Bayesian Cost Effectiveness Analysis
Add Contour Quadrants
Add Contours to Base R Plot
Deprecated functions in package BCEA.
BCEA: Bayesian Cost Effectiveness Analysis
Create Bayesian Cost-Effectiveness Analysis Object
Optimal intervention
Cost-effectiveness summary statistics table
CEAC Matrix Plot
Cost-Effectiveness Acceptability Curve (CEAC) Plot By Graph Device
Cost-Effectiveness Acceptability Curve (CEAC) Plot
Cost-Effectiveness Acceptability Frontier (CEAF) plot
Cost-effectiveness Efficiency Frontier Plot By Graph Device
Cost-Effectiveness Efficiency Frontier (CEEF) Plot
Summary table for CEEF
Extract Separate Parameter Sets
CE-plane ggplot Parameters
Cost-Effectiveness Plane Plot By Graph Device
Cost-effectiveness Plane Plot
Cost-effectiveness Analysis Including a Parameter of Risk Aversion
Cost-effectiveness Plot Including a Parameter of Risk Aversion
Comparison Names From
Compute Cost-Effectiveness Acceptability Curve
Compute Cost-Effectiveness Acceptability Frontier
Calculate Credible Intervals
Compute Expected Incremental Benefit
Compute Expected Value of Information
Compute Incremental Benefit
Compute Incremental Cost-Effectiveness Ratio
Compute k^*
Compute Opportunity Loss
Compute Probability Best Intervention
Compute Probability Optimal Intervention Best
Compute U Statistic
Compute NB for mixture of interventions
Compute Ustar Statistic
Compute Value of Information
Contour ggplot2 Parameters
Contour Cost-Effectiveness Plane
Add contour lines to a plotly contour or CE plane plot
Contour Plots for the Cost-Effectiveness Plane
Specialised CE-plane Contour Plot
Use from Base R to ggplot
Create Inputs for EVPI Calculation
Diagnostic Plots For The Results Of The EVPPI
EIB parameters specific to base R plot
EIB Parameters CrI
Expected Incremental Benefit Plot By Graph Device
Expected Incremental Benefit (EIB) Plot
Expected Value of Information Plot By Graph Device
EVI Plot of the Health Economic Analysis For Mixed Analysis
Expected Value of Information (EVI) Plot
Plot Expected Value of Partial Information With Respect to a Set of Pa...
Q-Q Plot
Residual Plot
Expected Value of Perfect Partial Information (EVPPI) for Selected Par...
Credible interval ggplot geom
Geom Quadrant Text
Get fitted values from evppi object
GrassmannOptim
IB plot base R version
Incremental Benefit (IB) Distribution Plot
Info Rank Plot By Graph Device
Information-Rank Plot for bcea Class
Prepare Info Rank plot parameters
Check bcea Class
Reports whether x is a rel object Copied from ggplot2
Prepare K-star vertical lines
Create Labels for Plot
Leave-one-out ranking
Legend Positioning
Cost-Effectiveness Analysis When Multiple (Possibly Non-Cost-Effective...
Cost-effectiveness Analysis With Multiple Comparison
Plot Multiple bcea Graphs
Constructor for bcea
Get number of lines
Plot Credible Intervals
Summary Plot of the Health Economic Analysis
Plots EIB and EVPI for the Risk Aversion Case
Plot Expected Value of Partial Information With Respect to a Set of Pa...
Prepare CE-plane Parameters
Prepare contour plot parameters Additional to ceplane parameters
Reshape BCEA object in long format (delta_ce)
Prepare EIB plot parameters
Prepare frontier data
bcea Print Method
Quadrant Parameters requires just a single comparison group
Allow disabling of the cat messages
Choose Graphical Engine
Set Comparison Group
Set Comparisons Group
Set Maximum Willingness to Pay
Set Reference Group
Table of Simulation Statistics for the Health Economic Model
Structural Probability Sensitivity Analysis
Summary Method for Objects of Class bcea
Summary Methods For Objects in the Class mixedAn (Mixed Analysis)
Summary Method for Objects of Class pairwise
Calculate Dataset For ICERs From bcea Object
bcea theme ggplot2
Validate bcea
Validate EIB parameters
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>.
Useful links