Calculate the discounted (net present value) of costs or effects
Description
![[Experimental]](../help/figures/lifecycle-experimental.svg)
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 )apply_discounting( values, discount_rate = c("costs", "effects"), times, aggregate = FALSE, digits = NULL )
Arguments
values |
A numeric (vector of) costs or effects over time (one value per period). |
discount_rate |
Specifies the discount rate to be used. Acceptable values are: "costs" (0.03), "effects" (0.015), or a custom numeric value such as 0.04. |
times |
A numeric (vector of) time points indicating the time used for the discounting. The length must match the length of the values vector. Since the default discounting is annual, the time points should be in years. The length of this vector should be the same as the length of the |
aggregate |
A logical: indicating whether to sum the discounted values. Default is FALSE. |
digits |
A numeric value to indicate the number of digits to round the value. Default is 3 digits |
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)# 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_sickerdata_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 }# 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:
#' The annual charge k is:
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") )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
v_replace_val |
V: vervangingswaarde; replacement value (numeric scalar, > 0) |
r_salvage_val |
R: restwaarde; salvage (residual) value at end of period (numeric scalar, >= 0) |
n_amortisation_period |
n: afschrijvingstermijn; amortisation period in years (numeric scalar, > 0) |
i_interest_rt |
i: rentepercntage; annual interest rate as a decimal (numeric scalar, >= 0) |
output |
One of |
Value
-
If
output = "dataframe": a data.frame with two columns:Annuity factorandYearly 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")# 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_combineddf_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_fpdf_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_pppdf_ppp
Format
df_ppp
A data frame with 64 rows and 2 columns:
- Year
-
Year of PPP
- PPP
-
Purchase Power Parity
Source
Complete data of reference values from the costing manual
Description
Complete data of reference values from the costing manual
Usage
df_ref_pricesdf_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
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 )friction_period( year = NULL, units = "weeks", avg = "5yr", output = c("tibble", "value"), data = tatooheene::df_fp )
Arguments
year |
Integer vector of years to return. If |
units |
One or more of |
avg |
One or more of |
output |
Either |
data |
Data source (mainly for testing); default is |
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")# 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")nl_ppp(year = "all")
Arguments
year |
The year of which the PPP factor should be downloaded, multiple years are possible, default is the whole dataset. |
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)# 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") )nl_price_index( start_year = 2013, end_year = 2023, output = c("table", "factor") )
Arguments
start_year |
start year for CPI output table or factor |
end_year |
End year for CPI output table or factor |
output |
Which output we would like to see. "factor": is the factor from start to end year, "table" is the table of all CPIs from start to end year |
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")# 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$).
Usage
nl_ref_prices( year = "all", domain = "all", category = "all", unit = "all", short_unit = "all", currency = c("EUR", "INT$") )nl_ref_prices( year = "all", domain = "all", category = "all", unit = "all", short_unit = "all", currency = c("EUR", "INT$") )
Arguments
year |
The year of which the reference price should be downloaded, multiple years are possible, default is the whole dataset |
domain |
The domain of prices that should be included (one or more categories), default is including all categories |
category |
The category of prices that should be included (one or more categories), default is including all categories |
unit |
The reference price that should be included (one or multiple reference prices), default is including the whole dataframe |
short_unit |
The short variable name that should be included (one or more short variables), default is including all |
currency |
The currency of the output of the prices. A decision can be made between EUR and INT$, the default is EUR. |
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$")# Example usage of the nl_med_prices function # Calculate for year 2024 with the category Nursing nl_ref_prices(year = "2024", category = "Nursing") # Calculate for year 2022 and 2023 the category Nursing nl_ref_prices(year = c(2022,2023), category = "Nursing") # Calculate for year 2022 with the category Nursing in INT$ 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", ...)pretty_price(x, digits = 2, currency = "EUR", ...)
Arguments
x |
A number to be printed |
digits |
Number of digits |
currency |
The name of the currency |
... |
Extra arguments for |
Value
A pretty price with the currency
Examples
# Example usage of the pretty_price function pretty_price(1000, currency = "EUR")# Example usage of the pretty_price function pretty_price(1000, currency = "EUR")