joineRML: mjoint_tidiers – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

mjoint_tidiers

Tidying methods for joint models for time-to-event data and multivariate longitudinal data

Description

These methods tidy the coefficients of joint models for time-to-event data and multivariate longitudinal data of the mjoint class from the joineRML package.

Usage

## S3 method for class 'mjoint'
tidy(
  x,
  component = "survival",
  bootSE = NULL,
  conf.int = FALSE,
  conf.level = 0.95,
  ...
)

## S3 method for class 'mjoint'
augment(x, data = x$data, ...)

## S3 method for class 'mjoint'
glance(x, ...)

Arguments

`x`	An object of class `mjoint`.
`component`	Either `survival` (the survival component of the model, default) or `longitudinal` (the longitudinal component).
`bootSE`	An object of class `bootSE` for the corresponding model. If `bootSE = NULL` (the default), the function will use approximate standard error estimates calculated from the empirical information matrix.
`conf.int`	Include (1 - `conf.level`)% confidence intervals? Defaults to `FALSE`.
`conf.level`	The confidence level required.
`...`	extra arguments (not used)
`data`	Original data this was fitted on, in a list (e.g. `list(data)`). This will be extracted from `x` if not given.

Value

All tidying methods return a data.frame without rownames. The structure depends on the method chosen.

tidy returns one row for each estimated fixed effect depending on the component parameter. It contains the following columns:

`term`	The term being estimated
`estimate`	Estimated value
`std.error`	Standard error
`statistic`	Z-statistic
`p.value`	P-value computed from Z-statistic
`conf.low`	The lower bound of a confidence interval on `estimate`, if required
`conf.high`	The upper bound of a confidence interval on `estimate`, if required

augment returns one row for each original observation, with columns (each prepended by a .) added. Included are the columns:

`.fitted_j_0`	population-level fitted values for the j-th longitudinal process
`.fitted_j_1`	individuals-level fitted values for the j-th longitudinal process
`.resid_j_0`	population-level residuals for the j-th longitudinal process
`.resid_j_1`	individual-level residuals for the j-th longitudinal process

See fitted.mjoint and residuals.mjoint for more information on the difference between population-level and individual-level fitted values and residuals.

glance returns one row with the columns

`sigma2_j`	the square root of the estimated residual variance for the j-th longitudinal process
`AIC`	the Akaike Information Criterion
`BIC`	the Bayesian Information Criterion
`logLik`	the data's log-likelihood under the model

Note

If fitting a joint model with a single longitudinal process, please make sure you are using a named list to define the formula for the fixed and random effects of the longitudinal submodel.

Author(s)

Alessandro Gasparini (alessandro.gasparini@ki.se)

Examples

## Not run: 
# Fit a joint model with bivariate longitudinal outcomes
library(joineRML)
data(heart.valve)
hvd <- heart.valve[!is.na(heart.valve$log.grad) &
  !is.na(heart.valve$log.lvmi) &
  heart.valve$num <= 50, ]
fit <- mjoint(
  formLongFixed = list(
    "grad" = log.grad ~ time + sex + hs,
    "lvmi" = log.lvmi ~ time + sex
  ),
  formLongRandom = list(
    "grad" = ~ 1 | num,
    "lvmi" = ~ time | num
  ),
  formSurv = Surv(fuyrs, status) ~ age,
  data = hvd,
  inits = list("gamma" = c(0.11, 1.51, 0.80)),
  timeVar = "time"
)

# Extract the survival fixed effects
tidy(fit)

# Extract the longitudinal fixed effects
tidy(fit, component = "longitudinal")

# Extract the survival fixed effects with confidence intervals
tidy(fit, ci = TRUE)

# Extract the survival fixed effects with confidence intervals based on bootstrapped standard errors
bSE <- bootSE(fit, nboot = 5, safe.boot = TRUE)
tidy(fit, bootSE = bSE, ci = TRUE)

# Augment original data with fitted longitudinal values and residuals
hvd2 <- augment(fit)

# Extract model statistics
glance(fit)

## End(Not run)

joineRML

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

v0.4.5

GPL-3 | file LICENSE

Authors

Graeme L. Hickey [cre, aut] (<https://orcid.org/0000-0002-4989-0054>), Pete Philipson [aut] (<https://orcid.org/0000-0001-7846-0208>), Andrea Jorgensen [ctb] (<https://orcid.org/0000-0002-6977-9337>), Ruwanthi Kolamunnage-Dona [aut] (<https://orcid.org/0000-0003-3886-6208>), Paula Williamson [ctb] (<https://orcid.org/0000-0001-9802-6636>), Dimitris Rizopoulos [ctb, dtc] (data/renal.rda, R/hessian.R, R/vcov.R), Alessandro Gasparini [aut] (<https://orcid.org/0000-0002-8319-7624>), Medical Research Council [fnd] (Grant number: MR/M013227/1)

Initial release

2021-04-21