Title:	Technology Appraisal Toolbox for Health Economic Evaluations in the Netherlands
Version:	1.0.0
Description:	Functions to support economic modelling in R based on the methods of the Dutch guideline for economic evaluations in healthcare https://www.zorginstituutnederland.nl/documenten/2024/01/16/richtlijn-voor-het-uitvoeren-van-economische-evaluaties-in-de-gezondheidszorg, CBS data https://www.cbs.nl/, and OECD data https://www.oecd.org/en.html.
License:	MIT + file LICENSE
Encoding:	UTF-8
RoxygenNote:	7.3.3
Depends:	R (≥ 4.1)
LazyData:	true
Imports:	rlang, dplyr (≥ 1.1.4), assertthat (≥ 0.2.1), tidyr (≥ 1.3.0)
Suggests:	cbsodataR, lubridate, tibble, lifecycle, here, testthat (≥ 3.0.0), knitr, rmarkdown
Config/testthat/edition:	3
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2025-12-15 09:56:22 UTC; 85304spe
Author:	Stijn Peeters [aut, cre], Eline Krijkamp [aut], Frederick Thielen [aut]
Maintainer:	Stijn Peeters <s.b.peeters@eshpm.eur.nl>
Repository:	CRAN
Date/Publication:	2025-12-15 10:10:02 UTC

Calculate the discounted (net present value) of costs or effects

Description

The apply_discounting function is designed to calculate the net present value of future costs or effects using a constant discount rate, following the Dutch guidelines for economic evaluations in health care. (section 2.6.1.2 version 2024). Here's a breakdown of how the function works:

Usage

apply_discounting(
  values,
  discount_rate = c("costs", "effects"),
  times,
  aggregate = FALSE,
  digits = NULL
)

Arguments

Details

This function ensures consistent application of discount rates in cost-effectiveness analyses, in line with Dutch guidelines. Custom rates can be specified when needed.

Examples

# NO Discounting in First Year (t starts at 0)
# constant cost of 100 for 3 years
apply_discounting(values = rep(100, 3), discount_rate = "costs", times = c(0, 1, 2))

# WITH discounting in first year (t starts at 1)
# Constant cost of 100 for 3 years,
apply_discounting(values = rep(100, 3),
                   discount_rate = "costs",
                   times = c(1, 2, 3))

# Present value of 100 euro in 3 years
apply_discounting(values = 100, discount_rate = "costs", times = 3)

# Custom Discount Rate
# discount rate of 4%, no discounting in first year
apply_discounting(values = rep(100, 3),
                  discount_rate = 0.04,
                  times = c(0, 1, 2))
# This will give you a messages to inform you about the different discount rate

# Same applies to utility values
# Utility values with aggregation - NO discounting in first year
apply_discounting(values = c(0.98, 0.82, 0.79),
                  discount_rate = "effect",
                  times = c(0, 1, 2),
                  aggregate = TRUE, digits = 3)

Example model output for the sick-sicker model

Description

This data structure with several lists and vectors collecting a subset of the model outputs from the Sick-Sicker model intended for demonstrating the discounting in this package. The full model is described in the DARTH “Sick-Sicker” example. Source code can be found here: https://github.com/DARTH-git/cohort-modeling-tutorial-intro

Usage

data_model_output_sick_sicker

Format

An object of class list of length 9.

Details

For the full reference read: Alarid-Escudero F, Krijkamp EM, Enns EA, Yang A, Hunink MGM, Pechlivanoglou P, Jalal H. An Introductory Tutorial on Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example. Medical Decision Making, 2023;43(1):3-20. https://doi.org/10.1177/0272989X221103163

These data can be used for demonstrating how to apply some of the package functions like discounting for the results of a model-based cost-effectiveness model.

#' @format A list with the following components

l_m_M_annual

Annual transition matrix for sick-sicker model.

l_m_M_monthly

Monthly transition matrix for sick-sicker model.

v_wcc_annual

Half-cycle correction weights for annual model.

v_wcc_monthly

Half-cycle correction weights for monthly model.

v_names_str

Names of the intervention strategies used in the model.

l_u_annual

Annual utilities for each health state.

l_u_monthly

Monthly utilities for each health state.

l_c_annual

Annual costs for each health state.

l_c_monthly

Monthly costs for each health state.

Examples

# Load the dataset
data("data_model_output_sick_sicker.rda")

# Load list in global environment
list2env(data_model_output_sick_sicker, envir = .GlobalEnv)
# Explore the available objects
ls(pattern = "l_|v_")

# View names of health states
v_names_str

# Inspect the first few cycles of the annual Markov trace for the first strategy
head(l_m_M_annual[[1]])

# Compare dimensions of annual and monthly traces
dim(l_m_M_annual[[1]])
dim(l_m_M_monthly[[1]])

# Apply the half cycle correction
## Loop through each strategy and calculate total utilities and costs ----

v_tot_qaly <- v_tot_cost <- c()
for (i in 1:length(v_names_str)) {
 v_u_str <- l_u_monthly[[i]]   # select the vector of state utilities for the i-th strategy
 v_c_str <- l_c_monthly[[i]]   # select the vector of state costs for the i-th strategy

  ###* Expected QALYs and costs per cycle
  ##* Vector of QALYs and Costs
  #* Apply state rewards
  v_qaly_str <- l_m_M_monthly[[i]] %*% v_u_str # sum the utilities of all states for each cycle
  v_cost_str <- l_m_M_monthly[[i]] %*% v_c_str # sum the costs of all states for each cycle

  ###*  Total expected QALYs and costs per strategy and apply half-cycle correction (if applicable)
  #* QALYs
  v_tot_qaly[i] <- t(v_qaly_str) %*% v_wcc_monthly
  #* Costs
  v_tot_cost[i] <- t(v_cost_str) %*% v_wcc_monthly
}

Annual depreciation + interest for medical equipment

Description

Compute the annuity factor and the annual depreciation-and-interest charge for medical equipment, following Section 7.3 of the Dutch costing manual; k = annual depreciation and interest expense jaarlijkse afschrijvings- en rentekosten

Let V be replacement value, R the salvage value, n the amortisation period (years), and i the interest rate (per year). The annuity factor is:

a_{n,i} = \frac{1}{i}*\bigg(1-\frac{1}{(1 + i)^{n}}\bigg)

#' The annual charge k is:

k = \frac{V - \frac{R}{(1 + i)^n}}{a_{n,i}}

Usage

depreciation_interest(
  v_replace_val,
  r_salvage_val,
  n_amortisation_period = 10,
  i_interest_rt = 0.025,
  output = c("dataframe", "annuity_factor", "annual_cost")
)

Arguments

Value

• If output = "dataframe": a data.frame with two columns: ⁠Annuity factor⁠ and ⁠Yearly depreciation and interest costs⁠.

• If output = "annuity_factor": a single numeric (the annuity factor).

• If output = "annual_cost": a single numeric (the annual charge k).

Examples

# Both values as a data frame (defaults: n=10, i=2.5%)
depreciation_interest(v_replace_val = 50000, r_salvage_val = 5000)

# Only the annuity factor
depreciation_interest(50000, 5000, output = "annuity_factor")

# Only the annual charge (k)
depreciation_interest(50000, 5000, output = "annual_cost")

# Zero interest (uses the i -> 0 limit): a = n, k = (V - R)/n
depreciation_interest(50000, 5000, n_amortisation_period = 8,
                      i_interest_rt = 0,
                      output = "dataframe")

Consumer Price Index (CPI) data from CBS

Description

A subset data frame of Consumentenprijzen; prijsindex 2015=100 from CBS. Identifier: 83131NED

Usage

df_cpi_combined

Format

df_cpi_combined

A data frame with 11 rows and 8 columns:

Year from

Year from in case of two consecutive years

Year to

Year ending in case of two consecutive years

Percentage

Percentage change in case of two consecutive years

Factor

Factor for multiplication in case of two consecutive years

Year from'

Year starting from, in the case of a range, the year up to the maximum year

Year to'

Year ending in case of a range, the year up to the maximum year

Percentage'

Percentage for multiplication in case of a range, the year up to the maximum year

Factor'

Factor for multiplication in case of a range, the year up to the maximum year

Source

https://www.cbs.nl/nl-nl/cijfers

Job vacancy data from CBS

Description

A subset data frame of job vacancies; SBI 2008; by economic activity and company size from CBS. Identifier: 80472NED

Usage

df_fp

Format

df_fp

A data frame with 27 rows and 7 columns:

Year

Year

Filled vacancies

Vervulde vacatures

Open vacancies

Openstaande vacatures

Friction period in days

Calculation of the friction period in days

Friction period in weeks

Calculation of the friction period in weeks

Friction period days average over 5 years

Calculation of the 5 year average friction period in days

Friction period weeks average over 5 years

Calculation of the 5 year average friction period in weeks

Source

https://www.cbs.nl/nl-nl/cijfers

Purchasing Power Parity (PPP) data from OECD

Description

A subset data frame of the OECD data set containing the Purchasing Power Parity (PPP) data.

Usage

df_ppp

Format

df_ppp

A data frame with 64 rows and 2 columns:

Year

Year of PPP

PPP

Purchase Power Parity

Source

https://sdmx.oecd.org/public

Complete data of reference values from the costing manual

Description

Complete data of reference values from the costing manual

Usage

df_ref_prices

Format

df_ref_prices

A data frame with 160 rows and 7 columns:

Domain

Care domain, for example medical services

Category

Service category such as nursing, intensive care or day treatment

Unit

Description of the care unit or service

short_var

Short variable name used in the package

2022

Reference price for the year 2022

2023

Reference price for the year 2023

2024

Reference price for the year 2024

Source

https://sdmx.oecd.org/public

Friction period lookup (days/weeks; 1-year or 5-year average)

Description

Return friction periods from the internal CBS-based table.

Usage

friction_period(
  year = NULL,
  units = "weeks",
  avg = "5yr",
  output = c("tibble", "value"),
  data = tatooheene::df_fp
)

Arguments

Value

A tibble when output = "tibble", or a single numeric when output = "value".

Examples

# All years, 5-year average in weeks (default)
friction_period()

# Specific year (2019), weeks 5-year average
friction_period(year = 2019)

# Days (1-year) for multiple years
friction_period(year = 2018:2020, units = "days", avg = "1yr")

# Single numeric value (requires one year + one combo)
friction_period(year = 2019, units = "weeks", avg = "5yr", output = "value")

A function to obtain the Dutch PPP factor values in International Dollar (INT$)

Description

This function downloads the Purchasing Power Parity (PPP) factor values for the Netherlands from the OECD website per year in International Dollar (Int$).

Usage

nl_ppp(year = "all")

Arguments

Value

A dataframe or value with the PPP factor values for the specified years.

Examples

# Example usage of the nl_ppp function
nl_ppp(year = 2019)

A function to calculate the Consumer Price Index (CPI) for a given year range.

Description

This function provides the Consumer Price Index (CPI) for a given year range both in a factor or dataframe based on CBS data and further described in 2.6.1.1 of the Dutch EE guideline

Usage

nl_price_index(
  start_year = 2013,
  end_year = 2023,
  output = c("table", "factor")
)

Arguments

Value

Dataframe or factor with CPI data from start year to end year

Examples

# Example usage of the nl_price_index function
# Get the CPI factor from 2013 to 2023
nl_price_index(start_year = 2013, end_year = 2023, output = "factor")

# Get the CPI table from 2013 to 2023
nl_price_index(start_year = 2013, end_year = 2023, output = "table")

A function to download the Reference prices of the Dutch Costing Manual for one or multiple years

Description

This function downloads the Reference prices of the Dutch Costing Manual for one or multiple years. The prices are available in Euro (EUR) or International Dollar (INT$).

Usage

nl_ref_prices(
  year = "all",
  domain = "all",
  category = "all",
  unit = "all",
  short_unit = "all",
  currency = c("EUR", "INT$")
)

Arguments

Value

A dataframe or value with the Medical Reference price(s) of the Dutch Costing Manual for the specified years

Examples

# Example usage of the nl_med_prices function
# Calculate for year 2024 with the category Nursing
nl_ref_prices(year = "2024", category = "Nursing")

# Calculate for year 2022 and 2023 the category Nursing
nl_ref_prices(year = c(2022,2023), category = "Nursing")

# Calculate for year 2022 with the category Nursing in INT$
nl_ref_prices(year = "2022", category = "Nursing" , currency = "INT$")

A function to write pretty prices in bookdown reports

Description

This function writes pretty prices in bookdown reports. The function uses the formatC() function to format the number and adds the currency to the end of the number.

Usage

pretty_price(x, digits = 2, currency = "EUR", ...)

Arguments

Value

A pretty price with the currency

Examples

# Example usage of the pretty_price function
pretty_price(1000, currency = "EUR")