Description Usage Arguments Details Value Examples
nma_inla
fits a network meta-analysis model using INLA.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | nma_inla(
datINLA,
likelihood = NULL,
fixed.par = c(0, 1000),
tau.prior = "uniform",
tau.par = c(0, 5),
kappa.prior = "uniform",
kappa.par = c(0, 5),
mreg = FALSE,
type = "consistency",
verbose = FALSE,
inla.strategy = "simplified.laplace",
improve.hyperpar.dz = 0.75,
correct = FALSE,
correct.factor = 10,
improve.hyperpar = TRUE
)
|
datINLA |
An object of |
likelihood |
The likelihood to be used. |
fixed.par |
A numerical vector specifying the parameter of the normal prior density for basic parameters, first value is parameter for mean, second is for variance. |
tau.prior |
A string specifying the prior density for the heterogeneity standard deviation, options are 'uniform' for uniform prior and 'half-normal' for half-normal prior. |
tau.par |
A numerical vector specifying the parameter of the prior density for heterogenety stdev.
|
kappa.prior |
A string specifying the prior density for the inconsistency standard deviation, options are 'uniform' for uniform prior and 'half-normal' for half-normal prior. |
kappa.par |
A numerical vector specifying the parameter of the prior.
density for inconsistency stdev. The definition of the priors is the same as for |
mreg |
Logical indicating whether covariate(s) should be incorporated to fit a
network meta-regression model, default |
type |
A string indicating the type of the model, options are "FE", "consistency" and "jackson". |
verbose |
Logical indicating whether the program should run in a verbose model, default |
inla.strategy |
A string specfying the strategy to use for the approximations of INLA;
one of 'gaussian', 'simplified.laplace' (default) or 'laplace', see |
improve.hyperpar.dz |
Step length in the standardized scale used in the construction of the grid, default 0.75,
see |
correct |
Logical Add correction for the Laplace approximation, default |
correct.factor |
Numerical Factor used in adjusting the correction factor if |
improve.hyperpar |
Improve the estimates of the posterior marginals for the hyperparameters
of the model using the grid integration strategy, default |
The following likelihood types are supported
normal
: for continuous (mean difference) data.
Required coloumns: [mean, std.err]
Result: relative mean difference
binomial
: for dichotomous data.
Required coloumns: [responders, sampleSize]
Result: log odds ratio
normal
: for event-rate (survival) data.
Required coloumns: [responders, exposure]
Result: log hazard ratio
The following model types are supported
FE
, ordinary fixed effect model, assuming homogeneity between trials
(Dias et al., 2013)
consistency
, ordinary consistency model, assuming consistency in the
network. (Jackson et al., 2014)
jackson
, the design-by-treatment interaction model with random
inconsistency parameters. (Jackson et al., 2014)
nma_inla
returns a nma_inla
object.
1 2 3 4 5 6 7 8 9 10 11 | SmokdatINLA <- create_INLA_dat(dat = Smokdat, armVars = c('treatment' = 't', 'responders' = 'r'
,'sampleSize' = 'n'), nArmsVar = 'na')
## Not run:
## Fitting a consistency model
if(requireNamespace('INLA', quietly = TRUE)){
require('INLA', quietly = TRUE)
fit.Smok.cons.INLA <- nma_inla(SmokdatINLA, likelihood = 'binomial', type = 'consistency',
tau.prior = 'uniform', tau.par = c(0, 5))
}
## End(Not run)
|
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