BCEA (version 2.2-2)

evppi: 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-parameteric regression for single parameter EVPPI and the SPDE-INLA method for larger parameter subsets.

Usage

evppi(parameter,input,he,N=NA,plot=F,residuals=T,...)
## S3 method for class 'default':
evppi(parameter,input,he,N=NA,plot=F,residuals=T,...)

Arguments

parameter

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.

he

A bcea object (the result of the call to the function bcea).

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 F.

residuals

A logical value indicating whether the fitted values for the SPDE-INLA method should be outputted. Default set to T.

...

Additional arguments. The default methods to compute the EVPPI are: - For single-parameter: GAM regression. - For multi-parameter: INLA/SPDE. However, it is possible (mainly for backward compatibility) to use different methods. For single-parameter, the

Value

• evppi
  The computed values of evppi for all values of the parameter of willingness to pay
• index
  A numerical vector with the index associated with the parameters for which the EVPPI was calculated
• k
  The vector of values for the willingness to pay
• evi
  The 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 the EVPPI
• time
  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

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)

References

Strong M., Oakley J. and Brennan A. (2014). Estimating multi-parameter partial Expected Value of Perfect Information from a probabilistic sensitivity analysis sample: a non-parametric regression approach, Medical Decision Making.

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 in Health

Baio G. (2012). Bayesian Methods in Health Economics. CRC/Chapman Hall, London

Heath A., Manolopoulou I., Baio G. (2015). Efficient High-Dimensional Gaussian Process Regression to calculate the Expected Value of Partial Perfect Information in Health Economic Evaluations. http://arxiv.org/pdf/1504.05436v1.pdf

See Also

plot.evppi, bcea

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,c,ref=2,interventions=treats)
#
# Computes the EVPPI for a bunch of parameters
inp <- CreateInputs(vaccine)
# Computes the EVPPI using INLA/SPDE
# x0 <- evppi(parameter=c(38:40),input=inp$mat,he=m)
# Now uses GAM regression
# x1 <- evppi(parameter=c(38:40),input=inp$mat,he=m,method="GAM")
# Now uses the GP regression
# x2 <- evppi(parameter=c(38:40),input=inp$mat,he=m,method="GP")
# Now plots the results
# plot(x0)
# points(x0$k,x0$evppi,lwd=2,lty=2,t="l")
# points(x1$k,x1$evppi,t="l",col="red")
# points(x2$k,x2$evppi,t="l",col="blue")