Description Usage Arguments Value Examples

Runs a cohort discrete event simulation

1 2 3 4 | ```
cohort_simulation(models, newdata, trans_mat, start_time = 0,
start_state = 1, time_limit = NULL, tcovs = NULL, M = 1000,
ci = FALSE, ci_margin = 0.95, agelimit = FALSE, agecol = "age",
agescale = 365.25)
``` |

`models` |
List of |

`newdata` |
Data frame with covariates of individual to simulate times for. Must contain all fields required by models. |

`trans_mat` |
Transition matrix, such as that used in |

`start_time` |
Entry times of individuals specified in |

`start_state` |
The starting state of the individuals specified in |

`time_limit` |
The maximum time to run the simulation for. If not provided then the simulation runs until all the individuals have obtained a sink state. |

`tcovs` |
As in |

`M` |
Number of times to run the simulations in order to obtain confidence interval estimates. |

`ci` |
Whether to calculate confidence intervals. See |

`ci_margin` |
Confidence interval range to use if |

`agelimit` |
Whether to automatically assign people to an 'early death' state.
This is useful as otherwise individuals can be assigned unrealistic time-to-events due to the
nature of sampling times from a random number distribution.
If this value is |

`agecol` |
The name of the column in |

`agescale` |
Any multiplication to be applied to the age covariate to put it onto the same time-scale as the simulation. This is often useful as time-to-event may be measured on a day-based time-scale while age is typically measured in years. |

A data frame with state entry times for each individual.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
library(multistateutils)
library(mstate)
library(flexsurv)
# Convert data to long
data(ebmt3)
tmat <- trans.illdeath()
long <- msprep(time=c(NA, 'prtime', 'rfstime'),
status=c(NA, 'prstat', 'rfsstat'),
data=ebmt3,
trans=tmat,
keep=c('age', 'dissub'))
# Fit parametric models
models <- lapply(1:3, function(i) {
flexsurvreg(Surv(time, status) ~ age + dissub, data=long, dist='weibull')
})
sim <- cohort_simulation(models, ebmt3, tmat)
``` |

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