A generic function for creating a `PsmCurves`

object.

```
create_PsmCurves(object, ...)
# S3 method for flexsurvreg_list
create_PsmCurves(
object,
input_data,
n = 1000,
uncertainty = c("normal", "bootstrap", "none"),
est_data = NULL,
...
)
# S3 method for params_surv_list
create_PsmCurves(object, input_data, ...)
```

- object
An object of the appropriate class containing either fitted survival models or parameters of survival models.

- ...
Further arguments passed to or from other methods. Passed to

`create_params.partsurvfit()`

when`object`

is of class`flexsurvreg_list`

.- input_data
An object of class

`expanded_hesim_data`

returned by`expand.hesim_data()`

. Must be expanded by the data tables`"strategies"`

and`"patients"`

.- 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`"bootstrap"`

, then parameters are bootstrapped using`bootstrap`

. If`"none"`

, then only point estimates are returned.- est_data
A

`data.table`

or`data.frame`

of estimation data used to fit survival models during bootstrap replications.

Returns an `R6Class`

object of class `PsmCurves`

.

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 `PsmCurves`

and `Psm`

for examples. `PsmCurves`

provides
an example in which a model is parameterized both with
(via `create_PsmCurves.flexsurvreg_list()`

) and without (via
`create_PsmCurves.params_surv_list()`

) access to patient-level data.
The `Psm`

example shows how state probabilities, costs, and utilities can
be computed from predicted survival curves.