A generic function for creating a
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, ...)
An object of the appropriate class containing either fitted survival models or parameters of survival models.
An object of class
Number of random observations to draw. Not used if
Method determining how parameter uncertainty should be handled.
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.
Psm for examples.
an example in which a model is parameterized both with
create_PsmCurves.flexsurvreg_list()) and without (via
create_PsmCurves.params_surv_list()) access to patient-level data.
Psm example shows how state probabilities, costs, and utilities can
be computed from predicted survival curves.