mjm_bamlss {MJMbamlss}

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(...)

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).

Value

An object of class family.bamlss.

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)