Description Usage Arguments Details Value Author(s) References See Also Examples
Use the prior specifications proposed in the paper by Turner et al., based on an analysis of studies using binary endpoints that were published in the Cochrane Database of Systematic Reviews.
1 2 3 4 5 6 7 8 9 10 11  TurnerEtAlPrior(outcome=c(NA, "allcause mortality", "obstetric outcomes",
"causespecific mortality / major morbidity event / composite (mortality or morbidity)",
"resource use / hospital stay / process", "surgical / device related success / failure",
"withdrawals / dropouts", "internal / structurerelated outcomes",
"general physical health indicators", "adverse events",
"infection / onset of new disease",
"signs / symptoms reflecting continuation / end of condition", "pain",
"quality of life / functioning (dichotomized)", "mental health indicators",
"biological markers (dichotomized)", "subjective outcomes (various)"),
comparator1=c("pharmacological", "nonpharmacological", "placebo / control"),
comparator2=c("pharmacological", "nonpharmacological", "placebo / control"))

outcome 
The type of outcome investigated (see below for a list of possible values). 
comparator1 
One comparator's type. 
comparator2 
The other comparator's type. 
Turner et al. conducted an analysis of studies listed in the
Cochrane Database of Systematic Reviews that were investigating
binary endpoints. As a result, they proposed empirically motivated
lognormal prior distributions for the (squared!) heterogeneity
parameter τ^2, depending on the particular type of outcome
investigated and the type of comparison in question. The lognormal
parameters (μ and σ) here are internally stored in
a 3dimensional array (named TurnerEtAlParameters
) and are most
conveniently accessed using the TurnerEtAlPrior()
function.
The outcome
argument specifies the type of outcome
investigated. It may take one of the following values
(partial matching is supported):
NA
"allcause mortality"
"obstetric outcomes"
"causespecific mortality / major morbidity event / composite (mortality or morbidity)"
"resource use / hospital stay / process"
"surgical / device related success / failure"
"withdrawals / dropouts"
"internal / structurerelated outcomes"
"general physical health indicators"
"adverse events"
"infection / onset of new disease"
"signs / symptoms reflecting continuation / end of condition"
"pain"
"quality of life / functioning (dichotomized)"
"mental health indicators"
"biological markers (dichotomized)"
"subjective outcomes (various)"
Specifying “outcome=NA
” (the default) yields the
marginal setting, without considering metaanalysis
characteristics as covariates.
The comparator1
and comparator2
arguments together
specify the type of comparison in question. These may take one of the
following values (partial matching is supported):
"pharmacological"
"nonpharmacological"
"placebo / control"
Any combination is allowed for the comparator1
and
comparator2
arguments, as long as not both arguments are set to
"placebo / control"
.
Note that the lognormal prior parameters refer to the
(squared) heterogeneity parameter τ^2. When you want
to use the prior specifications for τ, the square root,
as the parameter (as is necessary when using the bayesmeta()
function), you need to correct for the square root
transformation. Taking the square root is equivalent to dividing by
two on the logscale, so the square root's distribution will still be
lognormal, but with halved mean and standard deviation. The relevant
transformations are already taken care of when using the resulting
$dprior()
, $pprior()
and $qprior()
functions; see
also the example below.
a list with elements
parameters 
the lognormal parameters (μ and σ, corresponding to the squared heterogeneity parameter τ^2 as well as τ). 
outcome.type 
the corresponding type of outcome. 
comparison.type 
the corresponding type of comparison. 
dprior 
a 
pprior 
a 
qprior 
a 
Christian Roever [email protected]
R.M. Turner, D. Jackson, Y. Wei, S.G. Thompson, J.P.T. Higgins. Predictive distributions for betweenstudy heterogeneity and simple methods for their application in Bayesian metaanalysis. Statistics in Medicine, 34(6):984998, 2015.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52  # load example data:
data("CrinsEtAl2014")
# determine corresponding prior parameters:
TP < TurnerEtAlPrior("surgical", "pharma", "placebo / control")
print(TP)
# a prior 95 percent interval for tau:
TP$qprior(c(0.025,0.975))
## Not run:
# compute effect sizes (log odds ratios) from count data
# (using "metafor" package's "escalc()" function):
crins.es < escalc(measure="OR",
ai=exp.AR.events, n1i=exp.total,
ci=cont.AR.events, n2i=cont.total,
slab=publication, data=CrinsEtAl2014)
print(crins.es)
# perform meta analysis:
crins.ma01 < bayesmeta(crins.es, tau.prior=TP$dprior)
# for comparison perform analysis using weakly informative Cauchy prior:
crins.ma02 < bayesmeta(crins.es, tau.prior=function(t){dhalfcauchy(t,scale=1)})
# show results:
print(crins.ma01)
print(crins.ma02)
# compare estimates; heterogeneity (tau):
rbind("Turner prior"=crins.ma01$summary[,"tau"], "Cauchy prior"=crins.ma02$summary[,"tau"])
# effect (mu):
rbind("Turner prior"=crins.ma01$summary[,"mu"], "Cauchy prior"=crins.ma02$summary[,"mu"])
# illustrate heterogeneity priors and posteriors:
par(mfcol=c(2,2))
plot(crins.ma01, which=4, prior=TRUE, taulim=c(0,2),
main="informative lognormal prior")
plot(crins.ma02, which=4, prior=TRUE, taulim=c(0,2),
main="weakly informative halfCauchy prior")
plot(crins.ma01, which=3, mulim=c(3,0),
main="informative lognormal prior")
abline(v=0, lty=3)
plot(crins.ma02, which=3, mulim=c(3,0),
main="weakly informative halfCauchy prior")
abline(v=0, lty=3)
par(mfrow=c(1,1))
# compare prior and posterior 95 percent upper limits for tau:
TP$qprior(0.95)
crins.ma01$qposterior(0.95)
qhalfcauchy(0.95)
crins.ma02$qposterior(0.95)
## End(Not run)

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