\title{trials on serum cholesterin concentration lowering and risk of ischaemic heart disease}
\description{results from 28 trials evaluating effect of serum cholesterin concentration lowering on risk of ischaemic heart disease}
\bold{studyid}    \tab \code{integer} \tab study id \cr
\bold{ihd.cont}   \tab \code{integer} \tab number of ischaemic heart disease (control group) \cr
\bold{noihd.cont} \tab \code{integer} \tab number of non-events (control group) \cr
\bold{ihd.exp}    \tab \code{integer} \tab number of ischaemic heart disease (treated group) \cr
\bold{noihd.exp}  \tab \code{integer} \tab number of non-events (treated group) \cr
\bold{or}         \tab \code{numeric} \tab odds ratio \cr
\bold{logor}      \tab \code{numeric} \tab log odds ratio \cr
\bold{varlogor}   \tab \code{numeric} \tab variance of log odds ratio \cr
\bold{cholr}      \tab \code{numeric} \tab cholesterol reduction (mmol/l)
thompson and sharp (1999) compare several meta-regression approaches to explain heterogeneity in meta-analysis. the data set used is originally from law et al. (1994), but with minor modifications.
thompson, s. g., & sharp, s. j. (1999). explaining heterogeneity in meta-analysis: a comparison of methods. \emph{statistics in medicine}, \bold{18}(20), 2693--2708. \verb{https://doi.org/10.1002/(sici)1097-0258(19991030)18:20<2693::aid-sim235>3.0.co;2-v}
law, m. r., wald, n. j., & thompson, s. g. (1994). by how much and how quickly does reduction in serum cholesterol concentration lower risk of ischaemic heart disease? \emph{british medical journal}, \bold{308}(6925), 367--372. \verb{https://doi.org/10.1136/bmj.308.6925.367}
m <- metabin(ihd.exp, ihd.exp + noihd.exp, ihd.cont, ihd.cont + noihd.cont,
data = dat.thompson1999, sm = "or", method = "inverse")
medicine, cardiology, odds ratios, meta-regression

\description{results from 48 trials examining the effect of rosiglitazone on the risk of myocardial infarction and death from cardiovascular causes.}
\bold{study}                 \tab \code{character} \tab study id \cr
\bold{group}                 \tab \code{character} \tab treatment group (rosiglitazone or control) \cr
\bold{intervention}          \tab \code{character} \tab intervention group, i.e., combination of drug classes \cr
\bold{detailed.intervention} \tab \code{character} \tab intervention group with drug names instead of classes \cr
\bold{phase}                 \tab \code{integer}   \tab phase of clinical trial \cr
\bold{duration}              \tab \code{integer}   \tab duration of the trial (in weeks) \cr
\bold{n.all}                 \tab \code{integer}   \tab number of patients \cr
\bold{n}                     \tab \code{integer}   \tab number of patients with information on outcomes \cr
\bold{deaths}                \tab \code{integer}   \tab number of deaths \cr
\bold{infarcts}              \tab \code{integer}   \tab number of myocardial infarctions \cr
\bold{type}                  \tab \code{character} \tab study types as in table 1 from nissen and wolski (2007) \cr
\bold{population}            \tab \code{character} \tab details on the trial population \cr
\bold{period}                \tab \code{character} \tab trial period \cr
\bold{age}                   \tab \code{numeric}   \tab mean age (in years) \cr
\bold{male}                  \tab \code{numeric}   \tab percentage of males \cr
\bold{hemoglobin}            \tab \code{numeric}   \tab mean baseline glycated hemoglobin level \cr
nissen and wolski (2007) performed a meta-analysis of trials examining the effect of rosiglitazone on the risk of myocardial infarction and death from cardiovascular causes. the present dataset by tian et al. (2009) is based on this meta-analysis, but includes six additional trials where no event was observed in either group for both outcomes. the data set is in long arm-based format. see \code{\link{dat.nissen2007}} for the original dataset.
### show first 6 rows / 3 trials of the dataset
### print odds ratios and confidence limits with two digits and define
label.e = "rosiglitazone", label.c = "control",
pw <- pairwise(treat = group, event = deaths, n = n, studlab = study,
data = dat.tian2009, sm = "or", varnames = c("lnor", "selnor"))
mod_mh <- update(mod_iv, method = "mh", random = false,
mod_peto <- update(mod_mh, method = "peto",
### fit generalized linear mixed models (glmm)
mod_glmm <- update(mod_mh, method = "glmm", model = "cm.al",
if (requirenamespace("brglm2", quietly = true)) {
### fit the logistic regression model with penalized likelihood (lrp)
mod_plma <- update(mod_glmm, method = "lrp",
### fit the logistic regression model after excluding double zero studies
mod_plma1 <- metabin(event1, n1, event2, n2, studlab = study,
data = subset(pw, !is.na(lnor)), sm = "or", method = "lrp",
text.common = "lrp method, exclude zeros (common)",
text.random = "lrp method, exclude zeros (random)")
fname <- tempfile(pattern = "forest", fileext = ".pdf")
forest(mm, hetstat = false, file = fname, width = 10, rows.gr = 1)