mjoint_tidiers: Tidying methods for joint models for time-to-event data and...

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

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 ([e-mail])

Alessandro Gasparini ([e-mail])

Alessandro Gasparini ([e-mail])

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 documentation built on Jan. 22, 2023, 1:18 a.m.