Package 'EconEvalR'

Description

Convert an EconBCR object to a data frame

Usage

## S3 method for class 'EconBCR'
as.data.frame(x, ...)

Arguments

Value

A data frame with one row containing the benefit, cost, benefit-cost ratio, and decision.

Description

Converts an object of class "EconBIA" into a data frame suitable for exporting or further analysis.

Usage

## S3 method for class 'EconBIA'
as.data.frame(x, ...)

Arguments

Value

A data frame containing the annual budget impact results.

Examples

## Not run: 
df <- as.data.frame(bia)
head(df)

## End(Not run)

Description

Convert a Cost-Effectiveness Acceptability Curve Object to a Data Frame

Usage

## S3 method for class 'EconCEAC'
as.data.frame(x, ...)

Arguments

Value

A data frame containing the willingness-to-pay thresholds, probabilities of cost-effectiveness, and mean incremental net monetary benefit.

Description

Converts an object of class "EconCEAF" into a data frame suitable for exporting or further analysis.

Usage

## S3 method for class 'EconCEAF'
as.data.frame(x, ...)

Arguments

Value

A data frame containing:

WTP

Willingness-to-pay threshold.

OptimalStrategy

Optimal strategy at each WTP.

Probability

Probability that the optimal strategy is cost-effective.

ExpectedNMB

Expected Net Monetary Benefit of the optimal strategy.

Examples

## Not run: 
df <- as.data.frame(ceaf)
head(df)

## End(Not run)

Description

Converts an object of class "EconEVPI" into a data frame suitable for exporting or further analysis.

Usage

## S3 method for class 'EconEVPI'
as.data.frame(x, ...)

Arguments

Value

A data frame containing:

WTP

Willingness-to-pay threshold.

EVPI

Expected Value of Perfect Information.

ExpectedPerfectInformation

Expected value under perfect information, $E[\max(NMB)]$.

ExpectedCurrentInformation

Expected value under current information, $\max(E[NMB])$.

Examples

## Not run: 
df <- as.data.frame(evpi)
head(df)

## End(Not run)

Description

Convert an Expected Value of Partial Perfect Information Object to a Data Frame

Usage

## S3 method for class 'EconEVPPI'
as.data.frame(x, ...)

Arguments

Value

A data frame.

Description

Convert a Probabilistic Sensitivity Analysis Object to a Data Frame

Usage

## S3 method for class 'EconPSA'
as.data.frame(x, ...)

Arguments

Value

A data frame containing every Monte Carlo simulation.

Description

Convert a Tornado Diagram Object to a Data Frame

Usage

## S3 method for class 'EconTornado'
as.data.frame(x, ...)

Arguments

Value

A data frame.

Description

Converts an object of class "EconTree" into a data frame suitable for exporting or further analysis.

Usage

## S3 method for class 'EconTree'
as.data.frame(x, ...)

Arguments

Value

A data frame containing the terminal node results.

Examples

## Not run: 
df <- as.data.frame(tree)
head(df)

## End(Not run)

Description

Computes the Benefit-Cost Ratio (BCR) for an investment, intervention, or project.

Usage

benefit_cost_ratio(benefit, cost)

Arguments

Details

The Benefit-Cost Ratio is calculated as

$BCR = \frac{Benefit}{Cost}$

Value

An object of class "EconBCR".

Examples

benefit_cost_ratio(
  benefit = 150000,
  cost = 100000
)

Description

Performs a Budget Impact Analysis (BIA) by comparing the projected healthcare expenditure under current practice with a new intervention over multiple years.

Usage

budget_impact_analysis(
  population,
  uptake,
  current_cost,
  new_cost,
  years = length(uptake),
  discount = 0
)

Arguments

Details

The function calculates, for each year:

• Eligible population

• Number treated

• Current budget

• New budget

• Incremental budget impact

• Discounted budget impact

• Cumulative discounted impact

Value

An object of class "EconBIA".

Examples

bia <- budget_impact_analysis(
    population = 10000,
    uptake = c(0.10,0.20,0.35,0.45,0.60),
    current_cost = 1200,
    new_cost = 1500,
    years = 5,
    discount = 0.03
)

print(bia)
summary(bia)
plot(bia)
as.data.frame(bia)

Description

Performs a cost-effectiveness analysis.

Usage

cost_effectiveness(strategy, cost, effectiveness)

Arguments

Value

Object of class "EconCEA".

Description

Computes the probability that an intervention is cost-effective across a range of willingness-to-pay (WTP) thresholds.

Usage

cost_effectiveness_acceptability_curve(
  incremental_cost,
  incremental_effect,
  wtp = seq(0, 1e+05, by = 1000)
)

Arguments

Details

The Incremental Net Monetary Benefit (INMB) is computed as

$INMB = \lambda \times \Delta E - \Delta C$

where

• $\lambda$ = willingness-to-pay threshold

• $\Delta E$ = incremental effect

• $\Delta C$ = incremental cost

The CEAC is the proportion of simulations with INMB > 0.

Value

An object of class "EconCEAC".

Examples

set.seed(1)

cost <- rnorm(1000,500,100)

effect <- rnorm(1000,0.35,0.08)

ceac <- cost_effectiveness_acceptability_curve(
    incremental_cost=cost,
    incremental_effect=effect,
    wtp=seq(0,5000,100)
)

Description

Computes the Cost-Effectiveness Acceptability Frontier (CEAF) for multiple competing strategies.

Usage

cost_effectiveness_acceptability_frontier(
  cost,
  effect,
  wtp = seq(0, 1e+05, by = 1000)
)

Arguments

Details

Net Monetary Benefit (NMB) is calculated as

$NMB = \lambda \times Effect - Cost$

where

• $\lambda$ is the willingness-to-pay threshold.

For each WTP threshold:

• Expected NMB is calculated for every strategy.

• The optimal strategy is identified.

• The probability that this strategy is optimal is estimated.

Value

An object of class "EconCEAF".

Examples

set.seed(123)

cost <- cbind(
  rnorm(1000,1000,100),
  rnorm(1000,1200,120),
  rnorm(1000,1400,150)
)

effect <- cbind(
  rnorm(1000,0.70,0.05),
  rnorm(1000,0.80,0.05),
  rnorm(1000,0.90,0.05)
)

ceaf <- cost_effectiveness_acceptability_frontier(
  cost = cost,
  effect = effect
)

Description

Performs a simple decision tree analysis by calculating expected costs and expected effectiveness for terminal nodes.

Usage

decision_tree_analysis(strategy, probability, cost, effectiveness)

Arguments

Details

Expected values are calculated as:

$Expected\ Cost = Probability \times Cost$

$Expected\ Effectiveness = Probability \times Effectiveness$

Value

An object of class "EconTree".

Examples

strategy <- c(
  "Treatment A",
  "Treatment B",
  "Treatment C"
)

probability <- c(
  0.30,
  0.45,
  0.25
)

cost <- c(
  1200,
  1800,
  2400
)

effectiveness <- c(
  0.70,
  0.82,
  0.91
)

tree <- decision_tree_analysis(
    strategy,
    probability,
    cost,
    effectiveness
)

print(tree)
summary(tree)
plot(tree)
as.data.frame(tree)

Description

Discounts one or more future cash flows to their present values.

Usage

discount_cashflow(cashflow, rate, period)

Arguments

Details

The discounted cash flow is calculated as

$PV = \frac{CF_t}{(1+r)^t}$

where:

• CF = Cash flow

• r = Discount rate

• t = Time period

Value

An object of class "EconDCF".

Examples

discount_cashflow(
  cashflow = c(1000, 1200, 1500),
  rate = 0.10,
  period = 1:3
)

Description

Calculates the discounted payback period of an investment.

Usage

discounted_payback_period(cashflow, rate)

Arguments

Value

An object of class "EconDiscountedPayback".

Examples

dpb <- discounted_payback_period(
  cashflow = c(-50000,12000,15000,18000,20000),
  rate = 0.10
)

print(dpb)

Description

Identifies strongly dominated strategies.

Usage

dominance_analysis(strategy, cost, effectiveness)

Arguments

Details

A strategy is strongly dominated if another strategy has lower cost and greater or equal effectiveness.

Value

An object of class "EconDominance".

Examples

dominance_analysis(
  strategy = c("A","B","C"),
  cost = c(1000,1200,900),
  effectiveness = c(0.70,0.75,0.80)
)

Description

Computes the Expected Value of Partial Perfect Information (EVPPI) using regression-based approximation.

Usage

expected_value_partial_perfect_information(
  cost,
  effect,
  parameter,
  wtp = seq(0, 1e+05, by = 1000),
  method = "gam"
)

Arguments

Details

The EVPPI is estimated by regression of the Net Monetary Benefit (NMB) on the parameter of interest.

This implementation uses Generalized Additive Models (mgcv::gam) to approximate the conditional expectation of NMB.

Value

An object of class "EconEVPPI".

Examples

## Not run: 

library(mgcv)

set.seed(123)

cost <- cbind(
  rnorm(1000,1000,100),
  rnorm(1000,1200,100)
)

effect <- cbind(
  rnorm(1000,0.70,0.05),
  rnorm(1000,0.82,0.05)
)

theta <- runif(1000)

evppi <- expected_value_partial_perfect_information(
    cost = cost,
    effect = effect,
    parameter = theta
)


## End(Not run)

Description

Computes the Expected Value of Perfect Information (EVPI) across a range of willingness-to-pay (WTP) thresholds using probabilistic sensitivity analysis results.

Usage

expected_value_perfect_information(
  cost,
  effect,
  wtp = seq(0, 1e+05, by = 1000)
)

Arguments

Details

Net Monetary Benefit (NMB) is computed as

$NMB = \lambda \times Effect - Cost$

where

• $\lambda$ is the willingness-to-pay threshold.

EVPI is calculated as

$EVPI = E[\max(NMB)] - \max(E[NMB])$

where

• $E[\max(NMB)]$ is the expected value under perfect information.

• $\max(E[NMB])$ is the best expected value under current information.

Value

An object of class "EconEVPI".

Examples

set.seed(123)

cost <- cbind(
  rnorm(1000,1000,100),
  rnorm(1000,1200,120),
  rnorm(1000,1500,140)
)

effect <- cbind(
  rnorm(1000,0.70,0.05),
  rnorm(1000,0.82,0.05),
  rnorm(1000,0.92,0.05)
)

evpi <- expected_value_perfect_information(
    cost = cost,
    effect = effect
)

Description

Calculates the Incremental Cost-Effectiveness Ratio (ICER) between two competing interventions.

Usage

icer(cost, effectiveness)

Arguments

Details

The ICER is calculated as

$ICER=\frac{\Delta Cost}{\Delta Effectiveness}$

where

• ΔCost = Difference in cost

• ΔEffectiveness = Difference in effectiveness

Value

An object of class "EconICER".

Examples

icer(
  cost = c(1000,1300,1700),
  effectiveness = c(0.60,0.75,0.90)
)

Description

Calculates the Internal Rate of Return (IRR).

Usage

internal_rate_return(cashflow, interval = c(-0.99, 10), tol = 1e-08)

Arguments

Value

Object of class "EconIRR".

Description

Calculates the Modified Internal Rate of Return (MIRR).

Usage

modified_internal_rate_return(cashflow, finance_rate, reinvest_rate)

Arguments

Value

Object of class "EconMIRR".

Examples

mirr <- modified_internal_rate_return(
  cashflow = c(-50000,12000,15000,18000,20000),
  finance_rate = 0.08,
  reinvest_rate = 0.10
)

print(mirr)

Description

Performs deterministic multi-way sensitivity analysis.

Usage

multi_way_sensitivity_analysis(parameters, model)

Arguments

Value

An object of class "EconMWSA".

Examples

pars <- data.frame(
  Cost = c(1000,1200,1400),
  Effectiveness = c(0.70,0.75,0.82),
  Probability = c(0.90,0.85,0.80)
)

model <- function(x){
  x$Effectiveness * 5000 -
    x$Cost * x$Probability
}

mw <- multi_way_sensitivity_analysis(
  parameters = pars,
  model = model
)

Description

Calculates the Net Health Benefit (NHB).

Usage

net_health_benefit(strategy, cost, effectiveness, willingness_to_pay)

Arguments

Details

Net Health Benefit is calculated as

$NHB = E - \frac{C}{\lambda}$

where

• E = Effectiveness

• C = Cost

• λ = Willingness-to-pay threshold

Value

An object of class "EconNHB".

Examples

nhb <- net_health_benefit(
  strategy = c("Current","Treatment A","Treatment B"),
  cost = c(1000,1300,1700),
  effectiveness = c(0.60,0.75,0.90),
  willingness_to_pay = 5000
)

print(nhb)

Description

Calculates the Net Monetary Benefit (NMB).

Usage

net_monetary_benefit(strategy, cost, effectiveness, willingness_to_pay)

Arguments

Details

Net Monetary Benefit is calculated as

$NMB = \lambda \times E - C$

where

• $\lambda$ = willingness-to-pay threshold

• E = effectiveness

• C = cost

Value

An object of class "EconNMB".

Examples

nmb <- net_monetary_benefit(
  strategy = c("Current", "Treatment A", "Treatment B"),
  cost = c(1000, 1300, 1700),
  effectiveness = c(0.60, 0.75, 0.90),
  willingness_to_pay = 5000
)

print(nmb)

Description

Calculates the Net Present Value (NPV) of a project.

Usage

net_present_value(cashflow, rate)

Arguments

Value

An object of class "EconNPV".

Description

Performs deterministic one-way sensitivity analysis by varying a single parameter.

Usage

one_way_sensitivity_analysis(base_case, low, high, model)

Arguments

Value

Object of class "EconOWSA".

Description

Performs a complete partial budget analysis.

Usage

partial_budget(added_returns, reduced_costs, added_costs, reduced_returns)

Arguments

Value

Object of class "EconPB".

Examples

partial_budget(
  added_returns = c(5000,2500),
  reduced_costs = c(1500,500),
  added_costs = c(2000,1000),
  reduced_returns = c(500)
)

Description

Calculates the payback period of an investment.

Usage

payback_period(cashflow)

Arguments

Details

The payback period is the time required for cumulative cash inflows to recover the initial investment. Linear interpolation is used when recovery occurs during a period.

Value

An object of class "EconPayback".

Examples

pb <- payback_period(
  cashflow = c(-50000,12000,15000,18000,20000)
)

print(pb)

Description

Produces a bar chart comparing total benefits and total costs.

Usage

## S3 method for class 'EconBCR'
plot(x, ...)

Arguments

Value

Invisibly returns the midpoints of the bars.

Examples

x <- benefit_cost_ratio(
  benefit = 150000,
  cost = 100000
)

plot(x)

Description

Produces a visualization of the annual incremental budget impact and cumulative budget impact over time.

Usage

## S3 method for class 'EconBIA'
plot(x, type = c("annual", "cumulative"), ...)

Arguments

Value

Produces a budget impact plot.

Examples

## Not run: 
plot(bia)
plot(bia, type = "cumulative")

## End(Not run)

Description

Plot Cost-Effectiveness Plane

Usage

## S3 method for class 'EconCEA'
plot(x, ...)

Arguments

Value

A Cost-Effectiveness Plane.

Description

Produces a Cost-Effectiveness Acceptability Curve (CEAC) showing the probability that an intervention is cost-effective over a range of willingness-to-pay (WTP) thresholds.

Usage

## S3 method for class 'EconCEAC'
plot(x, ...)

Arguments

Value

Produces a CEAC plot.

Description

Produces a Cost-Effectiveness Acceptability Frontier (CEAF) showing the probability that the optimal strategy is cost-effective over a range of willingness-to-pay (WTP) thresholds.

Usage

## S3 method for class 'EconCEAF'
plot(x, ...)

Arguments

Value

Produces a CEAF plot.

Examples

## Not run: 
plot(ceaf)

## End(Not run)

Description

Produces an Expected Value of Perfect Information (EVPI) curve across willingness-to-pay (WTP) thresholds.

Usage

## S3 method for class 'EconEVPI'
plot(x, ...)

Arguments

Value

Produces an EVPI plot.

Examples

## Not run: 
plot(evpi)

## End(Not run)

Description

Produces an Expected Value of Partial Perfect Information (EVPPI) curve across willingness-to-pay (WTP) thresholds.

Usage

## S3 method for class 'EconEVPPI'
plot(x, ...)

Arguments

Value

Produces an EVPPI plot.

Examples

## Not run: 
plot(evppi)

## End(Not run)

Description

Plot Multi-Way Sensitivity Analysis

Usage

## S3 method for class 'EconMWSA'
plot(x, ...)

Arguments

Value

Produces a line plot of model outcomes.

Description

Displays the distribution of simulated outcomes.

Usage

## S3 method for class 'EconPSA'
plot(x, ...)

Arguments

Value

Produces a histogram with density curve.

Description

Plot Threshold Analysis

Usage

## S3 method for class 'EconThreshold'
plot(x, ...)

Arguments

Value

Produces a threshold analysis plot.

Description

Plot Tornado Diagram

Usage

## S3 method for class 'EconTornado'
plot(x, ...)

Arguments

Description

Produces a bar plot of expected costs or expected effectiveness for each terminal strategy.

Usage

## S3 method for class 'EconTree'
plot(x, type = c("cost", "effectiveness"), ...)

Arguments

Value

Produces a bar plot.

Examples

## Not run: 
plot(tree)
plot(tree, type = "effectiveness")

## End(Not run)

Description

Calculates the present value of one or more future cash flows.

Usage

present_value(future_value, rate, period)

Arguments

Details

Present value is calculated as

$PV=\frac{FV}{(1+r)^t}$

where

• FV = Future Value

• r = Discount rate

• t = Time period

Value

An object of class "EconPV".

Examples

pv <- present_value(
    future_value = c(1000,1500,2000),
    rate = 0.10,
    period = 1:3
)

print(pv)

Description

Print an EconBCR object

Usage

## S3 method for class 'EconBCR'
print(x, ...)

Arguments

Value

Invisibly returns the object.

Description

Print a Budget Impact Analysis Object

Usage

## S3 method for class 'EconBIA'
print(x, ...)

Arguments

Value

The input object, invisibly.

Description

Print a Cost-Effectiveness Acceptability Curve Object

Usage

## S3 method for class 'EconCEAC'
print(x, ...)

Arguments

Value

The input object, invisibly.

Description

Print a Cost-Effectiveness Acceptability Frontier Object

Usage

## S3 method for class 'EconCEAF'
print(x, ...)

Arguments

Value

The input object, invisibly.

Description

Print an Expected Value of Perfect Information Object

Usage

## S3 method for class 'EconEVPI'
print(x, ...)

Arguments

Value

The input object, invisibly.

Description

Print an Expected Value of Partial Perfect Information Object

Usage

## S3 method for class 'EconEVPPI'
print(x, ...)

Arguments

Value

The input object, invisibly.

Description

Print a Probabilistic Sensitivity Analysis Object

Usage

## S3 method for class 'EconPSA'
print(x, ...)

Arguments

Value

The input object, invisibly.

Description

Print a Tornado Diagram Object

Usage

## S3 method for class 'EconTornado'
print(x, ...)

Arguments

Value

The input object, invisibly.

Description

Print a Decision Tree Analysis Object

Usage

## S3 method for class 'EconTree'
print(x, ...)

Arguments

Value

The input object, invisibly.

Description

Performs Monte Carlo simulation for economic evaluation.

Usage

probabilistic_sensitivity_analysis(n = 1000, sampler, model, seed = NULL)

Arguments

Details

This function performs probabilistic sensitivity analysis (PSA) using Monte Carlo simulation. During each iteration, random parameters are generated by sampler() and passed to model(). The model must return one finite numeric outcome.

Value

An object of class "EconPSA".

Examples

sampler <- function() {

  list(
    cost = rnorm(1, 1000, 100),
    effectiveness = rbeta(1, 20, 5)
  )

}

model <- function(par) {

  par$effectiveness * 5000 -
    par$cost

}

psa <- probabilistic_sensitivity_analysis(
  n = 1000,
  sampler = sampler,
  model = model,
  seed = 123
)

Description

Calculates the Profitability Index (PI).

Usage

profitability_index(cashflow, rate)

Arguments

Value

Object of class "EconPI".

Description

Summarize an EconBCR object

Usage

## S3 method for class 'EconBCR'
summary(object, ...)

Arguments

Value

A data frame summarizing the benefit-cost analysis.

Description

Returns a summary of the annual and cumulative budget impact over the specified time horizon.

Usage

## S3 method for class 'EconBIA'
summary(object, ...)

Arguments

Value

A data frame containing annual budget impact results.

Examples

## Not run: 
summary(bia)

## End(Not run)

Description

Summarize a Cost-Effectiveness Analysis

Usage

## S3 method for class 'EconCEA'
summary(object, ...)

Arguments

Value

A data frame summarizing the cost-effectiveness analysis.

Description

Summarize a Cost-Effectiveness Acceptability Curve Object

Usage

## S3 method for class 'EconCEAC'
summary(object, ...)

Arguments

Value

A data frame summarizing the Cost-Effectiveness Acceptability Curve.

Description

Returns a summary of the Cost-Effectiveness Acceptability Frontier (CEAF) results across all willingness-to-pay (WTP) thresholds.

Usage

## S3 method for class 'EconCEAF'
summary(object, ...)

Arguments

Value

A data frame containing:

WTP

Willingness-to-pay threshold.

OptimalStrategy

Optimal strategy at each WTP.

Probability

Probability that the optimal strategy is truly cost-effective.

ExpectedNMB

Expected Net Monetary Benefit.

Examples

## Not run: 
summary(ceaf)

## End(Not run)

Description

Returns a summary of the Expected Value of Perfect Information (EVPI) across willingness-to-pay (WTP) thresholds.

Usage

## S3 method for class 'EconEVPI'
summary(object, ...)

Arguments

Value

A data frame containing:

WTP

Willingness-to-pay threshold.

EVPI

Expected Value of Perfect Information.

ExpectedPerfectInformation

Expected value under perfect information, $E[\max(NMB)]$.

ExpectedCurrentInformation

Expected value under current information, $\max(E[NMB])$.

Examples

## Not run: 
summary(evpi)

## End(Not run)

Description

Returns a summary of the Expected Value of Partial Perfect Information (EVPPI) across willingness-to-pay (WTP) thresholds.

Usage

## S3 method for class 'EconEVPPI'
summary(object, ...)

Arguments

Value

A data frame containing:

WTP

Willingness-to-pay threshold.

EVPPI

Expected Value of Partial Perfect Information.

Examples

## Not run: 
summary(evppi)

## End(Not run)

Description

Summarize a Probabilistic Sensitivity Analysis Object

Usage

## S3 method for class 'EconPSA'
summary(object, ...)

Arguments

Value

A data frame containing summary statistics.

Description

Summarize a Tornado Diagram Object

Usage

## S3 method for class 'EconTornado'
summary(object, ...)

Arguments

Value

A data frame summarizing the tornado analysis.

Description

Returns a summary of the expected costs and expected effectiveness for each terminal node.

Usage

## S3 method for class 'EconTree'
summary(object, ...)

Arguments

Value

A data frame containing:

Strategy

Terminal strategy.

Probability

Terminal node probability.

Cost

Observed cost.

Effectiveness

Observed effectiveness.

ExpectedCost

Expected cost.

ExpectedEffectiveness

Expected effectiveness.

Examples

## Not run: 
summary(tree)

## End(Not run)

Description

Identifies the threshold value of a parameter at which a model outcome reaches a specified target.

Usage

threshold_analysis(lower, upper, target = 0, model, tol = 1e-08)

Arguments

Value

Object of class "EconThreshold".

Description

Creates data for a tornado diagram.

Usage

tornado_diagram(parameter, low, high, base_case)

Arguments

Value

Object of class "EconTornado".

Description

Performs deterministic two-way sensitivity analysis by varying two model parameters simultaneously.

Usage

two_way_sensitivity_analysis(parameter1, parameter2, model)

Arguments

Value

An object of class "EconTWSA".