\title{studies on om-89 (uro-vaxom) in urinary tract infections}
\description{estimated wilcoxon-mann-whitney (wmw) effect sizes from five studies on om-89 (uro-vaxom) in patients with recurrent urinary tract infections.}
\format{the data frame contains the following columns:
\bold{study}     \tab \code{character} \tab study identifier \cr
\bold{threshold} \tab \code{numeric}   \tab threshold (for bacteriuria / uti definition) \cr
\bold{n.treat}   \tab \code{numeric}   \tab number of patients in treatment group \cr
\bold{n.control} \tab \code{numeric}   \tab number of patients in control group \cr
\bold{n.total}   \tab \code{numeric}   \tab total number of patients \cr
\bold{wmwstat}   \tab \code{numeric}   \tab wilcoxon-mann-whitney statistic \cr
\bold{lower}     \tab \code{numeric}   \tab lower ci bound for the wmw-statistic \cr
\bold{upper}     \tab \code{numeric}   \tab upper ci bound for the wmw-statistic \cr
\bold{stderr}    \tab \code{numeric}   \tab standard error for the wmw-statistic
bauer et al. (2002) analyzed data from five randomized, placebo-controlled studies investigating the use of om-89 (uro-vaxom) in recurrent urinary tract infections (uti). treatment effects were quantified in terms of the \emph{wilcoxon-mann-whitney statistic} computed based on the numbers of recurrences experienced by included patients from each study.
the wmw-statistic gives an estimate of the probability that a randomly selected patient from the treatment group is superior to a randomly selected control patient, also denoted as the \emph{common language effect size} (cles). \dquote{superiority} in this case relates to fewer uti recurrences experienced. a wmw-statistic \eqn{>0.5} then suggests a beneficial treatment effect.
bauer, h. w., rahlfs, v. w., lauener, p. a., & \enc{bleßmann}{blessmann}, g. s. s. (2002). prevention of recurrent urinary tract infections with immuno-active e. coli fractions: a meta-analysis of five placebo-controlled double-blind studies. \emph{international journal of antimicrobial agents}, \bold{19}(6), 451--456. \verb{https://doi.org/10.1016/s0924-8579(02)00106-1}
# analyze based on plain probabilities as effect sizes
# perform a random-effects meta-analysis
ma01 <- rma.uni(wmw.prob, method="pm", test="adhoc")
# analyze based on log-odds (logits) as effect sizes
ma02 <- rma.uni(wmw.logit, method="pm", test="adhoc")
# show forest plot (back-transformed to probabilities)
header=c("study", "cles [95 percent ci]"),
# show forest plot (in terms of log-odds)
header=c("study", "logit(cles) [95 percent ci]"),
# (using a half-normal prior for the (logit-wmw) effect heterogeneity
#  and a normal(0.0, 1.814) prior for the overall mean logit;
#  the overall mean prior (with variance (pi/sqrt(3))^2)
#  roughly corresponds to a uniform prior in probability)
forestplot(ma03, title = "dat.bauer2002 (log-odds)",
txt_gp = fptxtgp(ticks = gpar(cex=1), xlab = gpar(cex=1)))
medicine, common-language effect sizes, proportions