| Type: | Package |
| Title: | Bayesian Cost Effectiveness Analysis |
| Version: | 2.4.81 |
| Imports: | cli (≥ 3.3.0), dplyr, ggplot2 (≥ 3.5.0), graphics, gridExtra, MASS, Matrix, MCMCvis, plotly, purrr, Rdpack, reshape2, rlang, rstan, scales, voi (≥ 1.0.1) |
| Depends: | R (≥ 4.1.0) |
| Suggests: | coda, grid, knitr, markdown, mgcv, plotrix, RColorBrewer, rjags, rmarkdown, splancs, testthat (≥ 2.1.0), vdiffr, withr |
| RdMacros: | Rdpack |
| VignetteBuilder: | knitr |
| Description: | Produces an economic evaluation of a sample of suitable variables of cost and effectiveness / utility for two or more interventions, e.g. from a Bayesian model in the form of MCMC simulations. This package computes the most cost-effective alternative and produces graphical summaries and probabilistic sensitivity analysis, see Baio et al (2017) <doi:10.1007/978-3-319-55718-2>. |
| License: | GPL-3 |
| URL: | https://gianluca.statistica.it/software/bcea/, https://gianluca.statistica.it/, https://n8thangreen.github.io/BCEA/, https://github.com/giabaio/BCEA/ |
| NeedsCompilation: | no |
| RoxygenNote: | 7.3.2.9000 |
| Encoding: | UTF-8 |
| BugReports: | https://github.com/giabaio/BCEA/issues/ |
| LazyData: | false |
| Packaged: | 2025-07-14 15:58:17 UTC; gianluca |
| Author: | Gianluca Baio 
[aut, cre, cph],
Andrea Berardi
[aut],
Anna Heath [aut],
Nathan Green
[aut] |
| Maintainer: | Gianluca Baio <g.baio@ucl.ac.uk> |
| Repository: | CRAN |
| Date/Publication: | 2025-07-14 16:30:14 UTC |
BCEA: Bayesian Cost Effectiveness Analysis
Description
Produces an economic evaluation of a sample of suitable variables of cost and effectiveness / utility for two or more interventions, e.g. from a Bayesian model in the form of MCMC simulations. This package computes the most cost-effective alternative and produces graphical summaries and probabilistic sensitivity analysis, see Baio et al (2017) doi:10.1007/978-3-319-55718-2.
Author(s)
Maintainer: Gianluca Baio g.baio@ucl.ac.uk (ORCID) [copyright holder]
Authors:
Andrea Berardi a.berardi@ucl.ac.uk (ORCID)
Anna Heath anna.heath@sickkids.ca (ORCID)
Nathan Green n.green@ucl.ac.uk (ORCID)
See Also
Useful links:
Report bugs at https://github.com/giabaio/BCEA/issues/
Deprecated functions in package BCEA.
Description
The functions listed below are deprecated and will be defunct in
the near future. When possible, alternative functions with similar
functionality are also mentioned. Help pages for deprecated functions are
available at help("<function>-deprecated").
Usage
plot.mixedAn(x, y.limits=NULL, pos=c(0,1), graph=c("base","ggplot2"),...)
Arguments
x |
An object of class |
y.limits |
Range of the y-axis for the graph. The default value is
|
pos |
Parameter to set the position of the legend. Can be given in form
of a string |
graph |
A string used to select the graphical engine to use for
plotting. Should (partial-)match the two options |
... |
Arguments to be passed to methods, such as graphical parameters
(see |
Value
evi |
A ggplot object containing the plot. Returned only if
|
The function produces a graph showing the difference between the ”optimal” version of the EVPI (when only the most cost-effective intervention is included in the market) and the mixed strategy one (when more than one intervention is considered in the market).
plot.mixedAn
For plot.mixedAn, use evi.plot().
Summary plot of the health economic analysis when the mixed analysis is considered
Compares the optimal scenario to the mixed case in terms of the EVPI.
Author(s)
Gianluca Baio, Andrea Berardi
Cost-effectiveness Analysis Including a Parameter of Risk Aversion
Description
Extends the standard cost-effectiveness analysis to modify the utility function so that risk aversion of the decision maker is explicitly accounted for.
Default vector of risk aversion parameters: 1e-11, 2.5e-6, 5e-6
Usage
CEriskav(he) <- value
## S3 replacement method for class 'bcea'
CEriskav(he) <- value
## Default S3 replacement method:
CEriskav(he) <- value
Arguments
he |
A |
value |
A vector of values for the risk aversion parameter. If |
Value
An object of the class CEriskav containing the following elements:
-
Ur: An array containing the simulated values for all the "known-distribution" utilities for all interventions, all the values of the willingness to pay parameter, and for all the possible values of
r. -
Urstar: An array containing the simulated values for the maximum "known-distribution" expected utility for all the values of the willingness to pay parameter and for all the possible values of
r. -
IBr: An array containing the simulated values for the distribution of the Incremental Benefit for all the values of the willingness to pay parameter and for all the possible values of
r. -
eibr: An array containing the Expected Incremental Benefit for each value of the willingness to pay parameter and for all the possible values of
r. -
vir: An array containing all the simulations for the Value of Information for each value of the willingness to pay parameter and for all the possible values of
r. -
evir: An array containing the Expected Value of Information for each value of the willingness to pay parameter and for all the possible values of
r. -
R: The number of possible values for the parameter of risk aversion
r. -
r: A vector containing all the possible values for the parameter of risk aversion
r.
Author(s)
Gianluca Baio
References
Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis in health economics.” Stat. Methods Med. Res., 1–20. ISSN 1477-0334, doi:10.1177/0962280211419832, https://pubmed.ncbi.nlm.nih.gov/21930515/.
Baio G (2013). Bayesian Methods in Health Economics. CRC.
See Also
Examples
# See Baio G., Dawid A.P. (2011) for a detailed description of the
# Bayesian model and economic problem
# Load the processed results of the MCMC simulation model
data(Vaccine)
# Runs the health economic evaluation using BCEA
m <- bcea(e=eff,c=cost, # defines the variables of
# effectiveness and cost
ref=2, # selects the 2nd row of (e, c)
# as containing the reference intervention
interventions=treats, # defines the labels to be associated
# with each intervention
Kmax=50000 # maximum value possible for the willingness
# to pay threshold; implies that k is chosen
# in a grid from the interval (0, Kmax)
)
# Define the vector of values for the risk aversion parameter, r, eg:
r <- c(1e-10, 0.005, 0.020, 0.035)
# Run the cost-effectiveness analysis accounting for risk aversion
# uses the results of the economic evaluation
# if more than 2 interventions, selects the
# pairwise comparison
CEriskav(m) <- r
Cost-effectiveness Plot Including a Parameter of Risk Aversion
Description
Choice of base R, ggplot2 or plotly.
Usage
CEriskav_plot_base(he, pos_legend)
CEriskav_plot_ggplot(he, pos_legend)
CEriskav_plot_plotly(he, pos_legend)
Arguments
he |
A |
pos_legend |
Legend position |
GrassmannOptim
Description
This function is taken from the GrassmannOptim package by Kofi Placid Adragni and Seongho Wu https://cran.r-project.org/web/packages/GrassmannOptim/index.html
Usage
GrassmannOptim(
objfun,
W,
sim_anneal = FALSE,
temp_init = 20,
cooling_rate = 2,
max_iter_sa = 100,
eps_conv = 1e-05,
max_iter = 100,
eps_grad = 1e-05,
eps_f = .Machine$double.eps,
verbose = FALSE
)
Arguments
objfun |
objfun |
W |
W |
sim_anneal |
sim_anneal |
temp_init |
temp_init |
cooling_rate |
cooling_rate |
max_iter_sa |
max_iter_sa |
eps_conv |
eps_conv |
max_iter |
max_iter |
eps_grad |
eps_grad |
eps_f |
eps_f |
verbose |
verbose |
Value
List
Smoking Cessation Cost-Effectiveness Data
Description
This data set contains the results of a Bayesian analysis modeling the clinical outputs and costs for an economic evaluation of four different smoking cessation interventions.
Format
A list containing the variables for the cost-effectiveness analysis:
- cost
A matrix of 500 simulations from the posterior distribution of the overall costs for the four strategies.
- data
A dataset with characteristics of smokers in the UK population.
- eff
A matrix of 500 simulations from the posterior distribution of the clinical benefits for the four strategies.
- life.years
A matrix of 500 simulations from the posterior distribution of the life years gained with each strategy.
- pi_post
A matrix of 500 simulations from the posterior distribution of the probability of smoking cessation with each strategy.
- smoking
A data frame with inputs for the network meta-analysis, containing:
nobs(record ID),s(study ID),i(intervention ID),r_i(number of patients who quit),n_i(total patients in arm), andb_i(reference intervention for the study).- smoking_output
A matrix of results from the network meta-analysis model run on the
smokingobject.- treats
A character vector of labels for the four strategies.
Source
Effectiveness data adapted from Hasselblad V. (1998). "Meta-analysis of Multitreatment Studies". Medical Decision Making, 18:37-43.
Cost and population data adapted from various sources:
Taylor, D.H. Jr, et al. (2002). "Benefits of smoking cessation on longevity". American Journal of Public Health, 92(6).
Action on Smoking and Health (ASH) (2013). "ASH fact sheet on smoking statistics". https://ash.org/wp-content/uploads/2014/05/ASH-Annual-Report-2014.pdf.
Flack, S., et al. (2007). "Cost-effectiveness of interventions for smoking cessation". York Health Economics Consortium.
McGhan, W.F.D., and Smith, M. (1996). "Pharmacoeconomic analysis of smoking-cessation interventions". American Journal of Health-System Pharmacy, 53:45-52.
References
Baio G. (2012). Bayesian Methods in Health Economics. CRC/Chapman & Hall, London.
Influenza Vaccination Cost-Effectiveness Data
Description
This data set contains the results of a Bayesian analysis modeling the clinical outputs and costs associated with an influenza vaccination program.
Format
A list containing variables for the influenza vaccination model:
- cost
A matrix of simulations of the overall costs for the two treatments.
- c.pts
Coordinates for plotting cost distributions.
- cost.GP
A matrix of simulations of the costs for GP visits.
- cost.hosp
A matrix of simulations of the costs for hospitalisations.
- cost.otc
A matrix of simulations of the costs for over-the-counter medications.
- cost.time.off
A matrix of simulations of the costs for time off work.
- cost.time.vac
A matrix of simulations of the costs for time to get vaccinated.
- cost.travel
A matrix of simulations of the costs for travel to get vaccinated.
- cost.trt1
A matrix of simulations of the overall costs for first-line treatment.
- cost.trt2
A matrix of simulations of the overall costs for second-line treatment.
- cost.vac
A matrix of simulations of the costs for vaccination.
- eff
A matrix of simulations of the clinical benefits.
- e.pts
Coordinates for plotting effectiveness distributions.
- N
The number of subjects in the reference population.
- N.outcomes
The number of clinical outcomes analysed.
- N.resources
The number of health-care resources under study.
- QALYs.adv
A vector of QALYs associated with adverse events.
- QALYs.death
A vector of QALYs associated with death.
- QALYs.hosp
A vector of QALYs associated with hospitalisation.
- QALYs.inf
A vector of QALYs associated with influenza infection.
- QALYs.pne
A vector of QALYs associated with pneumonia.
- treats
A character vector of labels for the two treatments.
- vaccine_mat
A matrix of simulations for the parameters in the original model.
Source
Adapted from Turner D, et al. (2006). "The cost-effectiveness of influenza vaccination of healthy adults 50-64 years of age". Vaccine, 24:1035-1043.
References
Baio, G., & Dawid, A. P. (2011). "Probabilistic Sensitivity Analysis in Health Economics". Statistical Methods in Medical Research. doi:10.1177/0962280211419832.
Add Contour Quadrants
Description
Add Contour Quadrants
Usage
add_contour_quadrants(he, params)
Arguments
he |
A |
params |
List |
Value
Plot side effect
Add Contours to Base R Plot
Description
Add Contours to Base R Plot
Usage
add_contours(he, params)
Arguments
he |
A |
params |
List |
Value
plot side effect
Create Bayesian Cost-Effectiveness Analysis Object
Description
Cost-effectiveness analysis based on the results of a simulation model for a variable of clinical benefits (e) and of costs (c). Produces results to be post-processed to give the health economic analysis. The output is stored in an object of the class "bcea".
Usage
bcea(
eff,
cost,
ref = 1,
interventions = NULL,
.comparison = NULL,
Kmax = 50000,
k = NULL,
plot = FALSE,
...
)
## Default S3 method:
bcea(
eff,
cost,
ref = NULL,
interventions = NULL,
.comparison = NULL,
Kmax = 50000,
k = NULL,
plot = FALSE,
...
)
## S3 method for class 'rjags'
bcea(eff, ...)
## S3 method for class 'rstan'
bcea(eff, ...)
## S3 method for class 'bugs'
bcea(eff, ...)
Arguments
eff |
An object containing |
cost |
An object containing |
ref |
Defines which intervention (columns of |
interventions |
Defines the labels to be associated with each
intervention. By default and if |
.comparison |
Selects the comparator, in case of more than two
interventions being analysed. Default as NULL plots all the comparisons
together. Any subset of the possible comparisons can be selected (e.g.,
|
Kmax |
Maximum value of the willingness to pay to be considered.
Default value is |
k |
A(n optional) vector for the values of the willingness
to pay grid. Should be of length > 1 otherwise plots will be empty.
If not specified then BCEA will construct a grid of 501 values
from 0 to |
plot |
A logical value indicating whether the function should produce the summary plot or not. |
... |
Additional arguments |
Value
An object of the class bcea containing the following elements:
-
n_sim: Number of simulations produced by the Bayesian model.
-
n_comparators: Number of interventions being analyzed.
-
n_comparisons: Number of possible pairwise comparisons.
-
delta.e: For each possible comparison, the differential in the effectiveness measure.
-
delta.c: For each possible comparison, the differential in the cost measure.
-
ICER: The value of the Incremental Cost-Effectiveness Ratio.
-
Kmax: The maximum value assumed for the willingness to pay threshold.
-
k: The vector of values for the grid approximation of the willingness to pay.
-
ceac: The value for the Cost-Effectiveness Acceptability Curve, as a function of the willingness to pay.
-
ib: The distribution of the Incremental Benefit, for a given willingness to pay.
-
eib: The value for the Expected Incremental Benefit, as a function of the willingness to pay.
-
kstar: The grid approximation of the break-even point(s).
-
best: A vector containing the numeric label of the intervention that is the most cost-effective for each value of the willingness to pay in the selected grid approximation.
-
U: An array including the value of the expected utility for each simulation from the Bayesian model, for each value of the grid approximation of the willingness to pay, and for each intervention being considered.
-
vi: An array including the value of information for each simulation from the Bayesian model and for each value of the grid approximation of the willingness to pay.
-
Ustar: An array including the maximum "known-distribution" utility for each simulation from the Bayesian model and for each value of the grid approximation of the willingness to pay.
-
ol: An array including the opportunity loss for each simulation from the Bayesian model and for each value of the grid approximation of the willingness to pay.
-
evi: The vector of values for the Expected Value of Information, as a function of the willingness to pay.
-
interventions: A vector of labels for all the interventions considered.
-
ref: The numeric index associated with the intervention used as reference in the analysis.
-
comp: The numeric index(es) associated with the intervention(s) used as comparator(s) in the analysis.
-
step: The step size used to form the grid approximation to the willingness to pay.
-
e: The
effmatrix used to generate the object (see Arguments). -
c: The
costmatrix used to generate the object (see Arguments).
Author(s)
Gianluca Baio, Andrea Berardi, Nathan Green
References
Baio G (2013). Bayesian Methods in Health Economics. CRC.
Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis in health economics.” Stat. Methods Med. Res., 1–20. ISSN 1477-0334, doi:10.1177/0962280211419832, https://pubmed.ncbi.nlm.nih.gov/21930515/.
Examples
# See Baio (2013), Baio (2011) for a detailed description of the
# Bayesian model and economic problem
# Load the processed results of the MCMC simulation model
data(Vaccine)
# Runs the health economic evaluation using BCEA
m <- bcea(
e=eff,
c=cost, # defines the variables of
# effectiveness and cost
ref=2, # selects the 2nd row of (e, c)
# as containing the reference intervention
interventions=treats, # defines the labels to be associated
# with each intervention
Kmax=50000, # maximum value possible for the willingness
# to pay threshold; implies that k is chosen
# in a grid from the interval (0, Kmax)
plot=TRUE # plots the results
)
# Creates a summary table
summary(
m, # uses the results of the economic evaluation
# (a "bcea" object)
wtp=25000 # selects the particular value for k
)
# Plots the cost-effectiveness plane using base graphics
ceplane.plot(
m, # plots the Cost-Effectiveness plane
comparison=1, # if more than 2 interventions, selects the
# pairwise comparison
wtp=25000, # selects the relevant willingness to pay
# (default: 25,000)
graph="base" # selects base graphics (default)
)
# Plots the cost-effectiveness plane using ggplot2
if (requireNamespace("ggplot2")) {
ceplane.plot(
m, # plots the Cost-Effectiveness plane
comparison=1, # if more than 2 interventions, selects the
# pairwise comparison
wtp=25000, # selects the relevant willingness to pay
# (default: 25,000)
graph="ggplot2"# selects ggplot2 as the graphical engine
)
# Some more options
ceplane.plot(
m,
graph="ggplot2",
pos="top",
size=5,
ICER_size=1.5,
label.pos=FALSE,
opt.theme=ggplot2::theme(text=ggplot2::element_text(size=8))
)
}
# Plots the contour and scatterplot of the bivariate
# distribution of (Delta_e,Delta_c)
contour(
m, # uses the results of the economic evaluation
# (a "bcea" object)
comparison=1, # if more than 2 interventions, selects the
# pairwise comparison
nlevels=4, # selects the number of levels to be
# plotted (default=4)
levels=NULL, # specifies the actual levels to be plotted
# (default=NULL, so that R will decide)
scale=0.5, # scales the bandwidths for both x- and
# y-axis (default=0.5)
graph="base" # uses base graphics to produce the plot
)
# Plots the contour and scatterplot of the bivariate
# distribution of (Delta_e,Delta_c)
contour2(
m, # uses the results of the economic evaluation
# (a "bcea" object)
wtp=25000, # selects the willingness-to-pay threshold
)
# Using ggplot2
if (requireNamespace("ggplot2")) {
contour2(
m, # uses the results of the economic evaluation
# (a "bcea" object)
graph="ggplot2",# selects the graphical engine
wtp=25000, # selects the willingness-to-pay threshold
label.pos=FALSE # alternative position for the wtp label
)
}
# Plots the Expected Incremental Benefit for the "bcea" object m
eib.plot(m)
# Plots the distribution of the Incremental Benefit
ib.plot(
m, # uses the results of the economic evaluation
# (a "bcea" object)
comparison=1, # if more than 2 interventions, selects the
# pairwise comparison
wtp=25000, # selects the relevant willingness
# to pay (default: 25,000)
graph="base" # uses base graphics
)
# Produces a plot of the CEAC against a grid of values for the
# willingness to pay threshold
ceac.plot(m)
# Plots the Expected Value of Information for the "bcea" object m
evi.plot(m)
Optimal intervention
Description
Select the best option for each value of willingness to pay.
Usage
best_interv_given_k(eib, ref, comp)
Arguments
eib |
Expected incremental benefit |
ref |
Reference group number |
comp |
Comparison group number(s) |
Value
Group index
Cost-effectiveness summary statistics table
Description
As is commonly shown in a journal paper.
Usage
ce_table(he, wtp = 25000, ...)
Arguments
he |
A |
wtp |
Willingness to pay |
... |
Additional parameters |
Examples
data(Vaccine)
# Runs the health economic evaluation using BCEA
m <- bcea(
e=eff,
c=cost, # defines the variables of
# effectiveness and cost
ref=2, # selects the 2nd row of (e, c)
# as containing the reference intervention
interventions=treats, # defines the labels to be associated
# with each intervention
Kmax=50000, # maximum value possible for the willingness
# to pay threshold; implies that k is chosen
# in a grid from the interval (0, Kmax)
)
ce_table(m)
Cost-Effectiveness Acceptability Curve (CEAC) Plot
Description
Produces a plot of the Cost-Effectiveness Acceptability Curve (CEAC) against the willingness to pay threshold.
Usage
## S3 method for class 'bcea'
ceac.plot(
he,
comparison = NULL,
pos = "bottomright",
graph = c("base", "ggplot2", "plotly"),
...
)
ceac.plot(he, ...)
Arguments
he |
A |
comparison |
Selects the comparator, in case of more than two
interventions being analysed. Default as NULL plots all the comparisons
together. Any subset of the possible comparisons can be selected (e.g.,
|
pos |
Parameter to set the position of the legend (only relevant for
multiple interventions, ie more than 2 interventions being compared).
Can be given in form
of a string |
graph |
A string used to select the graphical engine to use for
plotting. Should (partial-)match the three options |
... |
If
|
Details
The CEAC estimates the probability of cost-effectiveness, with respect to a given willingness to pay threshold. The CEAC is used mainly to evaluate the uncertainty associated with the decision-making process, since it enables the quantification of the preference of the compared interventions, defined in terms of difference in utilities. Formally, the CEAC is defined as:
\textrm{CEAC} = P(\textrm{IB}(\theta) > 0)
If the net benefit function is used as utility function, the definition can be re-written as
\textrm{CEAC} = P(k \cdot \Delta_e - \Delta_c > 0)
effectively depending on the willingness to pay value k.
Value
ceac |
If |
The function produces a plot of the cost-effectiveness acceptability curve against the discrete grid of possible values for the willingness to pay parameter. Values of the CEAC closer to 1 indicate that uncertainty in the cost-effectiveness of the reference intervention is very low. Similarly, values of the CEAC closer to 0 indicate that uncertainty in the cost-effectiveness of the comparator is very low.
Author(s)
Gianluca Baio, Andrea Berardi
References
Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis in health economics.” Stat. Methods Med. Res., 1–20. ISSN 1477-0334, doi:10.1177/0962280211419832, https://pubmed.ncbi.nlm.nih.gov/21930515/.
Baio G (2013). Bayesian Methods in Health Economics. CRC.
See Also
Examples
data("Vaccine")
he <- BCEA::bcea(eff, cost)
ceac.plot(he)
ceac.plot(he, graph = "base")
ceac.plot(he, graph = "ggplot2")
ceac.plot(he, graph = "plotly")
ceac.plot(he, graph = "ggplot2",
title = "my title",
line = list(color = "green"),
theme = ggplot2::theme_dark())
## more interventions
he2 <- BCEA::bcea(cbind(eff, eff - 0.0002), cbind(cost, cost + 5))
mypalette <- RColorBrewer::brewer.pal(3, "Accent")
ceac.plot(he2, graph = "ggplot2",
title = "my title",
theme = ggplot2::theme_dark(),
pos = TRUE,
line = list(color = mypalette))
ceac.plot(he, graph = "base", title = "my title", line = list(color = "green"))
ceac.plot(he2, graph = "base")
ceac.plot(he2, graph = "plotly", pos = "bottom")
CEAC Matrix Plot
Description
CEAC plot using matplot in Base R.
Usage
ceac_matplot(he, pos_legend, graph_params, ceac)
Arguments
he |
A |
pos_legend |
Legend position |
graph_params |
Aesthetic ggplot parameters |
ceac |
|
Cost-Effectiveness Acceptability Curve (CEAC) Plot By Graph Device
Description
Choice of base R, ggplot2 or plotly.
Usage
ceac_plot_base(he, pos_legend, graph_params, ...)
## S3 method for class 'pairwise'
ceac_plot_base(he, pos_legend, graph_params, ...)
## S3 method for class 'bcea'
ceac_plot_base(he, pos_legend, graph_params, ...)
ceac_plot_ggplot(he, pos_legend, graph_params, ...)
## S3 method for class 'pairwise'
ceac_plot_ggplot(he, pos_legend, graph_params, ...)
## S3 method for class 'bcea'
ceac_plot_ggplot(he, pos_legend, graph_params, ...)
ceac_ggplot(he, pos_legend, graph_params, ceac, ...)
ceac_plot_plotly(he, pos_legend, graph_params, ...)
## S3 method for class 'pairwise'
ceac_plot_plotly(he, pos_legend, graph_params, ...)
## S3 method for class 'bcea'
ceac_plot_plotly(he, pos_legend, graph_params, ...)
ceac_plotly(he, pos_legend = "bottomright", graph_params, ceac, ...)
Arguments
he |
A |
pos_legend |
Legend position |
graph_params |
Aesthetic ggplot parameters |
... |
Additional arguments |
ceac |
ceac index in |
Cost-Effectiveness Acceptability Frontier (CEAF) plot
Description
Produces a plot the Cost-Effectiveness Acceptability Frontier (CEAF) against the willingness to pay threshold.
Usage
## S3 method for class 'pairwise'
ceaf.plot(mce, graph = c("base", "ggplot2", "plotly"), ...)
ceaf.plot(mce, ...)
Arguments
mce |
The output of the call to the function |
graph |
A string used to select the graphical engine to use for
plotting. Should (partial-) match the two options |
... |
Additional arguments |
Value
ceaf |
A ggplot object containing the plot. Returned only if
|
Author(s)
Gianluca Baio, Andrea Berardi
References
Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis in health economics.” Stat. Methods Med. Res., 1–20. ISSN 1477-0334, doi:10.1177/0962280211419832, https://pubmed.ncbi.nlm.nih.gov/21930515/.
Baio G (2013). Bayesian Methods in Health Economics. CRC.
See Also
Examples
# See Baio G., Dawid A.P. (2011) for a detailed description of the
# Bayesian model and economic problem
# Load the processed results of the MCMC simulation model
data(Vaccine)
# Runs the health economic evaluation using BCEA
m <- bcea(
e=eff,
c=cost, # defines the variables of
# effectiveness and cost
ref=2, # selects the 2nd row of (e, c)
# as containing the reference intervention
interventions=treats, # defines the labels to be associated
# with each intervention
Kmax=50000, # maximum value possible for the willingness
# to pay threshold; implies that k is chosen
# in a grid from the interval (0, Kmax)
plot=FALSE # inhibits graphical output
)
mce <- multi.ce(m) # uses the results of the economic analysis
ceaf.plot(mce) # plots the CEAF
ceaf.plot(mce, graph = "g") # uses ggplot2
# Use the smoking cessation dataset
data(Smoking)
m <- bcea(eff, cost, ref = 4, intervention = treats, Kmax = 500, plot = FALSE)
mce <- multi.ce(m)
ceaf.plot(mce)
Cost-Effectiveness Efficiency Frontier (CEEF) Plot
Description
The line connecting successive points on a cost-effectiveness plane which each represent the effect and cost associated with different treatment alternatives. The gradient of a line segment represents the ICER of the treatment comparison between the two alternatives represented by that segment. The cost-effectiveness frontier consists of the set of points corresponding to treatment alternatives that are considered to be cost-effective at different values of the cost-effectiveness threshold. The steeper the gradient between successive points on the frontier, the higher is the ICER between these treatment alternatives and the more expensive alternative would be considered cost-effective only when a high value of the cost-effectiveness threshold is assumed. Points not lying on the cost-effectiveness frontier represent treatment alternatives that are not considered cost-effective at any value of the cost-effectiveness threshold.
Usage
## S3 method for class 'bcea'
ceef.plot(
he,
comparators = NULL,
pos = "topright",
start.from.origins = TRUE,
threshold = NULL,
flip = FALSE,
dominance = TRUE,
print.summary = TRUE,
graph = c("base", "ggplot2", "plotly"),
print.plot = TRUE,
...
)
ceef.plot(he, ...)
Arguments
he |
A |
comparators |
Vector specifying the comparators to be included in the
frontier analysis. It must have a length > 1. Default as |
pos |
Parameter to set the position of the legend (only relevant for
multiple interventions, ie more than 2 interventions being compared).
Can be given in form
of a string |
start.from.origins |
Logical. Should the frontier start from the
origins of the axes? The argument is reset to |
threshold |
Specifies if the efficiency should be defined based on a
willingness-to-pay threshold value. If set to |
flip |
Logical. Should the axes of the plane be inverted? |
dominance |
Logical. Should the dominance regions be included in the plot? |
print.summary |
Logical. Should the efficiency frontier summary be printed along with the graph? See Details for additional information. |
graph |
A string used to select the graphical engine to use for
plotting. Should (partial-)match the two options |
print.plot |
Logical. Should the efficiency frontier be plotted? |
... |
If |
Details
Back compatibility with BCEA previous versions:
The bcea objects did not include the generating e and c
matrices in BCEA versions <2.1-0. This function is not compatible with
objects created with previous versions. The matrices can be appended to
bcea objects obtained using previous versions, making sure that the
class of the object remains unaltered.
The argument print.summary allows for printing a brief summary of the
efficiency frontier, with default to TRUE. Two tables are plotted,
one for the interventions included in the frontier and one for the dominated
interventions. The average costs and clinical benefits are included for each
intervention. The frontier table includes the slope for the increase in the
frontier and the non-frontier table displays the dominance type of each
dominated intervention. Please note that the slopes are defined as the
increment in the costs for a unit increment in the benefits even if
flip = TRUE for consistency with the ICER definition. The angle of
increase is in radians and depends on the definition of the axes, i.e. on
the value given to the flip argument.
Value
ceplane |
A ggplot object containing the plot. Returned only
if |
The function produces a plot of the
cost-effectiveness efficiency frontier. The dots show the simulated values
for the intervention-specific distributions of the effectiveness and costs.
The circles indicate the average of each bivariate distribution, with the
numbers referring to each included intervention. The numbers inside the
circles are black if the intervention is included in the frontier and grey
otherwise. If the option dominance is set to TRUE, the
dominance regions are plotted, indicating the areas of dominance.
Interventions in the areas between the dominance region and the frontier are
in a situation of extended dominance.
Author(s)
Andrea Berardi, Gianluca Baio
References
Baio G (2013). Bayesian Methods in Health Economics. CRC.
IQWIG (2009). “General Methods for the Assessment of the Relation of Benefits to Cost, Version 1.0.” Institute for Quality and Efficiency in Health Care (IQWIG).
See Also
Examples
## create the bcea object m for the smoking cessation example
data(Smoking)
m <- bcea(eff, cost, ref = 4, Kmax = 500, interventions = treats)
## produce plot
ceef.plot(m, graph = "base")
## tweak the options
## flip axis
ceef.plot(m,
flip = TRUE,
dominance = FALSE,
start.from.origins = FALSE,
print.summary = FALSE,
graph = "base")
## or use ggplot2 instead
if(require(ggplot2)){
ceef.plot(m,
dominance = TRUE,
start.from.origins = FALSE,
pos = TRUE,
print.summary = FALSE,
graph = "ggplot2")
}
Summary table for CEEF
Description
Summary table for CEEF
Usage
ceef.summary(he, frontier_data, frontier_params, include.ICER = FALSE, ...)
Arguments
he |
A |
frontier_data |
Frontier data |
frontier_params |
Frontier parameters |
include.ICER |
Should we include the ICER? default: FALSE |
... |
Additional arguments |
Value
Summary printed to console
Cost-effectiveness Efficiency Frontier Plot By Graph Device
Description
Choice of base R, ggplot2.
Usage
ceef_plot_ggplot(he, frontier_data, frontier_params, ...)
ceef_plot_plotly(he, frontier_data, frontier_params, ...)
ceef_plot_base(he, frontier_data, frontier_params)
Arguments
he |
A |
frontier_data |
Frontier data |
frontier_params |
Frontier parameters |
... |
Additional arguments |
Cost-effectiveness Plane Plot
Description
Produces a scatter plot of the cost-effectiveness plane, together with the sustainability area, as a function of the selected willingness to pay threshold.
Usage
## S3 method for class 'bcea'
ceplane.plot(
he,
comparison = NULL,
wtp = 25000,
pos = "topleft",
graph = c("base", "ggplot2", "plotly"),
...
)
ceplane.plot(he, ...)
Arguments
he |
A |
comparison |
Selects the comparator, in case of more than two
interventions being analysed. Default as |
wtp |
The value of the willingness to pay parameter. Not used if
|
pos |
Parameter to set the position of the legend; for a single
comparison plot, the ICER legend position. Can be given in form of a string
|
graph |
A string used to select the graphical engine to use for
plotting. Should (partial-) match the three options |
... |
If
|
Details
In the plotly version, colors can also
be specified as rgba colours using either the [plotly]toRGB
function or a rgba colour string, e.g. 'rgb(1, 1, 1, 1)'.
Value
If graph = "ggplot2" a ggplot object, or if graph = "plotly"
a plotly object containing the requested plot. Nothing is returned when
graph = "base", the default.
Grey dots show the simulated values for the joint distribution of the effectiveness and cost differentials. The larger red dot shows the ICER and the grey area identifies the sustainability area, i.e. the part of the plan for which the simulated values are below the willingness to pay threshold. The proportion of points in the sustainability area effectively represents the CEAC for a given value of the willingness to pay. If the comparators are more than 2 and no pairwise comparison is specified, all scatterplots are graphed using different colours.
Author(s)
Gianluca Baio, Andrea Berardi
References
Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis in health economics.” Stat. Methods Med. Res., 1–20. ISSN 1477-0334, doi:10.1177/0962280211419832, https://pubmed.ncbi.nlm.nih.gov/21930515/.
Baio G (2013). Bayesian Methods in Health Economics. CRC.
See Also
Examples
## create the bcea object for the smoking cessation example
data(Smoking)
m <- bcea(eff, cost, ref = 4, Kmax = 500, interventions = treats)
## produce the base plot
ceplane.plot(m, wtp = 200, graph = "base")
## select only one comparator
ceplane.plot(m, wtp = 200, graph = "base", comparison = 3)
## use ggplot2
if (requireNamespace("ggplot2")) {
ceplane.plot(m, wtp = 200, pos = "right", icer = list(size = 2), graph = "ggplot2")
}
## plotly
ceplane.plot(m, wtp = 200, graph = "plotly")
ceplane.plot(m, wtp = 200, comparison = 1, graph = "plotly")
Extract Separate Parameter Sets
Description
Extract Separate Parameter Sets
Usage
ceplane_geom_params(...)
Arguments
... |
Additional arguments |
CE-plane ggplot Parameters
Description
CE-plane ggplot Parameters
Usage
ceplane_ggplot_params(he, pos_legend, graph_params, ...)
Arguments
he |
A |
pos_legend |
Position of legend |
graph_params |
Other graphical parameters |
... |
Additional arguments |
Cost-Effectiveness Plane Plot By Graph Device
Description
Choice of base R, ggplot2 or plotly.
Usage
## S3 method for class 'bcea'
ceplane_plot_base(he, pos_legend, graph_params, ...)
ceplane_plot_base(he, ...)
## S3 method for class 'bcea'
ceplane_plot_ggplot(he, pos_legend, graph_params, ...)
ceplane_plot_ggplot(he, ...)
## S3 method for class 'bcea'
ceplane_plot_plotly(he, wtp = 25000, pos_legend, graph_params, ...)
ceplane_plot_plotly(he, ...)
Arguments
he |
A |
pos_legend |
Legend position |
graph_params |
Graph parameters in ggplot2 format |
... |
Additional arguments |
wtp |
Willingness to pay threshold; default 25,000 |
Value
For base R returns a plot
For ggplot2 returns ggplot2 object
For plotly returns a plot in the Viewer
Examples
# single comparator
data(Vaccine, package = "BCEA")
he <- bcea(eff, cost)
ceplane.plot(he, graph = "base")
## Not run:
# need to provide all the defaults because thats what
# ceplane.plot() does
graph_params <- list(xlab = "x-axis label",
ylab = "y-axis label",
title = "my title",
xlim = c(-0.002, 0.001),
ylim = c(-13, 5),
point = list(sizes = 1,
colors = "darkgrey"),
area = list(color = "lightgrey"))
he$delta_e <- as.matrix(he$delta_e)
he$delta_c <- as.matrix(he$delta_c)
BCEA::ceplane_plot_base(he, graph_params = graph_params)
## single non-default comparator
## multiple comparators
data(Smoking)
graph_params <- list(xlab = "x-axis label",
ylab = "y-axis label",
title = "my title",
xlim = c(-1, 2.5),
ylim = c(-1, 160),
point = list(sizes = 0.5,
colors = grey.colors(3, start = 0.1, end = 0.7)),
area = list(color = "lightgrey"))
he <- bcea(eff, cost, ref = 4, Kmax = 500, interventions = treats)
BCEA::ceplane_plot_base(he,
wtp = 200,
pos_legend = FALSE,
graph_params = graph_params)
## End(Not run)
data(Vaccine)
he <- bcea(eff, cost)
ceplane.plot(he, graph = "ggplot2")
ceplane.plot(he, wtp=10000, graph = "ggplot2",
point = list(colors = "blue", sizes = 2),
area = list(col = "springgreen3"))
data(Smoking)
he <- bcea(eff, cost, ref = 4, Kmax = 500, interventions = treats)
ceplane.plot(he, graph = "ggplot2")
ceplane.plot(he,
wtp = 200,
pos = "right",
ICER_size = 2,
graph = "ggplot2")
ceplane.plot(he,
wtp = 200,
pos = TRUE,
graph = "ggplot2")
ceplane.plot(he,
graph = "ggplot2",
wtp=200,
theme = ggplot2::theme_linedraw())
Comparison Names From
Description
Comparison Names From
Usage
comp_names_from_(df_ce)
Arguments
df_ce |
Cost-effectiveness dataframe |
Compute Cost-Effectiveness Acceptability Curve
Description
Compute Cost-Effectiveness Acceptability Curve
Usage
compute_CEAC(ib)
Arguments
ib |
Incremental benefit |
Value
Array with dimensions (interv x k)
See Also
Compute Expected Incremental Benefit
Description
A summary measure useful to assess the potential changes in the decision under different scenarios.
Usage
compute_EIB(ib)
Arguments
ib |
Incremental benefit |
Details
When considering a pairwise comparison
(e.g. in the simple case of a reference intervention t = 1 and a comparator,
such as the status quo, t = 0), it is defined as the difference between the
expected utilities of the two alternatives:
eib := \mbox{E}[u(e,c;1)] - \mbox{E}[u(e,c;0)] = \mathcal{U}^1 - \mathcal{U}^0.
Analysis of the expected incremental benefit describes how the decision changes for different values of the threshold. The EIB marginalises out the uncertainty, and does not incorporate and describe explicitly the uncertainty in the outcomes. To overcome this problem the tool of choice is the CEAC.
Value
Array with dimensions (interv x k)
See Also
ceac.plot(), compute_CEAC(), compute_IB()
Compute Expected Value of Information
Description
Compute Expected Value of Information
Usage
compute_EVI(ol)
Arguments
ol |
Opportunity loss |
Value
EVI
Compute Incremental Benefit
Description
Sample of incremental net monetary benefit for each
willingness-to-pay threshold, k, and comparator.
Usage
compute_IB(df_ce, k)
Arguments
df_ce |
Dataframe of cost and effectiveness deltas |
k |
Vector of willingness to pay values |
Details
Defined as:
IB = u(e,c; 1) - u(e,c; 0).
If the net benefit function is used as utility function, the definition can be re-written as
IB = k\cdot\Delta_e - \Delta_c.
Value
Array with dimensions (k x sim x ints)
See Also
Compute Incremental Cost-Effectiveness Ratio
Description
Defined as
Usage
compute_ICER(df_ce)
Arguments
df_ce |
Cost-effectiveness dataframe |
Details
ICER = \Delta_c/\Delta_e
Value
ICER for all comparisons
Compute U Statistic
Description
Sample of net (monetary) benefit for each willingness-to-pay threshold and intervention.
Usage
compute_U(df_ce, k)
Arguments
df_ce |
Cost-effectiveness dataframe |
k |
Willingness to pay vector |
Value
Array with dimensions (sim x k x ints)
Compute NB for mixture of interventions
Description
Compute NB for mixture of interventions
Usage
compute_Ubar(he, value)
Arguments
he |
A |
value |
Mixture weights |
Compute Ustar Statistic
Description
The maximum utility value among the comparators, indicating which intervention produced the most benefits at each simulation.
Usage
compute_Ustar(U)
Arguments
U |
Net monetary benefit (sim x k x intervs) |
Value
Array with dimensions (sim x k)
Compute Cost-Effectiveness Acceptability Frontier
Description
Compute Cost-Effectiveness Acceptability Frontier
Usage
compute_ceaf(p_best_interv)
Arguments
p_best_interv |
Probability of being best intervention |
Calculate Credible Intervals
Description
For expected incremental benefit plot.
Usage
compute_eib_cri(he, alpha_cri = 0.05, cri.quantile = TRUE)
Arguments
he |
A |
alpha_cri |
Significance level, 0 - 1 |
cri.quantile |
Credible interval quantile?; logical |
Value
cri
Compute k^*
Description
Find willingness-to-pay threshold when optimal decision changes.
Usage
compute_kstar(k, best, ref)
Arguments
k |
Willingness-to-pay grid approximation of the budget willing to invest (vector) |
best |
Best intervention for each |
ref |
Reference intervention (int) |
Details
k^* := \min\{k : IB < 0 \}
The value of the break-even point corresponds to the ICER and quantifies the point at which the decision-maker is indifferent between the two options.
Value
integer representing intervention
See Also
Compute Opportunity Loss
Description
The difference between the maximum utility computed for the current
parameter configuration (e.g. at the current simulation) U^* and the current
utility of the intervention associated with the maximum utility overall.
Usage
compute_ol(Ustar, U, best)
Arguments
Ustar |
Maximum utility value (sim x k) |
U |
Net monetary benefit (sim x k x interv) |
best |
Best intervention for given willingness-to-pay (k) |
Details
In mathematical notation,
\textrm{OL}(\theta) := U^*(\theta) - U(\theta^\tau)
where \tau is the intervention associated with the overall maximum utility
and U^*(\theta) is the maximum utility value among the comparators in the given simulation.
The opportunity loss is a non-negative quantity, since U(\theta^\tau)\leq U^*(\theta).
In all simulations where the intervention is more
cost-effective (i.e. when incremental benefit is positive), then \textrm{OL}(\theta) = 0
as there would be no opportunity loss, if the parameter configuration were the
one obtained in the current simulation.
Value
Array with dimensions (sim x k)
See Also
Compute Probability Best Intervention
Description
Compute Probability Best Intervention
Usage
compute_p_best_interv(he)
Arguments
he |
A |
Compute Probability Optimal Intervention Best
Description
Compute Probability Optimal Intervention Best
Usage
compute_p_optimal_best(he)
Arguments
he |
A |
Compute Value of Information
Description
The difference between the maximum utility computed for the current
parameter configuration U^* and the utility of the intervention which
is associated with the maximum utility overall.
Usage
compute_vi(Ustar, U)
Arguments
Ustar |
Maximum utility value (sim x k) |
U |
Net monetary benefit (sim x k x interv) |
Details
The value of obtaining additional information on the parameter \theta
to reduce the uncertainty in the decisional process.
It is defined as:
\textrm{VI}(\theta) := U^*(\theta) - \mathcal{U}^*
with U^*(\theta) the maximum utility value for the given simulation
among all comparators and \mathcal{U}^*(\theta) the expected utility
gained by the adoption of the cost-effective intervention.
Value
Array with dimensions (sim x k)
See Also
Contour Plots for the Cost-Effectiveness Plane
Description
Contour method for objects in the class bcea.
Produces a scatterplot of the cost-effectiveness plane, with a contour-plot
of the bivariate density of the differentials of cost (y-axis) and
effectiveness (x-axis).
Usage
## S3 method for class 'bcea'
contour(
he,
pos = "topleft",
graph = c("base", "ggplot2", "plotly"),
comparison = NULL,
...
)
contour(he, ...)
Arguments
he |
A |
pos |
Parameter to set the position of the legend (only relevant for
multiple interventions, ie more than 2 interventions being compared).
Can be given in form
of a string |
graph |
A string used to select the graphical engine to use for
plotting. Should (partial-)match the three options |
comparison |
Selects the comparator, in case of more than two
interventions being analysed. Default as NULL plots all the comparisons
together. Any subset of the possible comparisons can be selected (e.g.,
|
... |
Additional graphical arguments. The usual ggplot2 syntax is used regardless of graph type.
|
Value
ceplane |
A ggplot object containing the plot. Returned only
if |
Plots the cost-effectiveness plane with a
scatterplot of all the simulated values from the (posterior) bivariate
distribution of (\Delta_e, \Delta_c), the differentials of effectiveness and
costs; superimposes a contour of the distribution and prints the estimated
value of the probability of each quadrant (combination of positive/negative
values for both \Delta_e and \Delta_c)
Author(s)
Gianluca Baio, Andrea Berardi
References
Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis in health economics.” Stat. Methods Med. Res., 1–20. ISSN 1477-0334, doi:10.1177/0962280211419832, https://pubmed.ncbi.nlm.nih.gov/21930515/.
Baio G (2013). Bayesian Methods in Health Economics. CRC.
See Also
bcea(),
ceplane.plot(),
contour2()
Examples
data(Vaccine)
# run the health economic evaluation using BCEA
m <- bcea(e=eff,
c=cost, # defines the variables of
# effectiveness and cost
ref=2, # selects the 2nd row of (e,c)
# as containing the reference intervention
interventions=treats, # defines the labels to be associated
# with each intervention
Kmax=50000, # maximum value possible for the willingness
# to pay threshold; implies that k is chosen
# in a grid from the interval (0,Kmax)
plot=TRUE # plots the results
)
contour(m)
contour(m, graph = "ggplot2")
contour(m, # uses the results of the economic evaluation
# (a "bcea" object)
comparison=1, # if more than 2 interventions, selects the
# pairwise comparison
nlevels=10, # selects the number of levels to be
# plotted (default=4)
levels=NULL, # specifies the actual levels to be plotted
# (default=NULL, so that R will decide)
scale=1, # scales the bandwidths for both x- and
# y-axis (default=0.5)
graph="base" # uses base graphics to produce the plot
)
# use the smoking cessation dataset
data(Smoking)
m <- bcea(eff, cost, ref = 4, intervention = treats, Kmax = 500, plot = FALSE)
contour(m)
contour(m, graph = "ggplot2")
Specialised CE-plane Contour Plot
Description
Produces a scatterplot of the cost-effectiveness plane, with a contour-plot of the bivariate density of the differentials of cost (y-axis) and effectiveness (x-axis). Also adds the sustainability area (i.e. below the selected value of the willingness-to-pay threshold).
Usage
## S3 method for class 'bcea'
contour2(
he,
comparison = NULL,
wtp = 25000,
graph = c("base", "ggplot2", "plotly"),
pos = "topleft",
...
)
contour2(he, ...)
Arguments
he |
A |
comparison |
The comparison being plotted. Default to |
wtp |
The selected value of the willingness-to-pay. Default is
|
graph |
A string used to select the graphical engine to use for
plotting. Should (partial-)match the three options |
pos |
Parameter to set the position of the legend (only relevant for
multiple interventions, ie more than 2 interventions being compared).
Can be given in form
of a string |
... |
Arguments to be passed to |
Value
contour |
A ggplot item containing the requested plot.
Returned only if |
Plots the cost-effectiveness
plane with a scatterplot of all the simulated values from the (posterior)
bivariate distribution of (\Delta_e, \Delta_c), the differentials of
effectiveness and costs; superimposes a contour of the distribution and
prints the value of the ICER, together with the sustainability area.
Author(s)
Gianluca Baio, Andrea Berardi
References
Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis in health economics.” Stat. Methods Med. Res., 1–20. ISSN 1477-0334, doi:10.1177/0962280211419832, https://pubmed.ncbi.nlm.nih.gov/21930515/.
Baio G (2013). Bayesian Methods in Health Economics. CRC.
See Also
bcea(),
ceplane.plot(),
contour()
Examples
## create the bcea object m for the smoking cessation example
data(Smoking)
m <- bcea(eff, cost, ref = 4, interventions = treats, Kmax = 500)
## produce the plot
contour2(m,
wtp = 200,
graph_type = "base")
## or use ggplot2 to plot multiple comparisons
contour2(m,
wtp = 200,
ICER_size = 2,
graph_type = "ggplot2")
## vaccination example
data(Vaccine)
treats = c("Status quo", "Vaccination")
m <- bcea(eff, cost, ref = 2, interventions = treats, Kmax = 50000)
contour2(m)
contour2(m, wtp = 100)
Contour ggplot2 Parameters
Description
Contour ggplot2 Parameters
Usage
contour_ggplot_params(he, pos_legend, graph_params, ...)
Arguments
he |
A |
pos_legend |
legend position |
graph_params |
Other graphical parameters |
... |
Additional arguments |
Contour Cost-Effectiveness Plane
Description
Choice of base R, ggplot2.
Usage
contour_base(he, pos_legend, graph_params, ...)
contour_ggplot(he, pos_legend, graph_params, ...)
contour_plotly(he, pos_legend, graph_params, ...)
Arguments
he |
A |
pos_legend |
Legend position |
graph_params |
Plot parameters; list |
... |
Additional arguments |
Value
For plotly returns a plot in the Viewer
See Also
Add contour lines to a plotly contour or CE plane plot
Description
Add contour lines to a plotly contour or CE plane plot
Usage
contour_plotly_lines(plotly, he, delta_ce, graph_params, pt_cols)
Arguments
plotly |
plotly object generated by calling function |
he |
bcea object of interest |
delta_ce |
Object generated by calling function (long format) |
graph_params |
Other graphical parameters |
pt_cols |
Vector of colours for the interventions |
See Also
Use from Base R to ggplot
Description
Use from Base R to ggplot
Usage
convert_pts_to_mm(x)
Arguments
x |
points |
Create Inputs for EVPI Calculation
Description
Creates an object containing the matrix with the parameters simulated using
the MCMC procedure (using JAGS, BUGS or Stan) and a vector of parameters
(strings) that can be used to perform the expected value of partial
information analysis. In the process, createInputs also checks for
linear dependency among columns of the PSA samples or columns having
constant values and removes them to only leave the fundamental parameters
(to run VoI analysis). This also deals with simulations stored in a
.csv or .txt file (e.g. as obtained using bootstrapping from a
non-Bayesian model).
Usage
## Default S3 method:
createInputs(inputs, print_is_linear_comb = TRUE)
createInputs(inputs, print_is_linear_comb = TRUE)
## S3 method for class 'rjags'
createInputs(inputs, print_is_linear_comb = TRUE)
## S3 method for class 'bugs'
createInputs(inputs, print_is_linear_comb = TRUE)
## S3 method for class 'stanfit'
createInputs(inputs, print_is_linear_comb = TRUE)
## S3 method for class 'data.frame'
createInputs(inputs, print_is_linear_comb = TRUE)
## S3 method for class 'numeric'
createInputs(inputs, print_is_linear_comb = TRUE)
Arguments
inputs |
A |
print_is_linear_comb |
Logical indicator. If set to |
Value
mat |
Data.frame containing all the simulations for all the monitored parameters |
parameters |
Character vectors of the names of all the monitored parameters |
Author(s)
Gianluca Baio, Anna Heath and Mark Strong
See Also
Diagnostic Plots For The Results Of The EVPPI
Description
The function produces either a residual plot comparing the fitted values from the INLA-SPDE Gaussian Process regression to the residuals. This is a scatter plot of residuals on the y axis and fitted values (estimated responses) on the x axis. The plot is used to detect non-linearity, unequal error variances, and outliers. A well-behaved residual plot supporting the appropriateness of the simple linear regression model has the following characteristics:
The residuals bounce randomly around the 0 line. This suggests that the assumption that the relationship is linear is reasonable.
The residuals roughly form a horizontal band around the 0 line. This suggests that the variances of the error terms are equal.
None of the residual stands out from the basic random pattern of residuals. This suggests that there are no outliers.
Usage
diag.evppi(evppi, he, plot_type = c("residuals", "qqplot"), interv = 1)
Arguments
evppi |
A |
he |
A |
plot_type |
The type of diagnostics to be performed. It can be the 'residual
plot' ( |
interv |
Specifies the interventions for which diagnostic tests should be performed (if there are many options being compared) |
Details
The second possible diagnostic is the Q-Q plot for the fitted value. This is a graphical method for comparing the fitted values distributions with the assumed underlying normal distribution by plotting their quantiles against each other. First, the set of intervals for the quantiles is chosen. A point (x,y) on the plot corresponds to one of the quantiles of the second distribution (y-coordinate) plotted against the same quantile of the first distribution (x-coordinate). If the two distributions being compared are identical, the Q-Q plot follows the 45 degrees line.
Value
Plot
Author(s)
Gianluca Baio, Anna Heath
References
Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis in health economics.” Stat. Methods Med. Res., 1–20. ISSN 1477-0334, doi:10.1177/0962280211419832, https://pubmed.ncbi.nlm.nih.gov/21930515/.
Baio G (2013). Bayesian Methods in Health Economics. CRC.
See Also
Expected Incremental Benefit (EIB) Plot
Description
Produces a plot of the Expected Incremental Benefit (EIB) as a function of the willingness to pay.
Usage
## S3 method for class 'bcea'
eib.plot(
he,
comparison = NULL,
pos = "bottomright",
size = NULL,
plot.cri = NULL,
graph = c("base", "ggplot2", "plotly"),
...
)
eib.plot(he, ...)
Arguments
he |
A |
comparison |
Selects the comparator, in case of more than two
interventions being analysed. Default as NULL plots all the comparisons
together. Any subset of the possible comparisons can be selected (e.g.,
|
pos |
Parameter to set the position of the legend (only relevant for
multiple interventions, ie more than 2 interventions being compared).
Can be given in form
of a string |
size |
Value (in millimetres) of the size of the willingness to pay
label. Used only if |
plot.cri |
Logical value. Should the credible intervals be plotted
along with the expected incremental benefit? Default as |
graph |
A string used to select the graphical engine to use for
plotting. Should (partial-)match the three options |
... |
If
|
Value
eib |
If |
The function produces a plot of the
Expected Incremental Benefit as a function of the discrete grid
approximation of the willingness to pay parameter. The break even point
(i.e. the point in which the EIB = 0, i.e. when the optimal decision changes
from one intervention to another) is also showed by default. The value k* is
the discrete grid approximation of the ICER.
Author(s)
Gianluca Baio, Andrea Berardi
References
Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis in health economics.” Stat. Methods Med. Res., 1–20. ISSN 1477-0334, doi:10.1177/0962280211419832, https://pubmed.ncbi.nlm.nih.gov/21930515/.
Baio G (2013). Bayesian Methods in Health Economics. CRC.
See Also
bcea(),
ib.plot(),
ceplane.plot()
Examples
data(Vaccine)
# Runs the health economic evaluation using BCEA
m <- bcea(
e=eff,
c=cost, # defines the variables of
# effectiveness and cost
ref=2, # selects the 2nd row of (e, c)
# as containing the reference intervention
interventions=treats, # defines the labels to be associated
# with each intervention
Kmax=50000, # maximum value possible for the willingness
# to pay threshold; implies that k is chosen
# in a grid from the interval (0, Kmax)
plot=FALSE # plots the results
)
eib.plot(m)
eib.plot(m, graph = "ggplot2") + ggplot2::theme_linedraw()
data(Smoking)
treats <- c("No intervention", "Self-help",
"Individual counselling", "Group counselling")
m <- bcea(eff, cost, ref = 4, interventions = treats, Kmax = 500)
eib.plot(m)
EIB parameters specific to base R plot
Description
EIB parameters specific to base R plot
Usage
eib_params_base(he, graph_params, cri_params)
Arguments
he |
A |
graph_params |
Type of plot device |
cri_params |
Credible interval parameters |
Value
list
EIB Parameters CrI
Description
EIB Parameters CrI
Usage
eib_params_cri(he, graph_params)
Expected Incremental Benefit Plot By Graph Device
Description
Choice of base R, ggplot2 or plotly.
Usage
eib_plot_base(he, graph_params, ...)
eib_plot_ggplot(he, graph_params, ...)
eib_plot_plotly(he, graph_params, ...)
Arguments
he |
A |
graph_params |
Graph parameters |
... |
Additional parameters |
Expected Value of Information (EVI) Plot
Description
Plots the Expected Value of Information (EVI) against the willingness to pay.
Usage
## S3 method for class 'bcea'
evi.plot(he, graph = c("base", "ggplot2", "plotly"), ...)
evi.plot(he, ...)
Arguments
he |
A |
graph |
A string used to select the graphical engine to use for
plotting. Should (partial-)match the three options |
... |
Additional graphical arguments:
|
Value
eib |
If |
The function produces a plot of the Expected Value of Information as a function of the discrete grid approximation of the willingness to pay parameter. The break even point(s) (i.e. the point in which the EIB=0, ie when the optimal decision changes from one intervention to another) is(are) also showed.
Author(s)
Gianluca Baio, Andrea Berardi
References
Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis in health economics.” Stat. Methods Med. Res., 1–20. ISSN 1477-0334, doi:10.1177/0962280211419832, https://pubmed.ncbi.nlm.nih.gov/21930515/.
Baio G (2013). Bayesian Methods in Health Economics. CRC.
See Also
bcea(),
ceac.plot(),
ceplane.plot()
Examples
data(Vaccine)
m <- bcea(
e=eff,
c=cost, # defines the variables of
# effectiveness and cost
ref=2, # selects the 2nd row of (e, c)
# as containing the reference intervention
interventions=treats, # defines the labels to be associated
# with each intervention
Kmax=50000, # maximum value possible for the willingness
# to pay threshold; implies that k is chosen
# in a grid from the interval (0, Kmax)
plot=FALSE # plots the results
)
evi.plot(m)
data(Smoking)
treats <- c("No intervention", "Self-help",
"Individual counselling", "Group counselling")
m <- bcea(eff, cost, ref = 4, interventions = treats, Kmax = 500)
evi.plot(m)
EVI Plot of the Health Economic Analysis For Mixed Analysis
Description
Compares the optimal scenario to the mixed case in terms of the EVPI.
Usage
## S3 method for class 'mixedAn'
evi.plot(he, y.limits = NULL, pos = c(0, 1), graph = c("base", "ggplot2"), ...)
Arguments
he |
An object of class |
y.limits |
Range of the y-axis for the graph. The default value is
|
pos |
Parameter to set the position of the legend (only relevant for
multiple interventions, ie more than 2 interventions being compared).
Can be given in form
of a string |
graph |
A string used to select the graphical engine to use for
plotting. Should (partial-)match the two options |
... |
Arguments to be passed to methods, such as graphical parameters
(see |
Value
evi |
A ggplot object containing the plot. Returned only if
|
The function produces a graph showing the difference between the ”optimal” version of the EVPI (when only the most cost-effective intervention is included in the market) and the mixed strategy one (when more than one intervention is considered in the market).
Author(s)
Gianluca Baio, Andrea Berardi
References
Baio G, Russo P (2009). “A decision-theoretic framework for the application of cost-effectiveness analysis in regulatory processes.” Pharmacoeconomics, 27(8), 5–16. ISSN 20356137, doi:10.1007/bf03320526.
Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis in health economics.” Stat. Methods Med. Res., 1–20. ISSN 1477-0334, doi:10.1177/0962280211419832, https://pubmed.ncbi.nlm.nih.gov/21930515/.
Baio G (2013). Bayesian Methods in Health Economics. CRC.
See Also
Examples
# See Baio G., Dawid A.P. (2011) for a detailed description of the
# Bayesian model and economic problem
#
# Load the processed results of the MCMC simulation model
data(Vaccine)
# Runs the health economic evaluation using BCEA
m <- bcea(e=eff, c=cost, # defines the variables of
# effectiveness and cost
ref=2, # selects the 2nd row of (e,c)
# as containing the reference intervention
interventions=treats, # defines the labels to be associated
# with each intervention
Kmax=50000, # maximum value possible for the willingness
# to pay threshold; implies that k is chosen
# in a grid from the interval (0,Kmax)
plot=FALSE # inhibits graphical output
)
mixedAn(m) <- NULL # uses the results of the mixed strategy
# analysis (a "mixedAn" object)
# the vector of market shares can be defined
# externally. If NULL, then each of the T
# interventions will have 1/T market share
# produces the plots
evi.plot(m)
evi.plot(m, graph="base")
# Or with ggplot2
if (require(ggplot2)) {
evi.plot(m, graph="ggplot2")
}
Expected Value of Information Plot By Graph Device
Description
Choice of base R, ggplot2 or plotly.
Usage
evi_plot_base(he, data.psa, plot_aes, plot_annotations)
evi_plot_ggplot(he, data.psa, plot_aes, plot_annotations)
evi_plot_plotly(data.psa, plot_aes, plot_annotations)
Arguments
he |
A |
data.psa |
Data |
plot_aes |
Aesthetic parameters |
plot_annotations |
Plot parameters |
Expected Value of Perfect Partial Information (EVPPI) for Selected Parameters
Description
Calculates the Expected Value of Perfect Partial Information (EVPPI) for subsets of parameters. Uses GAM non-parametric regression for single parameter EVPPI and the SPDE-INLA method for larger parameter subsets.
Usage
evppi(he, param_idx, input, N = NULL, plot = FALSE, residuals = TRUE, ...)
## Default S3 method:
evppi(he, ...)
## S3 method for class 'bcea'
evppi(
he,
param_idx = NULL,
input,
N = NULL,
plot = FALSE,
residuals = TRUE,
method = NULL,
...
)
Arguments
he |
A |
param_idx |
A vector of parameters for which the EVPPI should be calculated. This can be given as a string (or vector of strings) of names or a numeric vector, corresponding to the column numbers of important parameters. |
input |
A matrix containing the simulations for all the parameters monitored by the call to JAGS or BUGS. The matrix should have column names matching the names of the parameters and the values in the vector parameter should match at least one of those values. |
N |
The number of PSA simulations used to calculate the EVPPI. The default uses all the available samples. |
plot |
A logical value indicating whether the triangular mesh for
SPDE-INLA should be plotted. Default set to |
residuals |
A logical value indicating whether the fitted values for
the SPDE-INLA method should be outputted. Default set to |
... |
Additional arguments. Details of the methods to compute the EVPPI and their additional arguments are:
|
method |
Character string to select which method to use. The default methods are recommended. However, it is possible (mainly for backward compatibility) to use different methods. |
Details
The single parameter EVPPI has been calculated using the non-parametric GAM regression developed by Strong et al. (2014). The multi-parameter EVPPI is calculated using the SPDE-INLA regression method for Gaussian Process regression developed by Heath et al. (2015).
This function has been completely changed and restructured to make it possible
to change regression method.
The method argument can now be given as a list. The first element element in the
list is a vector giving the regression method for the effects. The second gives
the regression method for the costs. The method' argument can also be given as before which then uses the same regression method for all curves. All other extra_argscan be given as before.int.ordcan be updated using the list formulation above to give the interactions for each different curve. The formula argument for GAM can only be given once, eitherte()ors() + s()'
as this is for computational reasons rather than to aid fit.
You can still plot the INLA mesh elements but not output the meshes.
Value
An object of class evppi, containing the following components:
-
evppi: The computed values of
evppifor all values of the parameter of willingness to pay. -
index: A numerical vector with the indices of the parameters for which the EVPPI was calculated.
-
k: A vector of values for the willingness to pay.
-
evi: A vector of values for the overall EVPPI.
-
fitted.costs: The fitted values for the costs.
-
fitted.effects: The fitted values for the effects.
-
parameters: A single string containing the names of the parameters for which the EVPPI was calculated (used for plotting).
-
time: The computational time (in seconds).
-
fit.c: The object produced by the model fit for the costs.
-
fit.e: The object produced by the model fit for the effects.
-
formula: The formula used to fit the model.
-
method: A string indicating the method used to estimate the EVPPI.
GAM regression
For multi-parameter, the user can select 3 possible methods. If
method = "GAM" (BCEA will accept also "gam", "G" or
"g"), then the computations are based on GAM regression. The user can
also specify the formula for the regression. The default option is to use a
tensor product (e.g. if there are two main parameters, p1 and
p2, this amounts to setting formula = "te(p1,p2)", which
indicates that the two parameters interact). Alternatively, it is possible
to specify a model in which the parameters are independent using the
notation formula = "s(p1) + s(p2)". This may lead to worse accuracy in
the estimates.
Strong et al. GP regression
This is used if method="GP" (BCEA will also accept the specification
method="gp"). In this case, the user can also specify the number of
PSA runs that should be used to estimate the hyperparameters of the model
(e.g. n.sim=100). This value is set by default to 500.
INLA-related options
These are all rather technical and are described in detail in Baio et al. (2017).
The optional parameter vector int.ord can take integer values (c(1,1) is
default) and will force the predictor to include interactions: if
int.ord = c(k, h), then all k-way interactions will be used for the
effects and all h-way interactions will be used for the costs. Also, the
user can specify the feature of the mesh for the "spatial" part of the
model. The optional parameter cutoff (default 0.3) controls the
density of the points inside the mesh. Acceptable values are typically in
the interval (0.1, 0.5), with lower values implying more points (and thus
better approximation and greater computational time). The construction of the
boundaries for the mesh can be controlled by the optional inputs
convex.inner (default = -0.4) and convex.outer (default =
-0.7). These should be negative values and can be decreased (say to -0.7 and
-1, respectively) to increase the distance between the points and the outer
boundary, which also increases precision and computational time. The
optional argumentrobust can be set to TRUE, in which case INLA will
use a t prior distribution for the coefficients of the linear predictor.
Finally, the user can control the accuracy of the INLA grid-search for the
estimation of the hyperparameters. This is done by setting a value
h.value (default = 0.00005). Lower values imply a more refined search
(and hence better accuracy), at the expense of computational speed. The
method argument can also be given as a list allowing different regression
methods for the effects and costs, and the different incremental decisions.
The first list element should contain a vector of methods for the
incremental effects and the second for the costs, for example
method = list(c("GAM"), c("INLA")). The int.ord argument can also
be given as a list to give different interaction levels for each regression
curve.
By default, when no method is specified by the user, evppi will
use GAM if the number of parameters is <5 and INLA otherwise.
Author(s)
Anna Heath, Gianluca Baio
References
Strong M, Oakley JE, Brennan A (2014). “Estimating Multiparameter Partial Expected Value of Perfect Information from a Probabilistic Sensitivity Analysis Sample : A Nonparametric Regression Approach.” Medical Decision Making, 311–326. doi:10.1177/0272989X13505910.
Sadatsafavi M, Bansback N, Zafari Z, Najafzadeh M, Marra C (2013). “Need for speed: An efficient algorithm for calculation of single-parameter expected value of partial perfect information.” Value Heal., 16(2), 438–448. ISSN 10983015, doi:10.1016/j.jval.2012.10.018, http://dx.doi.org/10.1016/j.jval.2012.10.018.
Baio G (2013). Bayesian Methods in Health Economics. CRC.
Baio, Gianluca, Berardi, Andrea, Heath A (2017). Bayesian Cost-Effectiveness Analysis with the R package BCEA. Springer International Publishing. https://link.springer.com/book/10.1007/978-3-319-55718-2.
Heath A, Manolopoulou I, Baio G (2016). “Estimating the expected value of partial perfect information in health economic evaluations using integrated nested Laplace approximation.” Stat. Med., 35(23), 4264–4280. ISSN 0277-6715, doi:10.1002/sim.6983, 1504.05436, https://pubmed.ncbi.nlm.nih.gov/27189534/.
See Also
Examples
# See Baio G., Dawid A.P. (2011) for a detailed description of the
# Bayesian model and economic problem
## Not run:
# Load the post-processed results of the MCMC simulation model
# original JAGS output is can be downloaded from here
# https://gianluca.statistica.it/books/bcea/code/vaccine.RData
data(Vaccine, package = "BCEA")
treats <- c("Status quo", "Vaccination")
# Run the health economic evaluation using BCEA
m <- bcea(e.pts, c.pts, ref = 2, interventions = treats)
# Compute the EVPPI for a bunch of parameters
inp <- createInputs(vaccine_mat)
# explicitly use BCEA package namespace to avoid voi package conflict
EVPPI <- BCEA::evppi(m, c("beta.1." , "beta.2."), inp$mat)
plot(EVPPI)
# deprecated (single parameter) methods
EVPPI.so <- BCEA::evppi(m, c("beta.1.", "beta.2."), inp$mat, method = "so", n.blocks = 50)
EVPPI.sad <- BCEA::evppi(m, c("beta.1.", "beta.2."), inp$mat, method = "sad", n.seps = 1)
plot(EVPPI.so)
plot(EVPPI.sad)
# Compute the EVPPI using INLA/SPDE
if (require("INLA"))
x_inla <- BCEA::evppi(he = m, 39:40, input = inp$mat)
# using GAM regression
x_gam <- BCEA::evppi(he = m, 39:40, input = inp$mat, method = "GAM")
# using Strong et al GP regression
x_gp <- BCEA::evppi(he = m, 39:40, input = inp$mat, method = "GP")
# plot results
if (require("INLA")) plot(x_inla)
points(x_inla$k, x_inla$evppi, type = "l", lwd = 2, lty = 2)
points(x_gam$k, x_gam$evppi, type = "l", col = "red")
points(x_gp$k, x_gp$evppi, type = "l", col = "blue")
if (require("INLA")) {
plot(x_inla$k, x_inla$evppi, type = "l", lwd = 2, lty = 2)
points(x_gam$k, x_gam$evppi, type = "l", col = "red")
points(x_gp$k, x_gp$evppi, type = "l", col = "blue")
}
data(Smoking)
treats <- c("No intervention", "Self-help",
"Individual counselling", "Group counselling")
m <- bcea(eff, cost, ref = 4, interventions = treats, Kmax = 500)
inp <- createInputs(smoking_output)
EVPPI <- BCEA::evppi(m, c(2,3), inp$mat, h.value = 0.0000005)
plot(EVPPI)
## End(Not run)
Plot Expected Value of Partial Information With Respect to a Set of Parameters
Description
Base R and ggplot2 versions.
Usage
evppi_plot_base(evppi_obj, pos_legend, col = NULL, annot = FALSE)
evppi_plot_ggplot(evppi_obj, pos_legend = c(0, 0.8), col = c(1, 1), ...)
Arguments
evppi_obj |
Object of class evppi |
pos_legend |
Position of legend |
col |
Colour |
annot |
Annotate EVPPI curve with parameter names |
... |
Additional arguments |
Q-Q Plot
Description
Q-Q Plot
Usage
evppi_qq_plot(evppi, he, interv)
Residual Plot
Description
Residual Plot
Usage
evppi_residual_plot(evppi, he, interv)
Credible interval ggplot geom
Description
Credible interval ggplot geom
Usage
geom_cri(plot.cri = TRUE, params = NA)
Arguments
plot.cri |
Should we plot CrI? Logical |
params |
Plot parameters including data |
Geom Quadrant Text
Description
Geom Quadrant Text
Usage
geom_quad_txt(he, graph_params)
Arguments
he |
A |
graph_params |
Plot parameters; list |
Get fitted values from evppi object
Description
Get fitted values from evppi object
Usage
get_fitted_(val, voi_methods, voi_models)
Value
matrix
Incremental Benefit (IB) Distribution Plot
Description
Plots the distribution of the Incremental Benefit (IB) for a given value of the willingness to pay threshold.
Usage
## S3 method for class 'bcea'
ib.plot(
he,
comparison = NULL,
wtp = 25000,
bw = "bcv",
n = 512,
xlim = NULL,
graph = c("base", "ggplot2", "plotly"),
...
)
ib.plot(he, ...)
Arguments
he |
A |
comparison |
In the case of multiple interventions, specifies the one
to be used in comparison with the reference. Default value of |
wtp |
The value of the willingness to pay threshold. Default value at
|
bw |
Identifies the smoothing bandwidth used to construct the kernel estimation of the IB density. |
n |
The number of equally spaced points at which the density is to be estimated. |
xlim |
The limits of the plot on the x-axis. |
graph |
A string used to select the graphical engine to use for
plotting. Should (partial-) match the two options |
... |
Additional arguments |
Value
ib |
A ggplot object containing the requested plot. Returned
only if |
The function produces a plot of the distribution of the Incremental Benefit for a given value of the willingness to pay parameter. The dashed area indicates the positive part of the distribution (i.e. when the reference is more cost-effective than the comparator).
Author(s)
Gianluca Baio, Andrea Berardi
References
Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis in health economics.” Stat. Methods Med. Res., 1–20. ISSN 1477-0334, doi:10.1177/0962280211419832, https://pubmed.ncbi.nlm.nih.gov/21930515/.
Baio G (2013). Bayesian Methods in Health Economics. CRC.
See Also
Examples
data("Vaccine")
he <- BCEA::bcea(eff, cost)
ib.plot(he)
IB plot base R version
Description
Choice of base R, ggplot2, plotly
Usage
ib_plot_base(he, comparison, wtp, bw, n, xlim)
ib_plot_ggplot(he, comparison, wtp, bw, n, xlim)
ib_plot_plotly(he, comparison, wtp, bw, n, xlim)
Arguments
he |
A |
comparison |
Comparison intervention |
wtp |
Willingness to pay |
bw |
band width |
n |
Number |
xlim |
x-axis limits |
Information-Rank Plot for bcea Class
Description
Produces a plot similar to a tornado plot, but based on the analysis of the EVPPI. For each parameter and value of the willingness-to-pay threshold, a barchart is plotted to describe the ratio of EVPPI (specific to that parameter) to EVPI. This represents the relative ‘importance’ of each parameter in terms of the expected value of information.
Usage
## S3 method for class 'bcea'
info.rank(
he,
inp,
wtp = NULL,
howManyPars = NA,
graph = c("base", "ggplot2", "plotly"),
rel = TRUE,
...
)
info.rank(he, ...)
Arguments
he |
A |
inp |
Named list from running
|
wtp |
A value of the wtp for which the analysis should be performed. If not specified then the break-even point for the current model will be used. |
howManyPars |
Optional maximum number of parameters to be included in the bar plot. Includes all parameters by default. |
graph |
A string used to select the graphical engine to use for plotting. Should (partial-)match one of the two options "base" or "plotly". Default value is "base" |
rel |
Logical argument that specifies whether the ratio of
EVPPI to EVPI ( |
... |
Additional options. These include graphical parameters that the user can specify:
|
Value
With base graphics: A data.frame containing the ranking of the parameters with the value of the selected summary, for the chosen wtp; with plotly: a plotly object, incorporating in the $rank element the data.frame as above. The function produces a 'Info-rank' plot. This is an extension of standard 'Tornado plots' and presents a ranking of the model parameters in terms of their impact on the expected value of information. For each parameter, the specific individual EVPPI is computed and used to measure the impact of uncertainty in that parameter over the decision-making process, in terms of how large the expected value of gaining more information is.
Author(s)
Anna Heath, Gianluca Baio, Andrea Berardi
References
Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis in health economics.” Stat. Methods Med. Res., 1–20. ISSN 1477-0334, doi:10.1177/0962280211419832, https://pubmed.ncbi.nlm.nih.gov/21930515/.
Baio G (2013). Bayesian Methods in Health Economics. CRC.
See Also
Examples
## Not run:
# Load the post-processed results of the MCMC simulation model
# original JAGS output is can be downloaded from here
# https://gianluca.statistica.it/books/bcea/code/vaccine.RData
data("Vaccine")
m <- bcea(eff, cost)
inp <- createInputs(vaccine_mat)
info.rank(m, inp)
info.rank(m, inp, graph = "base")
info.rank(m, inp, graph = "plotly")
info.rank(m, inp, graph = "ggplot2")
## End(Not run)
Info Rank Plot By Graph Device
Description
Choice of base R, ggplot2 and plotly.
Usage
info_rank_base(he, params)
info_rank_ggplot(he, params)
info_rank_plotly(params)
Arguments
he |
A |
params |
Graph Parameters including data |
Prepare Info Rank plot parameters
Description
Prepare Info Rank plot parameters
Usage
inforank_params(he, inp, wtp = NULL, rel, howManyPars, extra_args)
Arguments
he |
A |
inp |
Inputs |
wtp |
Willingness to pay |
rel |
Relative size |
howManyPars |
How mnay parameters to use? |
extra_args |
Additional arguments |
Check bcea Class
Description
Check bcea Class
Usage
is.bcea(he)
Arguments
he |
A |
Value
is.bcea returns TRUE or FALSE depending on whether its argument
is a bcea class object.
Reports whether x is a rel object Copied from ggplot2
Description
Reports whether x is a rel object Copied from ggplot2
Usage
is.rel(x)
Arguments
x |
An object to test |
Prepare K-star vertical lines
Description
Prepare K-star vertical lines
Usage
kstar_vlines(he, plot_params)
Arguments
he |
A |
plot_params |
Plots parameters |
Create Labels for Plot
Description
Create Labels for Plot
Swapped labels so that reference is second
Usage
line_labels(he, ...)
## Default S3 method:
line_labels(he, ref_first = TRUE, ...)
## S3 method for class 'pairwise'
line_labels(he, ...)
Arguments
he |
A |
... |
Additional arguments |
Leave-one-out ranking
Description
Leave-one-out ranking
Usage
loo_rank(params)
Arguments
params |
Parameters |
Make Report
Description
Constructs the automated report from the output of the BCEA.
Usage
make.report(he, evppi = NULL, ext = "pdf", echo = FALSE, ...)
Arguments
he |
A |
evppi |
An object obtained as output to a call to |
ext |
A string of text to indicate the extension of the
resulting output file. Possible options are |
echo |
A string (default to |
... |
Additional parameters. For example, the user can specify the
value of the willingness to pay |
Author(s)
Gianluca Baio
References
Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis in health economics.” Stat. Methods Med. Res., 1–20. ISSN 1477-0334, doi:10.1177/0962280211419832, https://pubmed.ncbi.nlm.nih.gov/21930515/.
Baio G (2013). Bayesian Methods in Health Economics. CRC.
See Also
Examples
## Not run:
data(Vaccine, package = "BCEA")
m <- bcea(eff, cost, ref = 2)
make.report(m)
## End(Not run)
Legend Positioning
Description
Legend Positioning
Usage
make_legend_plotly(pos_legend)
Arguments
pos_legend |
Position of legend |
Value
String
Plots the probability that each intervention is the most cost-effective
Description
This function is deprecated. Use ceac.plot() instead.
Plots the probability that each of the n_int interventions being analysed is
the most cost-effective.
Usage
mce.plot(mce, pos = c(1, 0.5), graph = c("base", "ggplot2"), ...)
Arguments
mce |
The output of the call to the function |
pos |
Parameter to set the position of the legend. Can be given in form
of a string |
graph |
A string used to select the graphical engine to use for
plotting. Should (partial-)match the two options |
... |
Optional arguments. For example, it is possible to specify the
colours to be used in the plot. This is done in a vector
|
Value
mceplot |
A ggplot object containing the plot. Returned only
if |
Author(s)
Gianluca Baio, Andrea Berardi
References
Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis in health economics.” Stat. Methods Med. Res., 1–20. ISSN 1477-0334, doi:10.1177/0962280211419832, https://pubmed.ncbi.nlm.nih.gov/21930515/.
Baio G (2013). Bayesian Methods in Health Economics. CRC.
See Also
Examples
# See Baio G., Dawid A.P. (2011) for a detailed description of the
# Bayesian model and economic problem
## Not run:
# Load the processed results of the MCMC simulation model
data(Vaccine)
#
# Runs the health economic evaluation using BCEA
m <- bcea(e=eff, c=cost, # defines the variables of
# effectiveness and cost
ref=2, # selects the 2nd row of (e,c)
# as containing the reference intervention
interventions=treats, # defines the labels to be associated
# with each intervention
Kmax=50000, # maximum value possible for the willingness
# to pay threshold; implies that k is chosen
# in a grid from the interval (0,Kmax)
plot=FALSE # inhibits graphical output
)
#
mce <- multi.ce(m) # uses the results of the economic analysis
#
mce.plot(mce, # plots the probability of being most cost-effective
graph="base") # using base graphics
#
if(require(ggplot2)){
mce.plot(mce, # the same plot
graph="ggplot2") # using ggplot2 instead
}
## End(Not run)
Cost-Effectiveness Analysis When Multiple (Possibly Non-Cost-Effective) Interventions are Present on the Market
Description
Runs the cost-effectiveness analysis, but accounts for the fact that more than one intervention is present on the market.
Usage
mixedAn(he) <- value
Arguments
he |
A |
value |
A vector of market shares associated with the interventions. Its size is the same as the number of possible comparators. By default, assumes uniform distribution for each intervention. |
Value
Creates an object in the class mixedAn, a subclass of bcea
which contains the results of the health economic evaluation in the mixed analysis case:
Ubar |
An array with the simulations of the ”known-distribution” mixed utilities, for each value of the discrete grid approximation of the willingness to pay parameter |
OL.star |
An array with the simulations of the distribution of the Opportunity Loss for the mixed strategy, for each value of the discrete grid approximation of the willingness to pay parameter |
evi.star |
The Expected Value of Information for the mixed strategy, for each value of the discrete grid approximation of the willingness to pay parameter |
mkt.shares |
The vector of market shares associated with each available intervention |
Author(s)
Gianluca Baio
References
Baio G, Russo P (2009). “A decision-theoretic framework for the application of cost-effectiveness analysis in regulatory processes.” Pharmacoeconomics, 27(8), 5–16. ISSN 20356137, doi:10.1007/bf03320526.
Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis in health economics.” Stat. Methods Med. Res., 1–20. ISSN 1477-0334, doi:10.1177/0962280211419832, https://pubmed.ncbi.nlm.nih.gov/21930515/.
Baio G (2013). Bayesian Methods in Health Economics. CRC.
See Also
Examples
# See Baio G., Dawid A.P. (2011) for a detailed description of the
# Bayesian model and economic problem
# Load the processed results of the MCMC simulation model
data(Vaccine)
# Runs the health economic evaluation using BCEA
m <- bcea(e=eff, c=cost, # defines the variables of
# effectiveness and cost
ref=2, # selects the 2nd row of (e, c)
# as containing the reference intervention
interventions=treats, # defines the labels to be associated
# with each intervention
Kmax=50000, # maximum value possible for the willingness
# to pay threshold; implies that k is chosen
# in a grid from the interval (0, Kmax)
plot=FALSE) # inhibits graphical output
mixedAn(m) <- NULL # uses the results of the mixed strategy
# analysis (a "mixedAn" object)
# the vector of market shares can be defined
# externally. If NULL, then each of the T
# interventions will have 1/T market share
# produces the plots
evi.plot(m)
Cost-effectiveness Analysis With Multiple Comparison
Description
Computes and plots the probability that each of the n_int interventions
being analysed is the most cost-effective and the cost-effectiveness
acceptability frontier.
Usage
## S3 method for class 'bcea'
multi.ce(he)
Arguments
he |
A |
Value
Original bcea object (list) of class "pairwise" with additional:
p_best_interv |
A matrix including the probability that each intervention is the most cost-effective for all values of the willingness to pay parameter |
ceaf |
A vector containing the cost-effectiveness acceptability frontier |
Author(s)
Gianluca Baio
See Also
Examples
# See Baio G., Dawid A.P. (2011) for a detailed description of the
# Bayesian model and economic problem
# Load the processed results of the MCMC simulation model
data(Vaccine)
# Runs the health economic evaluation using BCEA
m <- bcea(e=eff, c=cost, # defines the variables of
# effectiveness and cost
ref=2, # selects the 2nd row of (e,c)
# as containing the reference intervention
interventions=treats, # defines the labels to be associated
# with each intervention
Kmax=50000, # maximum value possible for the willingness
# to pay threshold; implies that k is chosen
# in a grid from the interval (0,Kmax)
plot=FALSE # inhibits graphical output
)
mce <- multi.ce(m) # uses the results of the economic analysis
ceac.plot(mce)
ceaf.plot(mce)
Plot Multiple bcea Graphs
Description
Arrange plots in grid. Sourced from R graphics cookbook.
Usage
multiplot(plotlist = NULL, cols = 1, layout_config = NULL)
Arguments
plotlist |
List of ggplot objects |
cols |
Number of columns |
layout_config |
Matrix of plot configuration |
Value
ggplot TableGrob object
Constructor for bcea
Description
Constructor for bcea
Usage
new_bcea(df_ce, k)
Arguments
df_ce |
Dataframe of all simulation eff and cost |
k |
Vector of willingness to pay values |
Value
List object of class bcea.
See Also
Get number of lines
Description
Get number of lines
Usage
num_lines(dat)
## S3 method for class 'pairwise'
num_lines(dat)
## S3 method for class 'bcea'
num_lines(dat)
## S3 method for class 'evppi'
num_lines(dat)
## Default S3 method:
num_lines(dat)
Arguments
dat |
Data |
Automatically open pdf output using default pdf viewer
Description
Automatically open pdf output using default pdf viewer
Usage
openPDF(file_name)
Arguments
file_name |
String file names for pdf |
Plots EIB and EVPI for the Risk Aversion Case
Description
Summary plot of the health economic analysis when risk aversion is included.
Usage
## S3 method for class 'CEriskav'
plot(x, pos = "topright", graph = c("base", "ggplot2", "plotly"), ...)
Arguments
x |
An object of the class |
pos |
Parameter to set the position of the legend (only relevant for
multiple interventions, ie more than 2 interventions being compared).
Can be given in form
of a string |
graph |
A string used to select the graphical engine to use for
plotting. Should (partial-)match the options |
... |
Arguments to be passed to methods, such as graphical parameters
(see |
Details
Plots the Expected Incremental Benefit and the Expected Value of Perfect Information when risk aversion is included in the utility function.
Value
list(eib, evi) |
A two-elements named list of the ggplot objects
containing the requested plots. Returned only if |
The function produces two plots for the risk aversion analysis. The first
one is the EIB as a function of the discrete grid approximation of the
willingness parameter for each of the possible values of the risk aversion
parameter, r. The second one is a similar plot for the EVPI.
Author(s)
Gianluca Baio, Andrea Berardi
References
Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis in health economics.” Stat. Methods Med. Res., 1–20. ISSN 1477-0334, doi:10.1177/0962280211419832, https://pubmed.ncbi.nlm.nih.gov/21930515/.
Baio G (2013). Bayesian Methods in Health Economics. CRC.
See Also
Examples
# See Baio G., Dawid A.P. (2011) for a detailed description of the
# Bayesian model and economic problem
# Load the processed results of the MCMC simulation model
data(Vaccine)
# Runs the health economic evaluation using BCEA
m <- bcea(e=eff, c=cost, # defines the variables of
# effectiveness and cost
ref=2, # selects the 2nd row of (e,c)
# as containing the reference intervention
interventions=treats, # defines the labels to be associated
# with each intervention
Kmax=50000, # maximum value possible for the willingness
# to pay threshold; implies that k is chosen
# in a grid from the interval (0,Kmax)
plot=FALSE # inhibits graphical output
)
# Define the vector of values for the risk aversion parameter, r, e.g.:
r <- c(1e-10, 0.005, 0.020, 0.035)
# Run the cost-effectiveness analysis accounting for risk aversion
CEriskav(m) <- r
# produce the plots
plot(m)
## Alternative options, using ggplot2
plot(m, graph = "ggplot2")
Summary Plot of the Health Economic Analysis
Description
Plots in a single graph the Cost-Effectiveness plane, the Expected Incremental Benefit, the CEAC and the EVPI.
Usage
## S3 method for class 'bcea'
plot(
x,
comparison = NULL,
wtp = 25000,
pos = FALSE,
graph = c("base", "ggplot2", "plotly"),
...
)
Arguments
x |
A |
comparison |
Selects the comparator, in case of more than two
interventions being analysed. Default as NULL plots all the comparisons
together. Any subset of the possible comparisons can be selected (e.g.,
|
wtp |
The value of the willingness to pay parameter. It is passed to
|
pos |
Parameter to set the position of the legend (only relevant for
multiple interventions, ie more than 2 interventions being compared).
Can be given in form
of a string |
graph |
A string used to select the graphical engine to use for
plotting. Should (partial-)match the options |
... |
Arguments to be passed to the methods |
Details
The default position of the legend for the cost-effectiveness plane
(produced by ceplane.plot()) is set to c(1, 1.025)
overriding its default for pos=FALSE, since multiple ggplot2 plots
are rendered in a slightly different way than single plots.
Value
A plot with four graphical summaries of the health economic evaluation.
Author(s)
Gianluca Baio, Andrea Berardi
References
Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis in health economics.” Stat. Methods Med. Res., 1–20. ISSN 1477-0334, doi:10.1177/0962280211419832, https://pubmed.ncbi.nlm.nih.gov/21930515/.
Baio G (2013). Bayesian Methods in Health Economics. CRC.
See Also
bcea(),
ceplane.plot(),
eib.plot(),
ceac.plot(),
evi.plot()
Examples
# See Baio G., Dawid A.P. (2011) for a detailed description of the
# Bayesian model and economic problem
# Load the processed results of the MCMC simulation model
data(Vaccine)
# Runs the health economic evaluation using BCEA
he <- bcea(
e=eff, c=cost, # defines the variables of
# effectiveness and cost
ref=2, # selects the 2nd row of (e,c)
# as containing the reference intervention
interventions=treats, # defines the labels to be associated
# with each intervention
Kmax=50000, # maximum value possible for the willingness
# to pay threshold; implies that k is chosen
# in a grid from the interval (0,Kmax)
plot=FALSE # does not produce graphical outputs
)
# Plots the summary plots for the "bcea" object m using base graphics
plot(he, graph = "base")
# Plots the same summary plots using ggplot2
if(require(ggplot2)){
plot(he, graph = "ggplot2")
# Example of a customized plot.bcea with ggplot2
plot(he,
graph = "ggplot2", # use ggplot2
ICER_size = 1.5, # extra options modifies the mean point size
text = list(size=rel(1.25)) # modifies the text size
)
}
Plot Expected Value of Partial Information With Respect to a Set of Parameters
Description
Plot Expected Value of Partial Information With Respect to a Set of Parameters
Usage
## S3 method for class 'evppi'
plot(x, pos = c(0, 0.8), graph = c("base", "ggplot2"), col = c(1, 1), ...)
Arguments
x |
An object in the class |
pos |
Parameter to set the position of the legend (only relevant for
multiple interventions, ie more than 2 interventions being compared).
Can be given in form
of a string |
graph |
A string used to select the graphical engine to use for
plotting. Should (partial-) match the two options |
col |
Sets the colour for the lines depicted in the graph. |
... |
Arguments to be passed to methods, such as graphical parameters
(see |
Value
Plot with base R or ggplot2.
Author(s)
Gianluca Baio, Andrea Berardi
References
Baio G (2013). Bayesian Methods in Health Economics. CRC.
See Also
Examples
## Not run:
data(Vaccine, package = "BCEA")
treats <- c("Status quo", "Vaccination")
# Run the health economic evaluation using BCEA
m <- bcea(e.pts, c.pts, ref = 2, interventions = treats)
# Compute the EVPPI for a bunch of parameters
inp <- createInputs(vaccine_mat)
# Compute the EVPPI using INLA/SPDE
if (require("INLA")) {
x0 <- BCEA::evppi(m, c("beta.1." , "beta.2."), input = inp$mat)
plot(x0, pos = c(0,1))
x1 <- BCEA::evppi(m, c(32,48,49), input = inp$mat)
plot(x1, pos = "topright")
plot(x0, col = c("black", "red"), pos = "topright")
plot(x0, col = c(2,3), pos = "bottomright")
plot(x0, pos = c(0,1), graph = "ggplot2")
plot(x1, pos = "top", graph = "ggplot2")
plot(x0, col = c("black", "red"), pos = "right", graph = "ggplot2")
plot(x0, col = c(2,3), size = c(1,2), pos = "bottom", graph = "ggplot2")
plot(x0, graph = "ggplot2", theme = ggplot2::theme_linedraw())
}
if (FALSE)
plot(x0, col = 3, pos = "topright")
# The vector 'col' must have the number of elements for an EVPI
# colour and each of the EVPPI parameters. Forced to black
## End(Not run)
Plot Credible Intervals
Description
Bayesian posterior credible intervals against willingness to pay.
Usage
plot_eib_cri(he, params)
Arguments
he |
A |
params |
Graph parameters |
Prepare CE-plane Parameters
Description
In ggplot format, combine user-supplied parameters with defaults.
Usage
prep_ceplane_params(he, wtp_params, ...)
Arguments
he |
A |
wtp_params |
Willingness-to-pay parameters. This can be a single value or a list. |
... |
Additional arguments |
Value
List of graph parameters
Prepare contour plot parameters Additional to ceplane parameters
Description
Prepare contour plot parameters Additional to ceplane parameters
Usage
prep_contour_params(he, ...)
Arguments
he |
A |
... |
Additional parameters |
Value
A list of parameters
Reshape BCEA object in long format (delta_ce)
Description
Reshape BCEA object in long format (delta_ce)
Usage
prep_delta_ce(he)
Arguments
he |
a BCEA object of interest |
Value
a data.frame, displaying the BCEA object information in long format
Prepare EIB plot parameters
Description
Parameters general to all plotting devices.
Usage
prep_eib_params(he, plot.cri, ...)
Arguments
he |
A |
plot.cri |
Make title including credible interval? Logical |
... |
Additional parameters |
Value
List of graph parameters
Prepare frontier data
Description
Prepare frontier data
Usage
prep_frontier_data(he, threshold = NULL, start.origin = TRUE)
Arguments
he |
A |
threshold |
Cost-effectiveness threshold i.e angle of line. Must be >=0 or NULL. |
start.origin |
Where should the frontier start from? |
Value
List with scatter.data, ceef.points, orig.avg
See Also
ceef.plot
bcea Print Method
Description
bcea Print Method
Usage
## S3 method for class 'bcea'
print(x, digits = getOption("digits"), give.attr = FALSE, no.list = TRUE, ...)
Arguments
x |
A |
digits |
Minimal number of significant digits, see |
give.attr |
Logical; if TRUE (default), show attributes as sub structures. |
no.list |
Logical; if TRUE, no ‘list of ...’ nor the class are printed. |
... |
Potential further arguments. |
Examples
data("Vaccine")
he <- BCEA::bcea(eff, cost)
Quadrant Parameters requires just a single comparison group
Description
Quadrant Parameters requires just a single comparison group
Usage
quadrant_params(he, params)
Allow disabling of the cat messages
Description
Allow disabling of the cat messages
Usage
quiet(x)
Arguments
x |
Object to quietly return |
Choose Graphical Engine
Description
From base R, ggplot2 or plotly.
Usage
select_plot_type(graph)
Arguments
graph |
Type names; string |
Value
Plot ID integer 1:base R; 2:ggplot2; 3:plotly
Set Comparisons Group
Description
One of the alternative way to set (e,c) comparison group. Simply recompute all comparisons and drop unwanted.
Usage
setComparisons(he, comparison)
Arguments
he |
A |
comparison |
Selects the comparator, in case of more than two
interventions being analysed. Default as NULL plots all the comparisons
together. Any subset of the possible comparisons can be selected (e.g.,
|
See Also
Set Comparison Group
Description
One of the alternative way to set (e,c) comparison group.
Usage
setComparisons(he) <- value
## S3 replacement method for class 'bcea'
setComparisons(he) <- value
## Default S3 replacement method:
setComparisons(he) <- value
Arguments
he |
A |
value |
Comparison |
Value
bcea-type object
See Also
Set Maximum Willingness to Pay
Description
Alternative way to define K statistic.
Usage
setKmax(he) <- value
## S3 replacement method for class 'bcea'
setKmax(he) <- value
## Default S3 replacement method:
setKmax(he) <- value
Arguments
he |
A |
value |
Maximum willingness to pay |
Value
bcea-type object
Set Reference Group
Description
Alternative way to define (e,c) reference group.
Usage
setReferenceGroup(he) <- value
## S3 replacement method for class 'bcea'
setReferenceGroup(he) <- value
## Default S3 replacement method:
setReferenceGroup(he) <- value
Arguments
he |
A |
value |
Reference group number |
Value
bcea-type object
Table of Simulation Statistics for the Health Economic Model
Description
Using the input in the form of MCMC simulations and after having run the health economic model, produces a summary table of the simulations from the cost-effectiveness analysis.
Usage
sim_table(he, ...)
## S3 method for class 'bcea'
sim_table(he, wtp = 25000, ...)
Arguments
he |
A |
... |
Additional arguments |
wtp |
The value of the willingness to pay threshold to be used in the summary table. |
Value
Produces the following elements:
table |
A table with simulation statistics from the economic model |
names.cols |
A vector of labels to be associated with each column of the table |
wtp |
The selected value of the willingness to pay |
idx_wtp |
The index associated with the selected value of the willingness to pay threshold in the grid used to run the analysis |
Author(s)
Gianluca Baio
References
Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis in health economics.” Stat. Methods Med. Res., 1–20. ISSN 1477-0334, doi:10.1177/0962280211419832, https://pubmed.ncbi.nlm.nih.gov/21930515/.
Baio G (2013). Bayesian Methods in Health Economics. CRC.
See Also
Examples
# See Baio G., Dawid A.P. (2011) for a detailed description of the
# Bayesian model and economic problem
# Load the processed results of the MCMC simulation model
data(Vaccine)
# Runs the health economic evaluation using BCEA
m <- bcea(e=eff, # defines the variables of
c=cost, # effectiveness and cost
ref=2, # selects the 2nd row of (e, c)
# as containing the reference intervention
interventions=treats, # defines the labels to be associated
# with each intervention
Kmax=50000) # maximum value possible for the willingness
# to pay threshold; implies that k is chosen
# in a grid from the interval (0, Kmax)
# Now can save the simulation exercise in an object using sim_table()
sim_table(m, # uses the results of the economic evaluation
wtp=25000) # selects the particular value for k
Statins Evidence-Synthesis Data (Robust Model)
Description
This data set contains the results of a Bayesian analysis used to model the effectiveness of various statins. The analysis uses robust Half-Cauchy priors for the structured effects standard deviations.
Format
A BUGS object containing the simulations for the evidence synthesis model.
Source
A systematic review and economic evaluation of statins for the prevention of coronary events. Ward 2007.
References
Baio G. (2012). Bayesian Methods in Health Economics. CRC/Chapman & Hall, London.
Examples
data(statins_HC)
lapply(statins_HC$sims.list, summary)
Statins Evidence-Synthesis Data (Base Model)
Description
This data set contains the results of a Bayesian analysis used to model the effectiveness of various statins. The analysis is based on the simplest model, using vague priors.
Format
A BUGS object containing the simulations for the evidence synthesis model.
Source
A systematic review and economic evaluation of statins for the prevention of coronary events. Ward 2007.
References
Baio G. (2012). Bayesian Methods in Health Economics. CRC/Chapman & Hall, London.
Examples
data(statins_base)
lapply(statins_base$sims.list, summary)
Structural Probability Sensitivity Analysis
Description
Computes the weights to be associated with a set of competing models in order to perform structural PSA.
Usage
struct.psa(
models,
effect,
cost,
ref = NULL,
interventions = NULL,
Kmax = 50000,
plot = FALSE,
w = NULL
)
Arguments
models |
A (possibly named) list containing the output from either R2jags or R2WinBUGS for all the models that need to be combined in the model average |
effect |
A list containing the measure of effectiveness computed from
the various models (one matrix with |
cost |
A list containing the measure of costs computed from the various
models (one matrix with |
ref |
Which intervention is considered to be the reference
strategy. The default value |
interventions |
Defines the labels to be associated with each
intervention. By default and if |
Kmax |
Maximum value of the willingness to pay to be considered.
Default value is |
plot |
A logical value indicating whether the function should produce the summary plot or not |
w |
A vector of weights. By default it's |
Details
The model is a list containing the output from either R2jags or
R2WinBUGS for all the models that need to be combined in the model average
effect is a list containing the measure of effectiveness computed from the
various models (one matrix with n_sim x n_ints simulations for each model)
cost is a list containing the measure of costs computed from the various
models (one matrix with n_sim x n_ints simulations for each model).
Value
List object of bcea object, model weights and DIC
Author(s)
Gianluca Baio
References
Baio G (2013). Bayesian Methods in Health Economics. CRC.
See Also
Examples
# load sample jags output
data("statins_base")
data("statins_HC")
interventions <- c("Atorvastatin", "Fluvastatin",
"Lovastatin", "Pravastatin",
"Rosuvastatin", "Simvastatin")
m1 <- bcea(eff = statins_base$sims.list$effect,
cost = statins_base$sims.list$cost.tot,
ref = 1, interventions = interventions)
m2 <- bcea(eff = statins_HC$sims.list$effect,
cost = statins_HC$sims.list$cost.tot,
ref = 1, interventions = interventions)
models <- list("Base"=statins_base, "Half Cauchy"=statins_HC)
effects <- list(statins_base$sims.list$effect,
statins_HC$sims.list$effect)
costs <- list(statins_base$sims.list$cost.tot,
statins_HC$sims.list$cost.tot)
## Not run:
m3 <- struct.psa(models, effects, costs,
ref = 1, interventions = interventions)
## End(Not run)
Summary Method for Objects of Class bcea
Description
Produces a table printout with some summary results of the health economic evaluation.
Usage
## S3 method for class 'bcea'
summary(object, wtp = 25000, ...)
Arguments
object |
A |
wtp |
The value of the willingness to pay threshold used in the summary table. |
... |
Additional arguments affecting the summary produced. |
Value
Prints a summary table with some information on the health economic output and synthetic information on the economic measures (EIB, CEAC, EVPI).
Author(s)
Gianluca Baio
References
Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis in health economics.” Stat. Methods Med. Res., 1–20. ISSN 1477-0334, doi:10.1177/0962280211419832, https://pubmed.ncbi.nlm.nih.gov/21930515/.
Baio G (2013). Bayesian Methods in Health Economics. CRC.
See Also
Examples
data(Vaccine)
he <- bcea(eff, cost, interventions = treats, ref = 2)
summary(he)
Summary Methods For Objects in the Class mixedAn (Mixed Analysis)
Description
Prints a summary table for the results of the mixed analysis for the economic evaluation of a given model.
Usage
## S3 method for class 'mixedAn'
summary(object, wtp = 25000, ...)
Arguments
object |
An object of the class |
wtp |
The value of the willingness to pay chosen to present the analysis. |
... |
Additional arguments affecting the summary produced. |
Value
Produces a table with summary information on the loss in expected value of information generated by the inclusion of non cost-effective interventions in the market.
Author(s)
Gianluca Baio
References
Baio G, Russo P (2009). “A decision-theoretic framework for the application of cost-effectiveness analysis in regulatory processes.” Pharmacoeconomics, 27(8), 5–16. ISSN 20356137, doi:10.1007/bf03320526.
Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis in health economics.” Stat. Methods Med. Res., 1–20. ISSN 1477-0334, doi:10.1177/0962280211419832, https://pubmed.ncbi.nlm.nih.gov/21930515/.
Baio G (2013). Bayesian Methods in Health Economics. CRC.
See Also
Examples
# See Baio G., Dawid A.P. (2011) for a detailed description of the
# Bayesian model and economic problem
# Load the processed results of the MCMC simulation model
data(Vaccine)
# Runs the health economic evaluation using BCEA
m <- bcea(e=eff, c=cost, # defines the variables of
# effectiveness and cost
ref=2, # selects the 2nd row of (e,c)
# as containing the reference intervention
interventions=treats, # defines the labels to be associated
# with each intervention
Kmax=50000 # maximum value possible for the willingness
# to pay threshold; implies that k is chosen
# in a grid from the interval (0,Kmax)
)
mixedAn(m) <- NULL # uses the results of the mixed strategy
# analysis (a "mixedAn" object)
# the vector of market shares can be defined
# externally. If NULL, then each of the T
# interventions will have 1/T market share
# Prints a summary of the results
summary(m, # uses the results of the mixed strategy analysis
wtp=25000) # (a "mixedAn" object)
# selects the relevant willingness to pay
# (default: 25,000)
Summary Method for Objects of Class pairwise
Description
Produces a table printout with some summary results of the health economic evaluation.
Usage
## S3 method for class 'pairwise'
summary(object, wtp = 25000, ...)
Arguments
object |
A |
wtp |
The value of the willingness to pay threshold used in the summary table. |
... |
Additional arguments affecting the summary produced. |
Value
Prints a summary table with some information on the health economic output and synthetic information on the economic measures (EIB, CEAC, EVPI).
Author(s)
Gianluca Baio
References
Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis in health economics.” Stat. Methods Med. Res., 1–20. ISSN 1477-0334, doi:10.1177/0962280211419832, https://pubmed.ncbi.nlm.nih.gov/21930515/.
Baio G (2013). Bayesian Methods in Health Economics. CRC.
See Also
Examples
data(Vaccine)
he <- bcea(eff, cost, interventions = treats, ref = 2)
he_multi <- multi.ce(he)
summary(he_multi)
Calculate Dataset For ICERs From bcea Object
Description
Calculate Dataset For ICERs From bcea Object
Usage
tabulate_means(he, comp_label = NULL, ...)
Arguments
he |
A |
comp_label |
Optional vector of strings with comparison labels |
... |
Additional arguments |
Value
A data.frame object including mean outcomes, comparison identifier, comparison label and associated ICER
Examples
data("Smoking")
he <- BCEA::bcea(eff, cost)
tabulate_means(he)
bcea theme ggplot2
Description
bcea theme ggplot2
Usage
theme_default()
theme_ceac()
theme_ceplane()
theme_eib()
theme_contour()
Validate bcea
Description
Validate bcea
Usage
validate_bcea(eff, cost, ref, interventions)
Arguments
eff |
Effectiveness matrix |
cost |
Cost matrix |
ref |
Reference intervention |
interventions |
All interventions |
Validate EIB parameters
Description
Validate EIB parameters
Usage
validate_eib_params(params)
Arguments
params |
Graph parameters |
Value
List of graph parameters