Apply the parameters for renege and capacity (by months waited) to projections of capacity and referrals. If needed, or if validating your parameters, include the observed incomplete pathways by the number of months waited for the period prior to the period being projected (eg, a starting position)

Apply the parameters for renege and capacity (by months waited) to projections of capacity and referrals. If needed, or if validating your parameters, include the observed incomplete pathways by the number of months waited for the period prior to the period being projected (eg, a starting position)

Usage

apply_params_to_projections(
  capacity_projections,
  referrals_projections,
  incomplete_pathways = NULL,
  renege_capacity_params,
  max_months_waited,
  surplus_treatment_redistribution_method = "evenly"
)

Arguments

capacity_projections

numeric; vector of projections for capacity for each time step. This must be the same length as referrals_projections

referrals_projections

numeric; vector of projections for reneges for each time step. This must be the same length as capacity_projections

incomplete_pathways

tibble; two column data frame or tibble, with fields called months_waited_id (taking values 0 to the maximum months waited group of interest), and incompletes (the count of the number of incomplete pathways) representing the count of incomplete pathways at timestep 0 (to initialise the model with)

renege_capacity_params

tibble; three column data frame or tibble, with fields called months_waited_id (taking values 0 to the maximum months waited group of interest), and fields called capacity_param and renege_param, which are outputs from the function calibrate_capacity_renege_params()

max_months_waited

integer; the maximum number of months to group patients waiting times by for the analysis. Data are published up to 104 weeks, so 24 is likely to be the maximum useful value for this argument.

surplus_treatment_redistribution_method

string; one of "none", "evenly" or "prioritise_long_waiters"; should cases where the counts of reneges and treatments exceed the counts of people waiting be redistributed, and if so, which method should be used

Value

a tibble with fields for period_id, months_waited_id, calculated_treatments, reneges, incompletes and input_treatments

Examples

max_months <- 4
refs <- create_dummy_data(
  type = "referral",
  max_months_waited = max_months,
  number_periods = 6
)
incomp <- create_dummy_data(
  type = "incomplete",
  max_months_waited = max_months,
  number_periods = 6
)

comp <- create_dummy_data(
  type = "complete",
  max_months_waited = max_months,
  number_periods = 6
)

params <- calibrate_capacity_renege_params(
  referrals = refs,
  incompletes = incomp,
  completes = comp,
  max_months_waited = max_months,
  redistribute_m0_reneges = TRUE
)

set.seed(3)
future_capacity <- sample(300:500, 4, replace = TRUE)
future_referrals <- sample(300:500, 4, replace = TRUE)
incompletes_t0 <- dplyr::tibble(
  months_waited_id = c(0, seq_len(max_months)),
  incompletes = sample(
    100:200,
    length(c(0, seq_len(max_months))),
    replace = TRUE
   )
)

projections <- apply_params_to_projections(
  capacity_projections = future_capacity,
  referrals_projections = future_referrals,
  incomplete_pathways = incompletes_t0,
  renege_capacity_params = params,
  max_months_waited = max_months
)