evppi(parameter,input,he,N=NULL,plot=F,residuals=T,...)
"evppi"(parameter,input,he,N=NULL,plot=F,residuals=T,...)bcea object (the result of the call to the function bcea).
method which takes as value a string "sad" in the former case
and a string "so" in the latter. In case "sal" is selected, then it is possible
to also specify the number of "separators" (e.g. n.seps=3). If none is specified,
the default value n.seps=1 is used. If "so" is used as method for the
calculation of the EVPPI, then the user *needs* to also specify the number of "blocks"
(e.g. n.blocks=20).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.
The second possible method is the GP regression derived by Strong et al. 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.
Finally, it is also possible to specify some INLA-related options. These are all rather
technical and are described in detail in Baio, Berardi and Heath.
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 greatercomputational time). The construction of the boundaries for
the mesh can becontrolled 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.
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. (2016). Estimating the Expected Value of Partial Perfect Information in Health Economic Evaluations using Integrated Nested Laplace Approximation. Statistics in Medicine. http://onlinelibrary.wiley.com/doi/10.1002/sim.6983/full
plot.evppi, bcea
# 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")
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