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, ...)

## Arguments

object |
An object of the appropriate class containing either a
fitted statistical model or model parameters. |

... |
Further arguments passed to `CohortDtstmTrans$new()` in
`CohortDtstmTrans` . |

input_data |
An object of class `expanded_hesim_data` returned by
`expand.hesim_data()` |

trans_mat |
A transition matrix describing the states and transitions
in a discrete-time multi-state model. See `CohortDtstmTrans` . |

n |
Number of random observations to draw. Not used if `uncertainty = "none"` . |

uncertainty |
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. |

cycle_length |
The length of a model cycle in terms of years. The default
is 1 meaning that model cycles are 1 year long. |

## Details

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 also