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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.


# S3 method for bcea
  wtp = NULL,
  howManyPars = NA,
  graph = c("base", "ggplot2", "plotly"),
  rel = TRUE,

info.rank(he, ...)



A bcea object containing the results of the Bayesian modelling and the economic evaluation.


Named list from running createInputs() containing:

  • parameter = A vector of parameters for which the individual 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.

  • mat = 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.


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.


Optional maximum number of parameters to be included in the bar plot. Includes all parameters by default.


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"


Logical argument that specifies whether the ratio of EVPPI to EVPI (rel = TRUE, default) or the absolute value of the EVPPI should be used for the analysis.


Additional options. These include graphical parameters that the user can specify:

  • xlim = limits of the x-axis; ca = font size for the axis label (default = 0.7 of full size).

  • cn = font size for the parameter names vector (default = 0.7 of full size) - base graphics only.

  • mai = margins of the graph (default = c(1.36, 1.5, 1,1)) - base graphics only.


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.


Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis in health economics.” Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 ,

Baio G (2013). Bayesian Methods in Health Economics. CRC.

See also


Anna Heath, Gianluca Baio, Andrea Berardi


if (FALSE) {
# Load the post-processed results of the MCMC simulation model
# original JAGS output is can be downloaded from here

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")