initsSurv_unbalanced


Internal function for generating initial parameters for the survival
sub-model when the data are *unbalanced*, i.e. longitudinal outcomes can be
measured at different times and frequencies within a patient


internal

Fits the joint model proposed by Henderson and colleagues (2000)
<doi:10.1093/biostatistics/1.4.465>, 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).

Graeme L. Hickey

joineRML

Joint Modelling of Multivariate Longitudinal Data and
Time-to-Event Outcomes

initsSurv_unbalanced function

Internal function for generating initial parameters for the survival
sub-model when the data are *unbalanced*, i.e. longitudinal outcomes can be
measured at different times and frequencies within a patient

initsSurv_unbalanced: Internal function for generating initial parameters for the survival sub-model when the data are unbalanced, i.e. longitudinal outcomes can be measured at different times and frequencies within a patient

Description

Usage

Arguments