An alternative approach to competing risk regression via multivariate transformation models
Compris(formula, data, subset, weights, na.action, offset,
primary = c("Coxph", "Colr", "BoxCox"),
competing = switch(primary, Coxph = "weibull",
Colr = "loglogistic",
BoxCox = "lognormal"),
NPlogLik = FALSE,
optim = mmltoptim(), args = list(seed = 1, M = 1000),
scale = FALSE, tol = 0.001, ...)An object of class mmlt, allowing to derive marginal time-to-event
distributions for the primary event of interest and all competing events.
an object of class "formula": a symbolic description
of the model structure to be
fitted. The details of model specification are given under
Details and in the package vignette.
an optional data frame, list or environment (or object
coercible by as.data.frame to a data frame) containing the
variables in the model. If not found in data, the
variables are taken from environment(formula).
an optional vector specifying a subset of observations to be used in the fitting process.
an optional vector of case weights to be used in the fitting
process. Should be NULL or a numeric vector. If present,
the weighted log-likelihood is maximised.
a function which indicates what should happen when the data
contain NAs. The default is set to na.omit.
this can be used to specify an _a priori_ known component to
be included in the linear predictor during fitting. This
should be NULL or a numeric vector of length equal to the
number of cases.
a character defining the marginal model for the primary event of interest, that is, the first status level.
a character defining the marginal models for the remaining competing events.
logical, optimise nonparametric likelihood defined in terms of multivariate probabilities.
see mmlt.
a list of arguments for lpmvnorm.
logical defining if variables in the linear predictor shall be scaled. Scaling is internally used for model estimation, rescaled coefficients are reported in model output.
a tolerance for faking interval censoring.
addition arguments.
This is a highly experimental approach to an alternative competing risk regression framework described by Czado and Van Keilegom (2023) and Deresa and Van Keilegom (2023).
Claudia Czado and Ingrid Van Keilegom (2023). Dependent Censoring Based on Parametric Copulas. Biometrika, 110(3), 721--738, tools:::Rd_expr_doi("10.1093/biomet/asac067").
Negera Wakgari Deresa and Ingrid Van Keilegom (2023). Copula Based Cox Proportional Hazards Models for Dependent Censoring. Journal of the American Statistical Association, tools:::Rd_expr_doi("10.1080/01621459.2022.2161387").