CohortDtstmTrans objectA generic function for creating an object of class CohortDtstmTrans.
create_CohortDtstmTrans(object, ...)# S3 method for multinom_list
create_CohortDtstmTrans(
object,
input_data,
trans_mat,
n = 1000,
uncertainty = c("normal", "none"),
...
)
# S3 method for msm
create_CohortDtstmTrans(
object,
input_data,
cycle_length,
n = 1000,
uncertainty = c("normal", "none"),
...
)
# S3 method for params_mlogit_list
create_CohortDtstmTrans(object, input_data, trans_mat, ...)
An object of the appropriate class containing either a fitted statistical model or model parameters.
Further arguments passed to CohortDtstmTrans$new() in
CohortDtstmTrans.
An object of class expanded_hesim_data returned by
expand.hesim_data()
A transition matrix describing the states and transitions
in a discrete-time multi-state model. See CohortDtstmTrans.
Number of random observations to draw. Not used if uncertainty = "none".
Method determining how parameter uncertainty should be handled.
If "normal", then parameters are randomly drawn from their multivariate normal
distribution. If "none", then only point estimates are returned.
The length of a model cycle in terms of years. The default is 1 meaning that model cycles are 1 year long.
Disease models may either be created from a fitted statistical
model or from a parameter object. In the case of the former, input_data
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 predict()
methods (e.g., see predict.lm()). In other words, variables used in the
formula of the statistical model must also be in input_data.
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
(e.g., with params_surv or 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 input_data, then
an error will be thrown.
See CohortDtstmTrans for examples.