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Table 1 The primary argumentsa with descriptions for the mjoint() function in the R package joineRML

From: joineRML: a joint model and software package for time-to-event and multivariate longitudinal outcomes

Argument Description
formLongFixed a list of formulae for the fixed effects component of each longitudinal outcome. The left hand-hand side defines the response, and the right-hand side specifies the fixed effect terms.
formLongRandom a list of one-sided formulae specifying the model for the random effects effects of each longitudinal outcome.
formSurv a formula specifying the proportional hazards regression model (not including the latent association structure).
data a list of data.frame objects for each longitudinal outcome in which to interpret the variables named in the formLongFixed and formLongRandom. The list structure enables one to include multiple longitudinal outcomes with different measurement protocols. If the multiple longitudinal outcomes are measured at the same time points for each patient (i.e. t ijk =t ij k), then a single data.frame object can be given instead of a list. It is assumed that each data frame is in long format.
survData (optional) a data.frame in which to interpret the variables named in the formSurv. If survData is not given, then mjoint() looks for the time-to-event data in data.
timeVar a character string indicating the time variable in the linear mixed effects model.
inits (optional) a list of initial values for some or all of the parameters estimated in the model.
control (optional) a list of control parameters. These allow for the control of ε0, ε1, and ε2 in (7) and (8); the choice of N, δ, and convergence criteria; the maximum number of MCEM iterations, and the minimum number of MCEM iterations during burn-in. Additionally, the control argument gammaOpt can be used to specify whether a one-step Newton-Raphson (=~NR~) or Gauss-Newton-like (=~GN~) update should be used for the M-step update of γ.
  1. amjoint() also takes the optional additional arguments verbose, which if TRUE allows for monitoring updates at each MCEM algorithm iteration, and pfs, which if FALSE can force the function not to calculate post-fit statistics such as the BLUPs and associated standard errors of the random effects and approximate standard errors of the model parameters. In general, these arguments are not required