Calculate present value for a payoff with dynamic pricing and dynamic uptake

Present value of a payoff affected by dynamic pricing, with uptake across multiple cohorts (dynamic uptake)

dynpv(
  uptakes = 1,
  payoffs,
  horizon = length(payoffs),
  tzero = 0,
  prices = rep(1, length(payoffs) + tzero),
  discrate = 0
)

Arguments

uptakes: Vector of patient uptake over time
payoffs: Vector of payoffs of interest (numeric vector)
horizon: Time horizon for the calculation (length must be less than or equal to the length of payoffs)
tzero: Time at the date of calculation, to be used in lookup in prices vector
prices: Vector of price indices through the time horizon of interest
discrate: Discount rate per timestep, corresponding to price index

Value

Two lists named inputs and results. The inputs list contains a list of the following parameters called with the function: uptakes, payoffs, horizon, tzero, prices, and discrate. The results list contains the following elements:

ncoh: Number of cohorts of uptaking patients
uptake: Total number of uptaking patients
calc: Tibble of calculation results
cohpv: Tibble of summarized calculation results for each uptake cohort
total: Total present value
mean: Average present value per uptaking patient (=total/uptake)

Examples

# Obtain dataset
democe <- get_dynfields(
   heemodel = oncpsm,
   payoffs = c("cost_daq_new", "cost_total", "qaly"),
   discount = "disc"
   )

# Obtain short payoff vector of interest
payoffs <- democe |>
   dplyr::filter(int=="new", model_time<11) |>
   dplyr::mutate(cost_oth = cost_total - cost_daq_new)
Nt <- nrow(payoffs)

# Example calculation
dynpv(
   uptakes = rep(1, Nt),
   payoffs = payoffs$cost_oth,
   prices = 1 + (0:(Nt-1))*0.05,
   discrate = 0.08 
)
#> $inputs
#> $inputs$uptakes
#>  [1] 1 1 1 1 1 1 1 1 1 1
#> 
#> $inputs$payoffs
#>  [1] 354.8228 354.3419 353.7327 352.9161 351.8567 350.5507 349.0109 347.2584
#>  [9] 345.3166 343.2095
#> 
#> $inputs$horizon
#> [1] 10
#> 
#> $inputs$tzero
#> [1] 0
#> 
#> $inputs$prices
#>  [1] 1.00 1.05 1.10 1.15 1.20 1.25 1.30 1.35 1.40 1.45
#> 
#> $inputs$discrate
#> [1] 0.08
#> 
#> 
#> $results
#> $results$ncoh
#> [1] 10
#> 
#> $results$uptake
#> [1] 10
#> 
#> $results$calc
#> # A tibble: 55 × 9
#>        j     k     l     t    uj    pk     R     v    pv
#>    <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1     1     1     0     1     1  355.  1    1      355.
#>  2     1     2     0     2     1  354.  1.05 0.926  344.
#>  3     1     3     0     3     1  354.  1.1  0.857  334.
#>  4     1     4     0     4     1  353.  1.15 0.794  322.
#>  5     1     5     0     5     1  352.  1.2  0.735  310.
#>  6     1     6     0     6     1  351.  1.25 0.681  298.
#>  7     1     7     0     7     1  349.  1.3  0.630  286.
#>  8     1     8     0     8     1  347.  1.35 0.583  274.
#>  9     1     9     0     9     1  345.  1.4  0.540  261.
#> 10     1    10     0    10     1  343.  1.45 0.500  249.
#> # ℹ 45 more rows
#> 
#> $results$cohpv
#> # A tibble: 10 × 3
#>        j tzero   spv
#>    <int> <dbl> <dbl>
#>  1     1     0 3033.
#>  2     2     0 2688.
#>  3     3     0 2351.
#>  4     4     0 2022.
#>  5     5     0 1703.
#>  6     6     0 1393.
#>  7     7     0 1093.
#>  8     8     0  804.
#>  9     9     0  525.
#> 10    10     0  257.
#> 
#> $results$total
#> [1] 15869.53
#> 
#> $results$mean
#> [1] 1586.953
#> 
#>