sim_prevalence: Estimate prevalence using Monte Carlo simulation.
In rprev: Estimating Disease Prevalence from Registry Data

Description Usage Arguments Value

Estimates prevalent cases at a specific index date by use of Monte Carlo simulation. Simulated cases are marked with age and sex to enable agreement with population survival data where a cure model is used, and calculation of the posterior distributions of each.

sim_prevalence(
  data,
  index,
  starting_date,
  inc_model,
  surv_model,
  age_column = "age",
  N_boot = 1000,
  age_dead = 100
)

`data`	A data frame with the corresponding column names provided in `form`.
`index`	The date at which to estimate point prevalence as a string in the format YYYY-MM-DD.
`starting_date`	The initial date to start simulating prevalence from as a `Date` object. Typically the index date - (Nyears * 365.25). Allows for non-whole year prevalence estimations.
`inc_model`	An object that has a `draw_incident_population` method. See the vignette for further guidance.
`surv_model`	An object that has a `predict_survival_probability` method. See the vignette for further guidance.
`age_column`	A string providing the name of the column that holds patient age. If provided then patients alive at `age_dead` are set to die. This helps combat 'immortal' patients.
`N_boot`	Number of bootstrapped calculations to perform.
`age_dead`	The age at which patients are set to be dead if they are still alive, to prevent 'immortal' patients. Used in conjunction with `age_column`.

A list with the following attributes:

`results`	A data.table containing the simulated incident populations from each simulation along with their covariates and survival status at the index.
`full_surv_model`	The survival model built on the full registry data set.
`full_inc_model`	The incidence model built on the full registry data set.
`surv_models`	A list containing survival models built on each bootstrap sample.
`inc_models`	A list containing incidence models built on each bootstrap sample.