heart.valve


This is longitudinal data on an observational study on detecting effects of
different heart valves, differing on type of tissue, implanted in the aortic
position. The data consists of longitudinal measurements on three different
heart function outcomes, after surgery occurred. There are several baseline
covariates available, and also survival data.


data

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

heart.valve function

This is a data frame in the unbalanced format, that is, with one row
  per observation. The data consists in columns for patient identification,
  time of measurements, longitudinal multiple longitudinal measurements,
  baseline covariates, and survival data. The column names are identified as
  follows: <dl> </dl>

Format

Mr Eric Lim (<a href="http://www.drericlim.com">http://www.drericlim.com</a>) and
  <code><a rd-options="joineR" href="/link/heart.valve?package=joineRML&version=0.1.1&to=joineR" data-mini-rdoc="joineR::heart.valve">heart.valve</a></code>

Source

Mr Eric Lim (<a href = 'http://www.drericlim.com'>http://www.drericlim.com</a>) and
  <code><a rd-options='joineR' href='heart.valve'>heart.valve</a></code>

heart.valve: Aortic valve replacement surgery data

Description

Usage

Arguments

Format

Source

References

See Also