Advanced SearchSearch Tips
Joint HGLM approach for repeated measures and survival data
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Joint HGLM approach for repeated measures and survival data
Ha, Il Do;
  PDF(new window)
In clinical studies, different types of outcomes (e.g. repeated measures data and time-to-event data) for the same subject tend to be observed, and these data can be correlated. For example, a response variable of interest can be measured repeatedly over time on the same subject and at the same time, an event time representing a terminating event is also obtained. Joint modelling using a shared random effect is useful for analyzing these data. Inferences based on marginal likelihood may involve the evaluation of analytically intractable integrations over the random-effect distributions. In this paper we propose a joint HGLM approach for analyzing such outcomes using the HGLM (hierarchical generalized linear model) method based on h-likelihood (i.e. hierarchical likelihood), which avoids these integration itself. The proposed method has been demonstrated using various numerical studies.
Frailty model;H-likelihood;hierarchical generalized linear model;joint model;random effects;
 Cited by
Breslow, N. E. (1972). Discussion of professor Cox's paper. Journal of the Royal Statistical Society B, 34, 216-217.

Christian, N. J., Ha, I. D. and Jeong, J. (2016). Hierarchical likelihood inference on clustered competing risks data. Statistics in Medicine, 35, 251-267. crossref(new window)

Cox, D. R. (1972). Regression models and life tables (with Discussion). Journal of the Royal Statistical Society B, 74, 187-220.

Elashoff, R. M., Li, G. and Li, N. (2008). A joint model for longitudinal measurements and survival data in the presence of multiple failure types. Biometrics, 64, 762-771. crossref(new window)

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. crossref(new window)

Ha, I. D. and Cho, G.-H. (2012). H-likelihood approach for variable selection in gamma frailty models. Journal of the Korean Data & Information Science Society, 23, 199-207. crossref(new window)

Ha, I. D. and Cho, G.-H. (2015). Variable selection in Poisson HGLMs using h-likelihood. Journal of the Korean Data & Information Science Society, 26, 1513-1521. crossref(new window)

Ha, I. D., Lee, Y. and Song, J. K. (2001). Hierarchical likelihood approach for frailty models. Biometrika, 88, 233-243. crossref(new window)

Ha, I. D. and Lee, Y. (2003). Estimating frailty models via Poisson hierarchical generalized linear models. Journal of Computational and Graphical Statistics, 12, 663-681. crossref(new window)

Ha, I. D. and Noh, M. (2013). A visualizing method for investigating individual frailties using frailtyHL R-package. Journal of the Korean Data & Information Science Society, 24, 931-940. crossref(new window)

Ha, I.D., Park, T. and Lee, Y. (2003). Joint modelling of repeated measures and survival time data. Bio-metrical Journal, 45, 647-658.

Henderson, R., Diggle, P. and Dobson, A. (2000). Joint modelling of longitudinal measurements and event time data. Biostatistics, 1, 465-480. crossref(new window)

Lee, Y. and Nelder, J. A. (1996). Hierarchical generalized linear models (with discussion). Journal of the Royal Statistical Society B, 58, 619-678.

Lee, Y., Nelder, J. A. and Pawitan, Y. (2006). Generalised Linear Models with Random Effects: Unified Analysis via h-Likelihood, Chapman and Hall, London.

Paik, M. C., Lee, Y. and Ha, I. D. (2015). Frequentist inference on random effects based on summarizability. Statistica Sinica, 25, 1107-1132.

Ripatti, S. and Palmgren, J. (2000). Estimation of multivariate frailty models using penalized partial like-lihood. Biometrics, 56, 1016-1022. crossref(new window)

Rizopoulos, D. (2012). Joint models for longitudinal and time-to-event data with applications in R, Chap-man and Hall, London.