added Baeten et al. dataset

DESCRIPTION

@@ -2,7 +2,7 @@ Package: bayesmeta
 Type: Package
 Title: Bayesian Random-Effects Meta-Analysis
 Version: 2.6
-Date: 2020-07-17
+Date: 2020-11-19
 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")),

data/BaetenEtAl2013.R

+#
+#   B. Neuenschwander, S. Weber, H. Schmidli, A. O'Hagan.
+#   Predictively consistent prior effective sample sizes.
+#   Biometrics 76(2):578-587, 2020.
+#   https://doi.org/10.1111/biom.13252
+#
+#   D. Baeten et al.
+#   Anti-interleukin-17A monoclonal antibody secukinumab
+#   in treatment of ankylosing spondylitis: a randomised,
+#   double-blind, placebo-controlled trial.
+#   The Lancet 382(9906):1705-1713, 2013.
+#   https://doi.org/10.1016/S0140-6736(13)61134-4
+#
+
+BaetenEtAl2013 <- cbind.data.frame("study"  = c("ATLAS", "Canadian AS", "Wyeth", "Calin",
+                                                "Davis", "Gorman", "ASSERT", "Braun"),
+                                   "year"   = c(2005, 2005, 2006, 2003,
+                                                2003, 2002, 2005, 2002),
+                                   "events" = c( 23, 12, 19,  9,   39,  6,  9, 10),
+                                   "total"  = c(107, 44, 51, 39,  139, 20, 78, 35),
+                                   stringsAsFactors=FALSE)
+
+rownames(BaetenEtAl2013) <- paste0("study_",1:8)

man/BaetenEtAl2013.Rd

+\name{BaetenEtAl2013}
+\docType{data}
+\alias{BaetenEtAl2013}
+\title{Ankylosing spondylitis example data}
+\description{Numbers of cases (patients) and events (responders)
+  in the placebo control groups of eight studies.}
+\usage{data("BaetenEtAl2013")}
+\format{The data frame contains the following columns:
+  \tabular{lll}{
+    \bold{study}  \tab \code{character} \tab study label \cr
+    \bold{year}   \tab \code{numeric}   \tab publication year \cr
+    \bold{events} \tab \code{numeric}   \tab number of responders \cr
+    \bold{total}  \tab \code{numeric}   \tab total number of patients
+  }
+}
+\details{A study was conducted in order to investigate a novel treatment
+  in ankylosing spondylitis (Baeten et al., 2013). The primary endpoint
+  related to \emph{treatment response}. 
+  In order to formulate an informative prior distribution for the
+  response rate to be expected in the control group of the new study, a
+  systematic review of previous studies was consulted (McLeod et al.,
+  2007), and, after a meta-analysis of the estimated response
+  probabilities, the predictive distribution for the new study's
+  response probability was derived. The predictive distribution here
+  constitutes the \emph{meta-analytic-predictive (MAP) prior}
+  distribution (Schmidli et al., 2014). The data set contains the
+  relevant data from the eight \dQuote{historical} studies' placebo
+  groups.
+
+  Note that the original analysis (Baeten et al., 2013) involved a
+  binomial model, and the resulting posterior predictive distribution
+  was eventually approximated by a mixture of beta distributions.
+}
+\source{
+  D. Baeten et al.
+  \href{https://doi.org/10.1016/S0140-6736(13)61134-4}{Anti-interleukin-17A
+  monoclonal antibody secukinumab in treatment of ankylosing
+  spondylitis: a randomised, double-blind, placebo-controlled trial}.
+  \emph{The Lancet}, \bold{382}(9906):1705-1713, 2013.
+}
+\seealso{
+  \code{\link{uisd}}.
+}
+\references{
+  C. McLeod et al.
+  \href{https://doi.org/10.3310/hta11280}{Anti-interleukin-17A
+    monoclonal antibody secukinumab in treatment of ankylosing
+    spondylitis: a randomised, double-blind, placebo-controlled trial}.
+  \emph{Health Technology Assessment}, \bold{11}(28), 2007.
+
+  H. Schmidli, S. Gsteiger, S. Roychoudhury, A. O'Hagan,
+  D. Spiegelhalter, B. Neuenschwander.
+  \href{https://doi.org/10.1111/biom.12242}{Robust
+  meta-analytic-predictive priors in clinical trials with historical
+  control information}.
+  \emph{Biometrics}, \bold{70}(4):1023-1032, 2014.
+}
+\examples{
+data("BaetenEtAl2013")
+\dontrun{
+# compute effect sizes (logarithmic odds) from the count data:
+as <- escalc(xi=events, ni=total, slab=study,
+             measure="PLO", data=BaetenEtAl2013)
+
+# compute the unit information standard deviation (UISD):
+uisd(as)
+
+# perform meta-analysis
+# (using uniform priors for effect and heterogeneity):
+bm <- bayesmeta(as)
+
+# show results (log-odds):
+forestplot(bm, xlab="log-odds", zero=NA)
+# show results (odds):
+forestplot(bm, exponentiate=TRUE, xlab="odds", zero=NA)
+
+# show posterior predictive distribution --
+# in terms of log-odds:
+bm$summary[,"theta"]
+# in terms of odds:
+logodds <- bm.odds$summary[c(2,5,6), "theta"]
+exp(logodds)
+# in terms of probabilities:
+(exp(logodds) / (exp(logodds) + 1))
+
+# illustrate MAP prior density:
+x <- seq(-3, 1, by=0.01)
+plot(x, bm$dposterior(theta=x, predict=TRUE), type="l",
+     xlab="log-odds (response)", ylab="posterior predictive density")
+abline(h=0, v=0, col="grey")
+}
+}
+\keyword{datasets}

man/bayesmeta-package.Rd

@@ -17,7 +17,7 @@
     Package: \tab bayesmeta\cr
     Type:    \tab Package\cr
     Version: \tab 2.6\cr
-    Date:    \tab 2020-07-17\cr
+    Date:    \tab 2020-11-19\cr
     License: \tab GPL (>=2)
   }
   The main functionality is provided by the \code{\link{bayesmeta}()}