Joint models for longitudinal data: Introduction and overview

Longitudinal Data Analysis

Volumn , Issue , 2008, Pages 319-326

  Verbeke, Geert ^a   Davidian, Marie ^b  

^a UNIVERSITY OF LEUVEN   (Belgium)

^b NORTH CAROLINA STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85054607046     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Chapter

References (6)

1. DeGruttola, V. and Tu, X. M. (1994). Modeling progression of CD-4 lymphocyte count and its relationship to survival time. Biometrics 50, 1003–1014.
2. Faucett, C. J. and Thomas, D. C. (1996). Simultaneously modeling censored survival data and repeatedly measured covariates: A Gibbs sampling approach. Statistics in Medicine 15, 1663–1685.
3. Galecki, A. T. (1994). General class of covariance structures for two or more repeated factors in longitudinal data analysis. Communications in Statistics — Theory and Methods 23, 3105–3119.
4. Molenberghs, G. and Verbeke, G. (2005). Models for Discrete Longitudinal Data. New York: Springer.
5. Tsiatis, A. A. and Davidian, M. (2004). Joint modeling of longitudinal and time-to-event data: An overview. Statistica Sinica 14, 809–834.
6. Tsiatis, A. A., DeGruttola, V., and Wulfsohn, M. S. (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, 27–37.