From 479fbd79113b9c04e95be2c13d0f21cd2f0e8114 Mon Sep 17 00:00:00 2001
From: Christian Roever <christian.roever@med.uni-goettingen.de>
Date: Wed, 16 Mar 2022 16:27:32 +0100
Subject: [PATCH] added 'SchmidliEtAl2017' example
---
DESCRIPTION | 2 +-
data/SchmidliEtAl2017.R | 15 +++++
man/SchmidliEtAl2017.Rd | 128 +++++++++++++++++++++++++++++++++++++++
man/bayesmeta-package.Rd | 2 +-
4 files changed, 145 insertions(+), 2 deletions(-)
create mode 100644 data/SchmidliEtAl2017.R
create mode 100644 man/SchmidliEtAl2017.Rd
diff --git a/DESCRIPTION b/DESCRIPTION
index a1d66fc..47cd485 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -2,7 +2,7 @@ Package: bayesmeta
Type: Package
Title: Bayesian Random-Effects Meta-Analysis and Meta-Regression
Version: 3.1
-Date: 2022-03-14
+Date: 2022-03-16
Authors@R: c(person(given="Christian", family="Roever", role=c("aut","cre"),
email="christian.roever@med.uni-goettingen.de",
comment=c(ORCID="0000-0002-6911-698X")),
diff --git a/data/SchmidliEtAl2017.R b/data/SchmidliEtAl2017.R
new file mode 100644
index 0000000..28c6b0f
--- /dev/null
+++ b/data/SchmidliEtAl2017.R
@@ -0,0 +1,15 @@
+#
+# H. Schmidli, B. Neuenschwander, T. Friede.
+# Meta-analytic-predictive use of historical variance data
+# for the design and analysis of clinical trials.
+# Computational Statistics and Data Analysis, 113:100-110, 2017.
+# https://doi.org/10.1016/j.csda.2016.08.007
+#
+
+# specify data (Table 2):
+SchmidliEtAl2017 <- cbind.data.frame("study" = c("CATT", "CLEAR-IT 2", "HARBOR",
+ "IVAN", "VIEW 1", "VIEW 2"),
+ "N" = c(599, 62, 550, 309, 909, 906),
+ "stdev" = c(12.11, 10.97, 10.94, 9.41, 10.97, 10.95),
+ "df" = c(597, 60, 548, 307, 906, 903),
+ stringsAsFactors=FALSE)
diff --git a/man/SchmidliEtAl2017.Rd b/man/SchmidliEtAl2017.Rd
new file mode 100644
index 0000000..1ccf4e3
--- /dev/null
+++ b/man/SchmidliEtAl2017.Rd
@@ -0,0 +1,128 @@
+\name{SchmidliEtAl2017}
+\docType{data}
+\alias{SchmidliEtAl2017}
+\title{Historical variance example data}
+\description{Estimates of endpoint variances from six studies.}
+\usage{data("SchmidliEtAl2017")}
+\format{The data frame contains the following columns:
+ \tabular{lll}{
+ \bold{study} \tab \code{character} \tab study label \cr
+ \bold{N} \tab \code{numeric} \tab total sample size \cr
+ \bold{stdev} \tab \code{numeric} \tab standard deviation estimate \cr
+ \bold{df} \tab \code{numeric} \tab associated degrees of freedom
+ }
+}
+\details{Schmidli \emph{et al.} (2017) investigated the use of
+ information on an endpoint's variance from previous (\dQuote{historical})
+ studies for the design and analysis of a new clinical trial. As an
+ example application, the problem of designing a trial in \emph{wet
+ age-related macular degeneration (AMD)} was considered. Trial
+ design, and in particular considerations regarding the required sample
+ size, hinge on the expected amount of variability in the primary
+ endpoint (here: \emph{visual acuity}).
+
+ Historical data from six previous trials are available (Szabo \emph{et
+ al.}; 2015), each trial providing an estimate \eqn{\hat{s}_i}{s[i]} of
+ the endpoint's standard deviation along with the associated number of
+ degrees of freedom \eqn{\nu_i}{nu[i]}. The standard deviations
+ may then be modelled on the logarithmic scale, where the estimates and
+ their associated standard errors are given by
+ \deqn{y_i=\log(\hat{s}_i) \quad \mbox{and} \quad
+ \sigma_i=\sqrt{\frac{1}{2\,\nu_i}}}{y[i] = log(s[i]) and sigma[i] =
+ sqrt(1/(2*nu[i]))}
+
+ The \emph{unit information standard deviation} for a logarithmic
+ standard deviation then is at approximately
+ \eqn{\frac{1}{\sqrt{2}}}{2^-0.5}.
+}
+\source{
+ H. Schmidli, B. Neuenschwander, T. Friede.
+ Meta-analytic-predictive use of historical variance data
+ for the design and analysis of clinical trials.
+ \emph{Computational Statistics and Data Analysis}, \bold{113}:100-110, 2017.
+ \doi{10.1016/j.csda.2016.08.007}.
+}
+\seealso{
+ \code{\link{uisd}}, \code{\link{ess}}.
+}
+\references{
+ S.M. Szabo, M. Hedegaard, K. Chan, K. Thorlund, R. Christensen,
+ H. Vorum, J.P. Jansen.
+ Ranibizumab vs. aflibercept for wet age-related macular degeneration:
+ network meta-analysis to understand the value of reduced frequency dosing.
+ \emph{Current Medical Research and Opinion}, \bold{31}(11):2031-2042, 2015.
+ \doi{10.1185/03007995.2015.1084909}.
+}
+\examples{
+# load data:
+data("SchmidliEtAl2017")
+
+# show data:
+SchmidliEtAl2017
+
+\dontrun{
+# derive log-SDs and their standard errors:
+dat <- cbind(SchmidliEtAl2017,
+ logstdev = log(SchmidliEtAl2017$stdev),
+ logstdev.se = sqrt(0.5/SchmidliEtAl2017$df))
+dat
+
+# alternatively, use "metafor::escalc()" function:
+es <- escalc(measure="SDLN",
+ yi=log(stdev), vi=0.5/df, ni=N,
+ slab=study, data=SchmidliEtAl2017)
+es
+
+# perform meta-analysis of log-stdevs:
+bm <- bayesmeta(y=dat$logstdev,
+ sigma=dat$logstdev.se,
+ label=dat$study,
+ tau.prior=function(t){dhalfnormal(t, scale=sqrt(2)/4)})
+
+# or, alternatively:
+bm <- bayesmeta(es,
+ tau.prior=function(t){dhalfnormal(t, scale=sqrt(2)/4)})
+
+# draw forest plot (see Fig.1):
+forestplot(bm, zero=NA,
+ xlab="log standard deviation")
+
+# show heterogeneity posterior:
+plot(bm, which=4, prior=TRUE)
+
+# posterior of log-stdevs, heterogeneity,
+# and predictive distribution:
+bm$summary
+
+# prediction (standard deviations):
+exp(bm$summary[c(2,5,6),"theta"])
+
+# prediction (variances):
+exp(2 * bm$summary[c(2,5,6),"theta"])
+
+# sample size formula (12) (per arm):
+alpha <- 0.025
+beta <- 0.20
+delta <- 8
+10.9^2 * 2*(qnorm(alpha) + qnorm(beta))^2 / delta^2
+
+# check UISD:
+uisd(es, indiv=TRUE)
+uisd(es)
+1 / sqrt(2)
+
+# compute predictive distribution's ESS:
+ess(bm, uisd=1/sqrt(2))
+# actual total sample size:
+sum(dat$N)
+
+# illustrate predictive distribution
+# on standard-deviation-scale (Fig.2):
+x <- seq(from=5, to=20, length=200)
+plot(x, (1/x) * bm$dposterior(theta=log(x), predict=TRUE), type="l",
+ xlab=expression("predicted standard deviation "*sigma[k+1]),
+ ylab="predictive density")
+abline(h=0, col="grey")
+}
+}
+\keyword{datasets}
diff --git a/man/bayesmeta-package.Rd b/man/bayesmeta-package.Rd
index d9f9fbc..7fc175d 100644
--- a/man/bayesmeta-package.Rd
+++ b/man/bayesmeta-package.Rd
@@ -17,7 +17,7 @@
Package: \tab bayesmeta\cr
Type: \tab Package\cr
Version: \tab 3.1\cr
- Date: \tab 2022-03-14\cr
+ Date: \tab 2022-03-16\cr
License: \tab GPL (>=2)
}
The main functionality is provided by the \code{\link{bayesmeta}()}
--
GitLab