Compris {tram} | R Documentation |
Competing Risk Regression
Description
An alternative approach to competing risk regression via multivariate transformation models
Usage
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, ...)
Arguments
formula |
an object of class |
data |
an optional data frame, list or environment (or object
coercible by |
subset |
an optional vector specifying a subset of observations to be used in the fitting process. |
weights |
an optional vector of case weights to be used in the fitting
process. Should be |
na.action |
a function which indicates what should happen when the data
contain |
offset |
this can be used to specify an _a priori_ known component to
be included in the linear predictor during fitting. This
should be |
primary |
a character defining the marginal model for the primary event of interest, that is, the first status level. |
competing |
a character defining the marginal models for the remaining competing events. |
NPlogLik |
logical, optimise nonparametric likelihood defined in terms of multivariate probabilities. |
optim |
see |
args |
a list of arguments for |
scale |
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. |
tol |
a tolerance for faking interval censoring. |
... |
addition arguments. |
Details
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).
Value
An object of class mmlt
, allowing to derive marginal time-to-event
distributions for the primary event of interest and all competing events.
References
Claudia Czado and Ingrid Van Keilegom (2023). Dependent Censoring Based on Parametric Copulas. Biometrika, 110(3), 721–738, 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, doi:10.1080/01621459.2022.2161387.