Joint modelling of longitudinal and time-to-event data with application to predicting abdominal aortic aneurysm growth and rupture

Biometrical Journal

Volumn 53, Issue 5, 2011, Pages 750-763

  Sweeting, Michael J ^a   Thompson, Simon G ^a  

^a MRC BIOSTATISTICS UNIT   (United Kingdom)

Author keywords

Abdominal aortic aneurysm; Hierarchical model; Joint model; Prediction; Shared random effects

Indexed keywords

BAYESIAN NETWORKS; BLOOD VESSELS; HAZARDS; HIERARCHICAL SYSTEMS; RANDOM PROCESSES;

ABDOMINAL AORTIC ANEURYSMS; ANEURYSM GROWTH; ANEURYSM RUPTURE; BAYESIAN; HIERARCHICAL MODEL; JOINT MODELS; RANDOM EFFECTS; RANDOM EFFECTS MODEL; SHARED RANDOM EFFECT; TIME TO EVENTS;

FORECASTING;

EID: 80052418167     PISSN: 03233847     EISSN: 15214036     Source Type: Journal    
DOI: 10.1002/bimj.201100052     Document Type: Article

References (27)

1. Bown, M. J., Sutton, A. J., Bell, P. R. F. and Sayers, R. D. (2002). A meta-analysis of 50 years of ruptured abdominal aortic aneurysm repair. British Journal of Surgery 89, 714-730.
2. Brady, A. R., Thompson, S. G., Fowkes, F. G. R., Greenhalgh, R. M., Powell, J. T. and UK Small Aneurysm Trial Participants (2004). Abdominal aortic aneurysm expansion - risk factors and time intervals for surveillance. Circulation 110, 16-21.
3. Brown, E. R. and Ibrahim, J. G. (2003). A Bayesian semiparametric joint hierarchical model for longitudinal and survival data. Biometrics 59, 221-228.
4. Brown, E. R., Ibrahim, J. G. and DeGruttola, V. (2005). A flexible B-spline model for multiple longitudinal biomarkers and survival. Biometrics 61, 64-73.
5. Bycott, P. and Taylor, J. (1998). A comparison of smoothing techniques for CD4 data measured with error in a time-dependent Cox proportional hazards model. Statistics in Medicine 17, 2061-2077.
6. Chi, Y. Y. and Ibrahim, J. G. (2006). Joint models for multivariate longitudinal and multivariate survival data. Biometrics 62, 432-445.
7. Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society Series B - Statistical Methodology 34, 187-220.
8. Dafni, U. G. and Tsiatis, A. A. (1998). Evaluating surrogate markers of clinical outcome when measured with error. Biometrics 54, 1445-1462.
9. Faucett, C. L. and Thomas, D. C. (1996). Simultaneously modelling censored survival data and repeatedly measured covariates: a Gibbs sampling approach. Statistics in Medicine 15, 1663-1685.
10. Gelman, A. and Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press, Cambridge.
11. 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.
12. Henderson, R., Diggle, P. and Dobson, A. (2000). Joint modelling of longitudinal measurements and event time data. Biostatistics 1, 465-480.
13. Hogan, J. W. and Laird, N. M. (1998). Increasing efficiency from censored survival data by using random effects to model longitudinal covariates. Statistical Methods in Medical Research 7, 28-48.
14. Hsieh, F., Tseng, Y.-K. and Wang, J.-L. (2006). Joint modeling of survival and longitudinal data: Likelihood approach revisited. Biometrics 62, 1037-1043.
15. Little, R. J. A. (1995). Modeling the drop-out mechanism in repeated-measures studies. Journal of the American Statistical Association 90, 1112-1121.
16. Prentice, R. L. (1982). Covariate measurement error and parameter-estimation in a failure time regression-model. Biometrika 69, 331-342.
17. Rizopoulos, D. (2010). JM: an R package for the joint modelling of longitudinal and time-to-event data. Journal of Statistical Software 35, 1-33.
18. Scott, R. A. P., Wilson, N. M., Ashton, H. A. and Kay, D. N. (1995). Influence of screening on the incidence of ruptured abdominal aortic aneurysm: 5-year results of a randomized controlled study. British Journal of Surgery 82, 1066-1070.
19. Self, S. and Pawitan, Y. (1992). Modeling a marker of disease progression and onset of disease. In: Jewell, N., Dietz, K. and Farewell, V., (Eds.), AIDS Epidemiology: Methodological Issues, Birkhäuser, Boston, MA.
20. Spiegelhalter, D., Thomas, A., Best, N. and Lunn, D. (2003). WinBUGS Version 1.4 User Manual. MRC Biostatistics Unit, Cambridge.
21. Sweeting, M. J. and Thompson, S. G. (2011). Making predictions from complex longitudinal data, with application to planning monitoring intervals in a national screening programme. Journal of the Royal Statistical Society Series A - Statistics in Society (To appear).
22. Thompson, S. G., Ashton, H. A., Gao, L. and Scott, R. A. P. (2009). Screening men for abdominal aortic aneurysm: 10 year mortality and cost effectiveness results from the randomised Multicentre Aneurysm Screening Study. British Medical Journal 338, b2307.
23. Tsiatis, A. A. and Davidian, M. (2004). Joint modeling of longitudinal and time-to-event data: an overview. Statistica Sinica 14, 809-834.
24. Tsiatis, A. A., De Gruttola, V. and Wulfsohn, M. S. (1995). Modelling the relationship of survival to longitudinal data measured with error - application to survival and CD4 counts in patients with AIDS. Journal of the American Statistical Association 90, 27-37.
25. UK Small Aneurysm Trial Participants (1995). The UK Small Aneurysm Trial - design, methods and progress. European Journal of Vascular and Endovascular Surgery 9, 42-48.
26. Vickers, A. J., Till, C., Tangen, C. M., Lilja, H. and Thompson, I. M. (2011). An empirical evaluation of guidelines on prostate-specific antigen velocity in prostate cancer detection. Journal of the National Cancer Institute 103, 462-469.
27. Williamson, P. R., Kolamunnage-Dona, R., Philipson, R. and Marson, A. G. (2008). Joint modelling of longitudinal and competing risks data. Statistics in Medicine 27, 6426-6438.