gmvjoint-package: Joint Models of Survival and Multivariate Longitudinal Data

In gmvjoint: Joint Models of Survival and Multivariate Longitudinal Data

Joint Models of Survival and Multivariate Longitudinal Data

Description

gmvjoint allows the user to fit joint models of survival and multivariate longitudinal data. The longitudinal data is specified by generalised linear mixed models (GLMMs). The joint models are fit via maximum likelihood using an approximate EM algorithm first proposed by Bernhardt et al. (2015). The GLMMs are specified using the same syntax as for package glmmTMB Brooks et al. (2017). The joint models themselves are then the flexible extensions to those in e.g. Wulfsohn and Tsiatis (1997). The user is able to simulate data under many different response types.

Author(s)

James Murray <[e-mail]>

References

Bernhardt PW, Zhang D and Wang HJ. A fast EM Algorithm for Fitting Joint Models of a Binary Response to Multiple Longitudinal Covariates Subject to Detection Limits. Computational Statistics and Data Analysis 2015; 85; 37–53

Mollie E. Brooks, Kasper Kristensen, Koen J. van Benthem, Arni Magnusson, Casper W. Berg, Anders Nielsen, Hans J. Skaug, Martin Maechler and Benjamin M. Bolker (2017). glmmTMB Balances Speed and Flexibility Among Packages for Zero-inflated Generalized Linear Mixed Modeling. The R Journal, 9(2), 378-400.

Murray, J and Philipson P. A fast approximate EM algorithm for joint models of survival and multivariate longitudinal data.Computational Statistics and Data Analysis 2022

Wulfsohn MS, Tsiatis AA. A joint model for survival and longitudinal data measured with error. Biometrics. 1997; 53(1), 330-339.

gmvjoint documentation built on Oct. 6, 2024, 1:07 a.m.