RhodesEtAlPrior {bayesmeta}  R Documentation 
Heterogeneity priors for continuous outcomes (standardized mean differences) as proposed by Rhodes et al. (2015).
Description
Use the prior specifications proposed in the paper by Rhodes et al., based on an analysis of studies using standardized mean differences (SMD) that were published in the Cochrane Database of Systematic Reviews.
Usage
RhodesEtAlPrior(outcome=c(NA, "obstetric outcome",
"resource use and hospital stay / process",
"internal and external structurerelated outcome",
"general physical health and adverse event and pain and quality of life / functioning",
paste("signs / symptoms reflecting continuation / end of condition and infection",
"/ onset of new acute / chronic disease"),
"mental health outcome", "biological marker", "various subjectively measured outcomes"),
comparator1=c("pharmacological", "nonpharmacological", "placebo / control"),
comparator2=c("pharmacological", "nonpharmacological", "placebo / control"),
area=c("other", "respiratory", "cancer"))
Arguments
outcome 
The type of outcome investigated (see below for a list
of possible values). The default ( 
comparator1 
One comparator's type. 
comparator2 
The other comparator's type. 
area 
The medical area. 
Details
Rhodes et al. conducted an analysis of studies listed in the
Cochrane Database of Systematic Reviews that were investigating
standardized mean differences (SMD) as endpoints. As a result, they
proposed empirically motivated logStudentt
prior distributions
for the (squared!) heterogeneity parameter \tau^2
, depending on
the particular type of outcome investigated and the type of comparison
in question. The underlying t
distribution's location and scale
parameters here are internally stored in a 3dimensional array (named
RhodesEtAlParameters
) and are most conveniently accessed using
the RhodesEtAlPrior()
function.
The outcome
argument specifies the type of outcome
investigated. It may take one of the following values
(partial matching is supported):

NA

"obstetric outcomes"

"resource use and hospital stay / process"

"internal and external structurerelated outcome"

"general physical health and adverse event and pain and quality of life / functioning"

"signs / symptoms reflecting continuation / end of condition and infection / onset of new acute / chronic disease"

"mental health outcome"

"biological marker"

"various subjectively measured outcomes"
.
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"
.
The area
argument specifies the medical context; possible
values are:

"respiratory"

"cancer"

"other"
(the default).
Note that the location and scale parameters refer to the
logarithmic (squared) heterogeneity parameter \tau^2
,
which is modelled using a Studentt
distribution with 5 degrees
of freedom. When you want to use the prior specifications for
\tau
, 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
will still be logStudentt distributed, but with halved location and
scale parameters. The relevant transformations are already taken care
of when using the resulting $dprior()
, $pprior()
and
$qprior()
functions; see also the example below.
Value
a list with elements
parameters 
the location and scale parameters (corresponding to the
logarithmic squared heterogeneity parameter 
outcome.type 
the corresponding type of outcome. 
comparison.type 
the corresponding type of comparison. 
medical.area 
the medical context. 
dprior 
a 
pprior 
a 
qprior 
a 
Author(s)
Christian Roever christian.roever@med.unigoettingen.de
References
K.M. Rhodes, R.M. Turner, J.P.T. Higgins. Predictive distributions were developed for the extent of heterogeneity in metaanalyses of continuous outcome data. Journal of Clinical Epidemiology, 68(1):5260, 2015. doi:10.1016/j.jclinepi.2014.08.012.
C. Roever, R. Bender, S. Dias, C.H. Schmid, H. Schmidli, S. Sturtz, S. Weber, T. Friede. On weakly informative prior distributions for the heterogeneity parameter in Bayesian randomeffects metaanalysis. Research Synthesis Methods, 12(4):448474, 2021. doi:10.1002/jrsm.1475.
See Also
Examples
# determine prior distribution for a specific setting:
RP < RhodesEtAlPrior("obstetric", "pharma", "placebo")
print(RP$parameters)
str(RP)
# a prior 95 percent interval for tau:
RP$qprior(c(0.025,0.975))
# the general (marginal) setting:
RP < RhodesEtAlPrior()
print(RP$parameters)
str(RP)
# a prior 95 percent interval for tau:
RP$qprior(c(0.025,0.975))
## Not run:
# load "metafor" package:
require("metafor")
# load data:
data("dat.normand1999")
# compute effect sizes (standardized mean differences):
es < escalc(measure="SMD", m1i=m1i, sd1i=sd1i, n1i=n1i,
m2i=m2i, sd2i=sd2i, n2i=n2i,
slab=source, data=dat.normand1999)
# derive appropriate prior:
RP < RhodesEtAlPrior("resource use", "nonpharma", "nonpharma")
# show (central) prior 95 percent interval:
RP$qprior(c(0.025, 0.975))
# show prior 95 percent upper limit:
RP$qprior(0.95)
# perform meta analysis:
bma < bayesmeta(es, tau.prior=RP$dprior)
# show results:
print(bma)
plot(bma, which=4, prior=TRUE)
## End(Not run)