. (2003a). Bayesian approaches to joint cure-rate and longitudinal models with applications to cancer vaccine trials. Biometrics 59, 686–693.
7. Brown, E. R. and Ibrahim, J. G. (2003b). A Bayesian semiparametric joint hierarchical model for longitudinal and survival data. Biometrics 59, 221–228.
8. Chi, Y. Y. and Ibrahim, J. G. (2006). Joint models for multivariate longitudinal and multivariate survival data. Biometrics 62, 432–445.
9. Cox, D. R. (1972). Regression models and life tables. Journal of the Royal Statistical Society, Series B, 34, 187–220.
10. de Bruijne, M. H. J., le Cessie, S., Kluin-Nelemans, H. C., and van Houwelingen, H. C. (2001). On the use of Cox regression in the presence of an irregularly observed time-dependent covariate. Statistics in Medicine 20, 3817–3829.
11. Diggle, P. J. (1988). An approach to the analysis of repeated measurements. Biometrics 44, 959–971.
12. Diggle, P. J. and Kenward, M. G. (1994). Informative drop-out in longitudinal data analysis (with discussion). Applied Statistics 43, 49–94.
13. Diggle, P. J. (1998). Dealing with missing values in longitudinal studies. In B. S. Everitt and G. Dunn (eds.), Recent Advances in the Statistical Analysis of Medical Data, pp. 203–228. London: Arnold.
14. Diggle, P. J., Heagerty, P., Liang, K. Y., and Zeger, S. L. (2002). Analysis of Longitudinal Data, 2nd ed. Oxford: Oxford University Press.
15. Do, K.-A. (2002). Biostatistical approaches for modeling longitudinal and event time data. Clinical Cancer Research 8, 2473–2474.
16. Dobson, A. and Henderson, R. (2003). Diagnostics for joint longitudinal and dropout time modeling. Biometrics 59, 741–751.
17. Dufoil, C., Brayne, D., and Clayton, D. (2004). Analysis of longitudinal studies with death and drop-out: a case study. Statistics in Medicine 23, 2215–2226.
18. Dupuy, J. F. and Mesbah, M. (2002). Joint modelling of event time and nonignorable missing longitudinal data. Lifetime Data Analysis 8, 99–115.
19. Elbers, C. and Ridder, G. (1982). True and spurious duration dependence: The identifiability of the proportional hazard model. Review of Economic Studies 49, 403–409.
20. Fitzmaurice, G. M., Laird, N. M., and Ware, J. H. (2004). Applied Longitudinal Analysis. Hoboken, NJ: Wiley.
21. Guo, X. and Carlin, B. P. (2004). Separate and joint modeling of longitudinal and event time data using standard computer packages. American Statistician 58, 16–24.
22. Ha, I. D., Park, T. S., and Lee, Y. J. (2003). Joint modelling of repeated measures and survival time data. Biometrical Journal 45, 647–658.
23. Hashemi, R., Jacqmin-Gadda, H., and Commenges, D. (2003). A latent process model for joint modeling of events and marker. Lifetime Data Analysis 9, 331–343.
24. Heckman, J. and Singer, B. (1984). The identifiability of the proportional hazard model. Review of Economic Studies 51, 231–243.
25. Henderson, R., Diggle, P., and Dobson, A. (2000). Joint modelling of measurements and event time data. Biostatistics 1, 465–480.
26. Henderson, R., Diggle, P., and Dobson, A. (2002). Identification and efficacy of longitudinal markers for survival. Biostatistics 3, 33–50.
27. Hogan, J. W. and Laird, N. M. (1997). Model-based approaches to analysing incomplete longitudinal and failure time data. Statistics in Medicine 16, 259–272.
28. Hogan, J. W., Roy, J., and Korkontzelou, C. (2004). Tutorial in biostatistics — handling drop-out in longitudinal studies. Statistics in Medicine 23, 1455–1497.
29. Hougaard, P. (2000). Analysis of Multivariate Survival Data. New York: Springer.
30. Hsieh, F., Tseng, Y. K., and Wang, J. L. (2006). Joint modelling of survival and longitudinal data: Likelihood approach revisited. Biometrics 62, 1037–1043.
31. Huang, W. Z., Zeger, S. L., Anthony J. C., and Garrett, E. (2001). Latent variable model for joint analysis of multiple repeated measures and bivariate event times, Journal of the American Statistical Association 96, 906–914.
32. Ibrahim, J. G., Chen, M. H., and Sinha, D. (2004) Bayesian methods for joint modeling of longitudinal and survival data with applications to cancer vaccine trials. Statistica Sinica 14, 863–883.
33. Laird, N. M. and Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics 38, 963–974.
34. Law, N. J., Taylor, J. M. G., and Sandler, H. (2002). The joint modeling of a longitudinal disease progression marker and the failure time process in the presence of cure. Biostatistics 3, 547–563.
35. Lim, E., Ali, A., Theodorou, P., Sousa, I., Ashrafian, H., Chamageorgakis, A. D., Henein, M., Diggle, P., and Pepper, J. (2008). A longitudinal study of the profile and predictors of left ventricular mass regression after stentless aortic valve replacement. Annals of Thoracic Surgery. To appear.
36. Lin, H., Turnbull, B. W., McCulloch, C. E., and Slate, E. H. (2002). Latent class models for joint analysis of longitudinal biomarker and event process data. Journal of the American Statistical Association 97, 53–65.
37. Little, R. J. A. (1995). Modeling the drop-out mechanism in repeated-measures studies. Journal of the American Statistical Association 90, 1112–1121.
38. Little, R. and Rubin, D. (2002). Statistical Analysis with Missing Data, 2nd ed. New York: Wiley.
39. Melino, A. and Sueyoshi, G. (1990). A simple approach to the identifiability of the proportional hazards model. Economics Letters 33, 63–68.
40. Ratcliffe, S. J., Guo, W., and Ten Have, T. R. (2004). Joint modelling of longitudinal and survival data via a common frailty. Biometrics 60, 892–899.
41. Rochon, J. and Gillespie, B. W. (2001). A methodology for analysing a repeated measures and survival outcome simultaneously. Statistics in Medicine, 20, 1173–1184.
42. Rubin, D. B. (1976). Inference and missing data. Biometrika 63, 581–592.
43. Ruppert, D., Wand, M. P., and Carroll, R. J. (2003). Semiparametric Regression. Cambridge: Cambridge University Press.
44. Song, X. A., Davidian, M., and Tsiatis, A. A. (2002a). An estimator for the proportional hazards model with multiple longitudinal covariates measured with error. Biostatistics 3, 511–528.
45. Song, X. A., Davidian, M., and Tsiatis, A. A. (2002b). A semiparametric likelihood approach to joint modeling of longitudinal and time-to-event data. Biometrics 58, 742–753.
46. Stein, M. L. (1999). Statistical Interpolation of Spatial Data: Some Theory for Kriging. New York: Springer.
47. Therneau, T. M. and Grambsch, P. M. (2000). Modeling Survival Data: Extending the Cox Model. New York: Springer.
48. Troxel, A. B. (2002). Techniques for incorporating longitudinal measurements into analyses of survival data from clinical trials. Statistical Methods in Medical Research 11, 237–245.
49. Tsiatis, A. A. and Davidian, M. (2004). Joint modelling of longitudinal and time-to-event data: an overview. Statistica Sinica 14, 809–834.
50. Tsiatis, A. A., DeGruttola, V., and Wulfsohn, M. S. (1995). Modelling 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, 27–37.
51. Vonesh, E. F., Greene, T., and Schluchter, M. D. (2006). Shared parameter models for the joint analysis of longitudinal data and event times. Statistics in Medicine 25, 143–163.
52. Wu, M. C. and Carroll, R. J. (1988). Estimation and comparison of changes in the presence of informative right censoring by modelling the censoring process. Biometrics, 44, 175–88.
53. Wulfsohn, M. S. and Tsiatis, A. A. (1997). A joint model for survival and longitudinal data measured with error. Biometrics 53, 330–339.
54. Xu, J. and Zeger, S. L. (2001a). The evaluation of multiple surrogate endpoints. Biometrics 57, 81–87.
55. Xu, J. and Zeger, S. L. (2001b). Joint analysis of longitudinal data comprising repeated measures and time to events. Applied Statistics 50, 375–388.
56. Yu, M. G., Law, N. J, Taylor J. M. G., and Sandler, H. M. (2004). Joint longitudinal-survival-cure models and their application to prostate cancer. Statistica Sinica 14, 835–862.
57. Zeng, D. and Lin, D. Y. (2007). Maximum likelihood estimation in semiparametric regression models with censored data. Journal of the Royal Statistical Society, Series B, 69, 1–30.