MJMbamlss (version 0.1.0)

mjm_bamlss: Family for Flexible Multivariate Joint Model

Description

This function specifies the different predictors and link functions as well as the corresponding transform/updating/sampling functions as well as the predict function.

Usage

mjm_bamlss(...)

Value

An object of class family.bamlss.

Arguments

...

All arguments are actually hard coded as needed by the implementation.

Details

Family object to fit a flexible additive joint model for multivariate longitudinal and survival data under a Bayesian approach using multivariate functional principal components as presented in Volkmann, Umlauf, Greven (2023).

References

Volkmann, A., Umlauf, N., Greven, S. (2023). Flexible joint models for multivariate longitudinal and time-to-event data using multivariate functional principal components. <arXiv:2311.06409>

Examples

library(mgcv)
library(bamlss)
data(pbc_subset)
mfpca <- preproc_MFPCA(pbc_subset, uni_mean = paste0(
  "logy ~ 1 + sex + drug + s(obstime, k = 5, bs = 'ps') + ",
  "s(age, k = 5, bs = 'ps')"),
  pve_uni = 0.99, nbasis = 5, weights = TRUE, save_uniFPCA = TRUE)
pbc_subset <- attach_wfpc(mfpca, pbc_subset, n = 2)
mfpca_list <- list(
  list(functions = funData::extractObs(mfpca$functions, 1),
       values = mfpca$values[1]),
  list(functions = funData::extractObs(mfpca$functions, 2),
       values = mfpca$values[2]))

# Model formula
f <- list(
  Surv2(survtime, event, obs = logy) ~ -1 +
    s(survtime, k = 5, bs = "ps", xt = list("scale" = FALSE)),
  gamma ~ 1 + sex + drug + s(age, k = 5, bs = 'ps'),
  mu ~ -1 + marker + sex:marker + drug:marker +
    s(obstime, by = marker, xt = list("scale" = FALSE), k = 5, bs = "ps") +
    s(age, by = marker, xt = list("scale" = FALSE), k = 5, bs = "ps") +
    s(id, fpc.1, bs = "unc_pcre",
      xt = list("mfpc" = mfpca_list[[1]], scale = "FALSE")) +
    s(id, fpc.2, bs = "unc_pcre",
      xt = list("mfpc" = mfpca_list[[2]], scale = "FALSE")),
  sigma ~ -1 + marker,
  alpha ~ -1 + marker
)

# Model fit
b <- bamlss(f, family = mjm_bamlss, data = pbc_subset,
            timevar = "obstime", maxit = 15, n.iter = 15, burnin = 2,
            thin = 2)