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

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

Alessandro Gasparini (alessandro.gasparini@ki.se)

Examples

if (FALSE) { # \dontrun{
# 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)
} # }