A generic function for creating an object of class
create_IndivCtstmTrans(object, ...) # S3 method for flexsurvreg_list create_IndivCtstmTrans( object, input_data, trans_mat, clock = c("reset", "forward"), n = 1000, uncertainty = c("normal", "none"), ... ) # S3 method for flexsurvreg create_IndivCtstmTrans( object, input_data, trans_mat, clock = c("reset", "forward"), n = 1000, uncertainty = c("normal", "none"), ... ) # S3 method for params_surv create_IndivCtstmTrans( object, input_data, trans_mat, clock = c("reset", "forward", "mix", "mixt"), reset_states = NULL, transition_types = NULL, ... ) # S3 method for params_surv_list create_IndivCtstmTrans( object, input_data, trans_mat, clock = c("reset", "forward", "mix", "mixt"), reset_states = NULL, transition_types = NULL, ... )
An object of the appropriate class containing either a fitted multi-state model or parameters of a multi-state model.
Further arguments passed to
An object of class
expanded_hesim_data returned by
"reset" for a clock-reset model, "forward" for a clock-forward model,
"mix" for a mixture by state, and "mixt" for a mixture by transition
of clock-reset and clock-forward models. See the field
Number of random observations to draw. Not used if
uncertainty = "none".
Method determining how parameter uncertainty should be handled.
"normal", then parameters are randomly drawn from their multivariate normal
"none", then only point estimates are returned.
A vector denoting the states in which time resets. See the field
A vector denoting the type for each transition. See the field
R6Class object of class
Disease models may either be created from a fitted statistical
model or from a parameter object. In the case of the former,
is a data frame like object that is used to look for variables from
the statistical model that are required for simulation. In this sense,
input_data is very similar to the
newdata argument in most
methods (e.g., see
predict.lm()). In other words, variables used in the
formula of the statistical model must also be in
In the case of the latter, the columns of
input_data must be named in a
manner that is consistent with the parameter object. In the typical case
params_mlogit), the parameter object
contains coefficients from a regression model, usually stored as matrix
where rows index parameter samples (i.e., for a probabilistic sensitivity
analysis) and columns index model terms. In such instances, there must
be one column from
input_data with the same name as each model term in the
coefficient matrix; that is, the columns in
input_data are matched with
the columns of the coefficient matrices by name. If there are model terms
in the coefficient matrices that are not contained in
an error will be thrown.