R package joineRML. Joint Modelling of Multivariate Longitudinal Data and Time-to-Event Outcomes. Fits the joint model proposed by Henderson and colleagues (2000) <<a href=”http://dx.doi.org/10.1093/biostatistics/1.4.465”>doi:10.1093/biostatistics/1.4.465</a>>, but extended to the case of multiple continuous longitudinal measures. The time-to-event data is modelled using a Cox proportional hazards regression model with time-varying covariates. The multiple longitudinal outcomes are modelled using a multivariate version of the Laird and Ware linear mixed model. The association is captured by a multivariate latent Gaussian process. The model is estimated using a Monte Carlo Expectation Maximization algorithm. This project is funded by the Medical Research Council (Grant number MR/M013227/1).
Keywords for this software
References in zbMATH (referenced in 6 articles )
Showing results 1 to 6 of 6.
- Murray, James; Philipson, Pete: A fast approximate EM algorithm for joint models of survival and multivariate longitudinal data (2022)
- Cong Xu, Pantelis Z. Hadjipantelis, Jane-Ling Wang: Semi-Parametric Joint Modeling of Survival and Longitudinal Data: The R Package JSM (2020) not zbMATH
- Emma C. Martin, Alessandro Gasparini, Michael J. Crowther: merlin: An R package for Mixed Effects Regression for Linear, Nonlinear and User-defined models (2020) arXiv
- Philipson, Pete; Hickey, Graeme L.; Crowther, Michael J.; Kolamunnage-Dona, Ruwanthi: Faster Monte Carlo estimation of joint models for time-to-event and multivariate longitudinal data (2020)
- Yi, Fengting; Tang, Niansheng; Sun, Jianguo: Regression analysis of interval-censored failure time data with time-dependent covariates (2020)
- Henderson, Robin; Diggle, Peter; Dobson, Angela: Joint modelling of longitudinal measurements and event time data (2000)